LLM Zoomcamp 2024

Homework 2: Open-Source LLMs Statistics

Distribution of scores and reported study time for this homework.

Submissions

419

Median total score

6

Average total score

7

Score distribution

All values are points.

Questions score

Min
-
Median
6.0
Max
6
Q1
6.0
Avg
5.9
Q3
6.0

Learning in public score

Min
-
Median
0.0
Max
7
Q1
0.0
Avg
0.6
Q3
0.0

Total score

Min
-
Median
6.0
Max
14
Q1
6.0
Avg
6.6
Q3
6.0

Time distribution

All values are hours reported by students.

Lectures

Min
0.0
Median
3.0
Max
32.0
Q1
2.0
Avg
4.5
Q3
5.0

Homework

Min
0.0
Median
2.0
Max
30.0
Q1
2.0
Avg
3.2
Q3
4.0

Question breakdown

Correctness and answer distribution per question.

1. Running Ollama with Docker. What's the version?

417 / 419 correct (99.5%)

Answer Count
0.1.48 282
ollama version is 0.1.48 38
0.2.1 25
0.1.47 20
0.2.0 11
0.1.45 8
ollama version is 0.2.1 5
0.1.46 5
0.1.44 4
version 0.1.48 3
ollama version is 0.1.44 3
ollama version is 0.1.45 2
ollama version is 0.1.47 2
Copy code docker exec -it ollama ollama run llama2 1
22.04 1
0.1.43 1
Docker version 26.1.3-1 1
ollama version is 0.1.46 1
0.1.28 1
client version is 0.2.1 1
3.8 1
Docker version 26.1.3-1, build b72abbb6f0351eb22e5c7bdbba9112fef6b41429 1

2. Downloading an LLM. Manifest file

417 / 419 correct (99.5%)

Answer Count
{"schemaVersion":2,"mediaType":"application/vnd.docker.distribution.manifest.v2+json","config":{"mediaType":"application/vnd.docker.container.image.v1+json","digest":"sha256:887433b89a901c156f7e6944442f3c9e57f3c55d6ed52042cbb7303aea994290","size":483},"layers":[{"mediaType":"application/vnd.ollama.image.model","digest":"sha256:c1864a5eb19305c40519da12cc543519e48a0697ecd30e15d5ac228644957d12","size":1678447520},{"mediaType":"application/vnd.ollama.image.license","digest":"sha256:097a36493f718248845233af1d3fefe7a303f864fae13bc31a3a9704229378ca","size":8433},{"mediaType":"application/vnd.ollama.image.template","digest":"sha256:109037bec39c0becc8221222ae23557559bc594290945a2c4221ab4f303b8871","size":136},{"mediaType":"application/vnd.ollama.image.params","digest":"sha256:22a838ceb7fb22755a3b0ae9b4eadde629d19be1f651f73efb8c6b4e2cd0eea0","size":84}]} 285
2b 17
{ "schemaVersion": 2, "mediaType": "application/vnd.docker.distribution.manifest.v2+json", "config": { "mediaType": "application/vnd.docker.container.image.v1+json", "digest": "sha256:887433b89a901c156f7e6944442f3c9e57f3c55d6ed52042cbb7303aea994290", "size": 483 }, "layers": [ { "mediaType": "application/vnd.ollama.image.model", "digest": "sha256:c1864a5eb19305c40519da12cc543519e48a0697ecd30e15d5ac228644957d12", "size": 1678447520 }, { "mediaType": "application/vnd.ollama.image.license", "digest": "sha256:097a36493f718248845233af1d3fefe7a303f864fae13bc31a3a9704229378ca", "size": 8433 }, { "mediaType": "application/vnd.ollama.image.template", "digest": "sha256:109037bec39c0becc8221222ae23557559bc594290945a2c4221ab4f303b8871", "size": 136 }, { "mediaType": "application/vnd.ollama.image.params", "digest": "sha256:22a838ceb7fb22755a3b0ae9b4eadde629d19be1f651f73efb8c6b4e2cd0eea0", "size": 84 } ] } 8
{ "schemaVersion": 2, "mediaType": "application/vnd.docker.distribution.manifest.v2+json", "config": { "mediaType": "application/vnd.docker.container.image.v1+json", "digest": "sha256:887433b89a901c156f7e6944442f3c9e57f3c55d6ed52042cbb7303aea994290", "size": 483 }, "layers": [ { "mediaType": "application/vnd.ollama.image.model", "digest": "sha256:c1864a5eb19305c40519da12cc543519e48a0697ecd30e15d5ac228644957d12", "size": 1678447520 }, { "mediaType": "application/vnd.ollama.image.license", "digest": "sha256:097a36493f718248845233af1d3fefe7a303f864fae13bc31a3a9704229378ca", "size": 8433 }, { "mediaType": "application/vnd.ollama.image.template", "digest": "sha256:109037bec39c0becc8221222ae23557559bc594290945a2c4221ab4f303b8871", "size": 136 }, { "mediaType": "application/vnd.ollama.image.params", "digest": "sha256:22a838ceb7fb22755a3b0ae9b4eadde629d19be1f651f73efb8c6b4e2cd0eea0", "size": 84 } ] } 4
gemma 3
{"schemaVersion":2,"mediaType":"application/vnd.docker.distribution.manifest.v2+json","config":{"mediaType":"application/vnd.docker.container.image.v1+json","digest":"sha256:887433b89a901c156f7e6944442f3c9e57f3c55d6ed52042cbb7303aea994290","size":483},"layers":[{"mediaType":"application/vnd.ollama.image.model","digest":"sha256:c1864a5eb19305c40519da12cc543519e48a0697ecd30e15d5ac228644957d12","size":1678447520},{"mediaType":"application/vnd.ollama.image.license","digest":"sha256:097a36493f718248845233af1d3fefe7a303f864fae13bc31a3a9704229378ca","size":8433},{"mediaType":"application/vnd.ollama.image.template","digest":"sha256:109037bec39c0becc8221222ae23557559bc594290945a2c4221ab4f303b8871","size":136},{"mediaType":"application/vnd.ollama.image.params","digest":"sha256:22a838ceb7fb22755a3b0ae9b4eadde629d19be1f651f73efb8c6b4e2cd0eea0","size":84}] 2
"schemaVersion":2,"mediaType":"application/vnd.docker.distribution.manifest.v2+json","config":{"mediaType":"application/vnd.docker.container.image.v1+json","digest":"sha256:887433b89a901c156f7e6944442f3c9e57f3c55d6ed52042cbb7303aea994290","size":483},"layers":[{"mediaType":"application/vnd.ollama.image.model","digest":"sha256:c1864a5eb19305c40519da12cc543519e48a0697ecd30e15d5ac228644957d12","size":1678447520},{"mediaType":"application/vnd.ollama.image.license","digest":"sha256:097a36493f718248845233af1d3fefe7a303f864fae13bc31a3a9704229378ca","size":8433},{"mediaType":"application/vnd.ollama.image.template","digest":"sha256:109037bec39c0becc8221222ae23557559bc594290945a2c4221ab4f303b8871","size":136},{"mediaType":"application/vnd.ollama.image.params","digest":"sha256:22a838ceb7fb22755a3b0ae9b4eadde629d19be1f651f73efb8c6b4e2cd0eea0","size":84}]} 2
- 2
gemma/2b 2
llmmanifest 1
Json object with metadata and security/verification hashes 1
"2b" json file 1
These describe the composition and size of the different layers of gemma. 1
gemma, phi3 1
Yes 1
1.688 GB (aproximadamente) 1
Manifest json file, model, license, template, params 1
{"schemaVersion":2, "mediaType":"application/vnd.docker.distribution.manifest.v2+json", "config": { "mediaType":"application/vnd.docker.container.image.v1+json","digest":"sha256:887433b89a901c156f7e6944442f3c9e57f3c55d6ed52042cbb7303aea994290","size":483}, "layers":[{"mediaType":"application/vnd.ollama.image.model","digest":"sha256:c1864a5eb19305c40519da12cc543519e48a0697ecd30e15d5ac228644957d12","size":1678447520},{"mediaType":"application/vnd.ollama.image.license","digest":"sha256:097a36493f718248845233af1d3fefe7a303f864fae13bc31a3a9704229378ca","size":8433},{"mediaType":"application/vnd.ollama.image.template","digest":"sha256:109037bec39c0becc8221222ae23557559bc594290945a2c4221ab4f303b8871","size":136},{"mediaType":"application/vnd.ollama.image.params","digest":"sha256:22a838ceb7fb22755a3b0ae9b4eadde629d19be1f651f73efb8c6b4e2cd0eea0","size":84}]} 1
{"schemaVersion":2, "mediaType":"application/vnd.docker.distribution.manifest.v2+json", "config":{"mediaType":"application/vnd.docker.container.image.v1+json", "digest":"sha256:887433b89a901c156f7e6944442f3c9e57f3c55d6ed52042cbb7303aea994290", "size":483}, "layers":[{"mediaType":"application/vnd.ollama.image.model", "digest":"sha256:c1864a5eb19305c40519da12cc543519e48a0697ecd30e15d5ac228644957d12","size":1678447520}, {"mediaType":"application/vnd.ollama.image.license", "digest":"sha256:097a36493f718248845233af1d3fefe7a303f864fae13bc31a3a9704229378ca","size":8433}, {"mediaType":"application/vnd.ollama.image.template", "digest":"sha256:109037bec39c0becc8221222ae23557559bc594290945a2c4221ab4f303b8871","size":136}, {"mediaType":"application/vnd.ollama.image.params", "digest":"sha256:22a838ceb7fb22755a3b0ae9b4eadde629d19be1f651f73efb8c6b4e2cd0eea0","size":84}]} 1
{"schemaVersion":2,"mediaType":"application/vnd.docker.distribution.manifest.v2+json","config":{"mediaType":"a pplication/vnd.docker.container.image.v1+json","digest":"sha256:887433b89a901c156f7e6944442f3c9e57f3c55d6ed520 42cbb7303aea994290","size":483},"layers":[{"mediaType":"application/vnd.ollama.image.model","digest":"sha256:c 1864a5eb19305c40519da12cc543519e48a0697ecd30e15d5ac228644957d12","size":1678447520},{"mediaType":"application/ vnd.ollama.image.license","digest":"sha256:097a36493f718248845233af1d3fefe7a303f864fae13bc31a3a9704229378ca"," size":8433},{"mediaType":"application/vnd.ollama.image.template","digest":"sha256:109037bec39c0becc8221222ae23 557559bc594290945a2c4221ab4f303b8871","size":136},{"mediaType":"application/vnd.ollama.image.params","digest": "sha256:22a838ceb7fb22755a3b0ae9b4eadde629d19be1f651f73efb8c6b4e2cd0eea0","size":84}]} 1
{"schemaVersion":2,"mediaType":"application/vnd.docker.distribution.manifest.v2+json","config":{"mediaType":"application/vnd.docker.container.image.v1+json","digest":"sha256:887433b89a901c156f7e6944442f3c9e57f3c55d6ed52042cbb7303aea994290","size":483},"layers":[{"mediaType":"application/vnd.ollama.image.model","digest":"sha256:c1864a5eb19305c40519da12cc543519e48a0697ecd30e15d5ac228644957d12","size":1678447520},{"mediaType":"application/vnd.ollama.image.license","digest":"sha256:097a36493f718248845233af1d3fefe7a303f864fae13bc31a3a9704229378ca","size":8433},{"mediaType":"application/vnd.ollama.image.template","digest":"sha256:109037bec39c0becc8221222ae23557559bc594290945a2c4221ab4f303b8871","size":136},{"mediaType":"application/vnd.ollama.image.params","digest":"sha256:22a838ceb7fb22755a3b0ae9b4eadde629d19be1f651f73efb8c6b4e2cd0eea0","size":84}]}root@57e6dc62c677:~/.ollama/models/manifests/registry.ollama.ai/librar y/ 1
{schemaVersion:2,mediaType:application/vnd.docker.distribution.manifest.v2+json,config:{mediaType:application/vnd.docker.container.image.v1+json,digest:sha256:887433b89a901c156f7e6944442f3c9e57f3c55d6ed52042cbb7303aea994290,size:483},layers:[{mediaType:application/vnd.ollama.image.model,digest:sha256:c1864a5eb19305c40519da12cc543519e48a0697ecd30e15d5ac228644957d12,size:1678447520},{mediaType:application/vnd.ollama.image.license,digest:sha256:097a36493f718248845233af1d3fefe7a303f864fae13bc31a3a9704229378ca,size:8433},{mediaType:application/vnd.ollama.image.template,digest:sha256:109037bec39c0becc8221222ae23557559bc594290945a2c4221ab4f303b8871,size:136},{mediaType:application/vnd.ollama.image.params,digest:sha256:22a838ceb7fb22755a3b0ae9b4eadde629d19be1f651f73efb8c6b4e2cd0eea0,size:84}]}root@5f729aee8bd0:~/.ollama/models/manifests/registry.ollama.ai/library/gemma# 1
{"schemaVersion":2,"mediaType":"application/vnd.docker.distribution.manifest.v2+json","config":{"mediaType":"application/vnd.docker.container.image.v1+json","digest":"sha256:887433b89a901c156f7e6944442f3c9e57f3c55d6ed52042cbb7303aea994290","size":483},"layers":[{"mediaType":"application/vnd.ollama.image.model","digest":"sha256:c1864a5eb19305c40519da12cc543519e48a0697ecd30e15d5ac228644957d12","size":1678447520},{"mediaType":"application/vnd.ollama.image.license","digest":"sha256:097a36493f718248845233af1d3fefe7a303f864fae13bc31a3a9704229378ca","size":8433},{"mediaType":"application/vnd.ollama.image.template","digest":"sha256:109037bec39c0becc8221222ae23557559bc594290945a2c4221ab4f303b8871","size":136},{"mediaType":"application/vnd.ollama.image.params","digest":"sha256:22a838ceb7fb22755a3b0ae9b4eadde629d19be1f651f73efb8c6b4e2cd0eea0","size":84}]}root@d187bb9387ee:~/.ollama/models/manifests/registry.ollama.ai/library/gemma# 1
{"schemaVersion":2,"mediaType":"application/vnd.docker.distribution.manifest.v2+json","config":{"mediaType":"application/vnd.docker.container.image.v1+json","digest":"sha256:887433b89a901c156f7e6944442f3c9e57f3c55d6ed52042cbb7303aea994290","size":483},"layers":[{"mediaType":"application/vnd.ollama.image.model","digest":"sha256:c1864a5eb19305c40519da12cc543519e48a0697ecd30e15d5ac228644957d12","size":1678447520},{"mediaType":"application/vnd.ollama.image.license","digest":"sha256:097a36493f718248845233af1d3fefe7a303f864fae13bc31a3a9704229378ca","size":8433},{"mediaType":"application/vnd.ollama.image.template","digest":"sha256:109037bec39c0becc8221222ae23557559bc594290945a2c4221ab4f303b8871","size":136},{"mediaType":"application/vnd.ollama.image.params","digest":"sha256:22a838ceb7fb22755a3b0ae9b4eadde629d19be1f651f73efb8c6b4e2cd0eea0","size":84}]}% 1
{"schemaVersion":2,"mediaType":"application/vnd.docker.distribution.manifest.v2+json","config":{"mediaType":"application/vnd.docker.container.image.v1+json","digest":"sha256:887433b89a901c156f7e6944442f3c9e57f3 c55d6ed52042cbb7303aea994290","size":483},"layers":[{"mediaType":"application/vnd.ollama.image.model","digest":"sha256:c1864a5eb19305c40519da12cc543519e48a0697ecd30e15d5ac228644957d12","size":1678447520},{"medi aType":"application/vnd.ollama.image.license","digest":"sha256:097a36493f718248845233af1d3fefe7a303f864fae13bc31a3a9704229378ca","size":8433},{"mediaType":"application/vnd.ollama.image.template","digest":"sha25 6:109037bec39c0becc8221222ae23557559bc594290945a2c4221ab4f303b8871","size":136},{"mediaType":"application/vnd.ollama.image.params","digest":"sha256:22a838ceb7fb22755a3b0ae9b4eadde629d19be1f651f73efb8c6b4e2cd0ee a0","size":84}]} 1
{"schemaVersion":2,"mediaType":"application/vnd.docker.distribution.manifest.v2+json","config":{"mediaType":"application/vnd.docker.container.image.v1+json","digest":"sha256:887433b89a901c156f7e6944442f3c9e57f3c55d6ed52042cbb7303aea994290","size":483},"layers":[{"mediaType":"application/vnd.ollama.image.model","digest":"sha256:c1864a5eb19305c40519da12cc543519e48a0697ecd30e15d5ac228644957d12","size":1678447520},{"mediaType":"application/vnd.ollama.image.license","digest":"sha256:097a36493f718248845233af1d3fefe7a303f864fae13bc31a3a9704229378ca","size":8433},{"mediaType":"application/vnd.ollama.image.template","digest":"sha256:109037bec39c0becc8221222ae23557559bc594290945a2c4221ab4f303b8871","size":136},{"mediaType":"application/vnd.ollama.image.params","digest":"sha256:22a838ceb7fb22755a3b0ae9b4eadde629d19be1f651f73efb8c6b4e2cd0eea0","size":84}]}root@567f95824fd5:~/.ollama/models/manifests/registry.ollama.ai/library/gemma 1
{"schemaVersion":2,"mediaType":"application/vnd.docker.distribution.manifest.v2+json","config":{"mediaType":"application/vnd.docker.container.image.v1+json","digest":"sha256:5af01f364d506fe53eaa9712107a9eceb9ecb9c824da301715e6516a1017870b","size":487},"layers":[{"mediaType":"application/vnd.ollama.image.model","digest":"sha256:0da0a4d67fe4ce317f63a6fcefa9152a2c5e035700ea3db79b3893dc2e7da2b0","size":5443143392},{"mediaType":"application/vnd.ollama.image.template","digest":"sha256:109037bec39c0becc8221222ae23557559bc594290945a2c4221ab4f303b8871","size":136},{"mediaType":"application/vnd.ollama.image.license","digest":"sha256:097a36493f718248845233af1d3fefe7a303f864fae13bc31a3a9704229378ca","size":8433},{"mediaType":"application/vnd.ollama.image.params","digest":"sha256:6522ca797f479b55f692288915af9d59911a436602a8c3b161caa6264f99b93a","size":99}]} 1
{"schemaVersion":2,"mediaType":"application/vnd.docker.distribution.manifest.v2+json","config":{"mediaType":"application/vnd.docker.container.image.v1+json ","digest":"sha256:887433b89a901c156f7e6944442f3c9e57f3c55d6ed52042cbb7303aea994290","size":483},"layers":[{"mediaType":"application/vnd.ollama.image.model ","digest":"sha256:c1864a5eb19305c40519da12cc543519e48a0697ecd30e15d5ac228644957d12","size":1678447520},{"mediaType":"application/vnd.ollama.image.license" ,"digest":"sha256:097a36493f718248845233af1d3fefe7a303f864fae13bc31a3a9704229378ca","size":8433},{"mediaType":"application/vnd.ollama.image.template","dige st":"sha256:109037bec39c0becc8221222ae23557559bc594290945a2c4221ab4f303b8871","size":136},{"mediaType":"application/vnd.ollama.image.params","digest":"sha2 56:22a838ceb7fb22755a3b0ae9b4eadde629d19be1f651f73efb8c6b4e2cd0eea0","size":84}]} 1
{"schemaVersion":2, "mediaType":"application/vnd.docker.distribution.manifest.v2+json", "config":{"mediaType":"application/vnd.docker.container.image.v1+json", "digest":"sha256:887433b89a901c156f7e6944442f3c9e57f3c55d6ed52042cbb7303aea994290", "size":483}, "layers": [{"mediaType":"application/vnd.ollama.image.model", "digest":"sha256:c1864a5eb19305c40519da12cc543519e48a0697ecd30e15d5ac228644957d12", "size":1678447520}, {"mediaType":"application/vnd.ollama.image.license", "digest":"sha256:097a36493f718248845233af1d3fefe7a303f864fae13bc31a3a9704229378ca", "size":8433}, {"mediaType":"application/vnd.ollama.image.template", "digest":"sha256:109037bec39c0becc8221222ae23557559bc594290945a2c4221ab4f303b8871", "size":136}, {"mediaType":"application/vnd.ollama.image.params", "digest":"sha256:22a838ceb7fb22755a3b0ae9b4eadde629d19be1f651f73efb8c6b4e2cd0eea0", "size":84}]} 1
{ "schemaVersion": 2, "mediaType": "application/vnd.docker.distribution.manifest.v2+json", "config": { "mediaType": "application/vnd.docker.container.image.v1+json", "digest": "sha256:ed7ab7698fdd3431927fb6425c6f76d9b15a44e94a68acdcaca4d9ee9eba1ba3", "size": 483 }, "layers": [ { "mediaType": "application/vnd.ollama.image.model", "digest": "sha256:3e38718d00bb0007ab7c0cb4a038e7718c07b54f486a7810efd03bb4e894592a", "size": 2176176608 }, { "mediaType": "application/vnd.ollama.image.license", "digest": "sha256:fa8235e5b48faca34e3ca98cf4f694ef08bd216d28b58071a1f85b1d50cb814d", "size": 1084 }, { "mediaType": "application/vnd.ollama.image.template", "digest": "sha256:542b217f179c7825eeb5bca3c77d2b75ed05bafbd3451d9188891a60a85337c6", "size": 148 }, { "mediaType": "application/vnd.ollama.image.params", "digest": "sha256:8dde1baf1db03d318a2ab076ae363318357dff487bdd8c1703a29886611e581f", "size": 78 } ] } 1
{"schemaVersion":2,"mediaType":"application/vnd.docker.distribution.manifest.v2+json","config":{"mediaType":"application/vnd.docker.container.image.v1+json","digest":"sha256:887433b89a901c156f7e6944442f3c9e57f3c55d6ed52042cbb7303aea994290","size":483},"layers":[{"mediaType":"application/vnd.ollama.image.model","digest":"sha256:c1864a5eb19305c40519da12cc543519e48a0697ecd30e15d5ac228644957d12","size":1678447520},{"mediaType":"application/vnd.ollama.image.license","digest":"sha256:097a36493f718248845233af1d3fefe7a303f864fae13bc31a3a9704229378ca","size":8433},{"mediaType":"application/vnd.ollama.image.template","digest":"sha256:109037bec39c0becc8221222ae23557559bc594290945a2c4221ab4f303b8871","size":136},{"mediaType":"application/vnd.ollama.image.params","digest":"sha256:22a838ceb7fb22755a3b0ae9b4eadde629d19be1f651f73efb8c6b4e2cd0eea0","size":84}]}root@b8bcd0994150:~/.ollama/models/manifests/registry.ollama.ai/library# 1
{"schemaVersion":2,"mediaType":"application/vnd.docker.distribution.manifest.v2+json","config":{"mediaType":"application/vnd.docker.container.image.v1+json","digest":"sha256:9d110ded43383f985acf1a8914de601efaa60f53e98e02ef8a11551d001b5156","size":487},"layers":[{"mediaType":"application/vnd.ollama.image.model","digest":"sha256:e84ed7399c82fbf7dbd6cdef3f12d356c3cdb5512e5d8b2a9898080cbcdd72e5","size":5453001504},{"mediaType":"application/vnd.ollama.image.template","digest":"sha256:e8c944daf3fe3235d2969ef266eecbfd3f0d63a203ba838fb71ef96e4ff22579","size":135},{"mediaType":"application/vnd.ollama.image.license","digest":"sha256:097a36493f718248845233af1d3fefe7a303f864fae13bc31a3a9704229378ca","size":8433},{"mediaType":"application/vnd.ollama.image.params","digest":"sha256:22a838ceb7fb22755a3b0ae9b4eadde629d19be1f651f73efb8c6b4e2cd0eea0","size":84}]} 1
{"schemaVersion":2,"mediaType":"application/vnd.docker.distribution.manifest.v2+json","config":{"mediaType":"application/vnd.docker.container.image.v1+json","digest":"sha256:887433b89a901c156f7e6944442f3c9e57f3c55d6ed52042cbb7303aea994290","size":483},"layers":[{"mediaType":"application/vnd.ollama.image.model","digest":"sha256:c1864a5eb19305c40519da12cc543519e48a0697ecd30e15d5ac228644957d12","size":1678447520},{"mediaType":"application/vnd.ollama.image.license","digest":"sha256:097a36493f718248845233af1d3fefe7a303f864fae13bc31a3a9704229378ca","size":8433},{"mediaType":"application/vnd.ollama.image.template","digest":"sha256:109037bec39c0becc8221222ae23557559bc594290945a2c4221ab4f303b8871","size":136},{"mediaType":"application/vnd.ollama.image.params","digest":"sha256:22a838ceb7fb22755a3b0ae9b4eadde629d19be1f651f73efb8c6b4e2cd0eea0","size":84}]}root@cef57fcb6a09:/# 1
root@b1e80992ba8a:~/.ollama/models/manifests/registry.ollama.ai/library/gemma# cat 2b {"schemaVersion":2,"mediaType":"application/vnd.docker.distribution.manifest.v2+json","config":{"mediaType":"application/vnd.docker.container.image.v1+json","digest":"sha256:887433b89a901c156f7e6944442f3c9e57f3c55d6ed52042cbb7303aea994290","size":483},"layers":[{"mediaType":"application/vnd.ollama.image.model","digest":"sha256:c1864a5eb19305c40519da12cc543519e48a0697ecd30e15d5ac228644957d12","size":1678447520},{"mediaType":"application/vnd.ollama.image.license","digest":"sha256:097a36493f718248845233af1d3fefe7a303f864fae13bc31a3a9704229378ca","size":8433},{"mediaType":"application/vnd.ollama.image.template","digest":"sha256:109037bec39c0becc8221222ae23557559bc594290945a2c4221ab4f303b8871","size":136},{"mediaType":"application/vnd.ollama.image.params","digest":"sha256:22a838ceb7fb22755a3b0ae9b4eadde629d19be1f651f73efb8c6b4e2cd0eea0","size":84}]}root@b1e80992ba8a:~/.ollama/models/manifests/registry.ollama.ai/library/gemma# 1
{ "schemaVersion":2, "mediaType":"application/vnd.docker.distribution.manifest.v2+json", "config":{ "mediaType":"application/vnd.docker.container.image.v1+json", "digest":"sha256:887433b89a901c156f7e6944442f3c9e57f3c55d6ed52042cbb7303aea994290", "size":483 }, "layers":[ { "mediaType":"application/vnd.ollama.image.model", "digest":"sha256:c1864a5eb19305c40519da12cc543519e48a0697ecd30e15d5ac228644957d12", "size":1678447520 }, { "mediaType":"application/vnd.ollama.image.license", "digest":"sha256:097a36493f718248845233af1d3fefe7a303f864fae13bc31a3a9704229378ca", "size":8433 }, { "mediaType":"application/vnd.ollama.image.template", "digest":"sha256:109037bec39c0becc8221222ae23557559bc594290945a2c4221ab4f303b8871", "size":136 }, { "mediaType":"application/vnd.ollama.image.params", "digest":"sha256:22a838ceb7fb22755a3b0ae9b4eadde629d19be1f651f73efb8c6b4e2cd0eea0", "size":84 } ] } 1
{"schemaVersion":2,"mediaType":"application/vnd.docker.distribution.manifest.v2+json","config":{"mediaType":"application/vnd.docker.container.image.v1+json","digest":"sha256:887433b89a901c156f7e6944442f3c9e57f3c55d6ed52042cbb7303aea994290","size":483},"layers":[{"mediaType":"application/vnd.ollama.image.model","digest":"sha256:c1864a5eb19305c40519da12cc543519e48a0697ecd30e15d5ac228644957d12","size":1678447520},{"mediaType":"application/vnd.ollama.image.license","digest":"sha256:097a36493f718248845233af1d3fefe7a303f864fae13bc31a3a9704229378ca","size":8433},{"mediaType":"application/vnd.ollama.image.template","digest":"sha256:109037bec39c0becc8221222ae23557559bc594290945a2c4221ab4f303b8871","size":136},{"mediaType":"application/vnd.ollama.image.params","digest":"sha256:22a838ceb7fb22755a3b0ae9b4eadde629d19be1f651f73efb8c6b4e2cd0eea0","size":84}]}root@a76557fa9d06:~/.ollama/models 1
{"schemaVersion":2,"mediaType":"application/vnd.docker.distribution.manifest.v2+json","config":{"mediaType":"application/vnd.docker.container.image.v1+json","digest":"sha256:887433b89a90 1c156f7e6944442f3c9e57f3c55d6ed52042cbb7303aea994290","size":483},"layers":[{"mediaType":"application/vnd.ollama.image.model","digest":"sha256:c1864a5eb19305c40519da12cc543519e48a0697ecd 30e15d5ac228644957d12","size":1678447520},{"mediaType":"application/vnd.ollama.image.license","digest":"sha256:097a36493f718248845233af1d3fefe7a303f864fae13bc31a3a9704229378ca","size":84 33},{"mediaType":"application/vnd.ollama.image.template","digest":"sha256:109037bec39c0becc8221222ae23557559bc594290945a2c4221ab4f303b8871","size":136},{"mediaType":"application/vnd.olla ma.image.params","digest":"sha256:22a838ceb7fb22755a3b0ae9b4eadde629d19be1f651f73efb8c6b4e2cd0eea0","size":84}]} 1
gemma_2b.json 1
{ "name": "gemma", "version": "2b", "description": "A smaller language model for efficient tasks.", "author": "Ollama AI", "license": "Apache-2.0", "size": "2B parameters", "download_url": "https://registry.ollama.ai/library/gemma/2b/download", "dependencies": [ { "name": "numpy", "version": ">=1.18.0" }, { "name": "torch", "version": ">=1.7.0" } ], "creation_date": "2023-05-01" } 1
{"schemaVersion":2,"mediaType":"application/vnd.docker.distribution.manifest.v2+json","config":{"mediaType":"application/vnd.docker.container.image.v1+json","digest":"sha256:887433b89a901c156f7e6944442f3c9e57f3c55d6ed52042cbb7303aea994290","size":483},"layers":[{"mediaType":"application/vnd.ollama.image.model","digest":"sha256:c1864a5eb19305c40519da12cc543519e48a0697ecd30e15d5ac228644957d12","size":1678447520},{"mediaType":"application/vnd.ollama.image.license","digest":"sha256:097a36493f718248845233af1d3fefe7a303f864fae13bc31a3a9704229378ca","size":8433},{"mediaType":"application/vnd.ollama.image.template","digest":"sha256:109037bec39c0becc8221222ae23557559bc594290945a2c4221ab4f303b8871","size":136},{"mediaType":"application/vnd.ollama.image.params","digest":"sha256:22a838ceb7fb22755a3b0ae9b4eadde629d19be1f651f73efb8c6b4e2cd0eea0","size":84}]}root@a8cd7ce46c6f:~/.ollama/models/manifests/registry.ollama.ai/library/gemma# 1
{"schemaVersion":2,"mediaType":"application/vnd.docker.distribution.manifest.v2+json","config":{"mediaType":"application/vnd.docker.container.image.v1+json","digest":"","size":483},"layers":[{"mediaType":"application/vnd.ollama.image.model","digest":"","size":1678447520},{"mediaType":"application/vnd.ollama.image.license","digest":"","size":8433},{"mediaType":"application/vnd.ollama.image.template","digest":"","size":136},{"mediaType":"application/vnd.ollama.image.params","digest":"","size":84}]} 1
Model arch gemma parameters 3B quantization Q4_0 context length 8192 embedding length 2048 Parameters repeat_penalty 1 stop "<start_of_turn>" stop "<end_of_turn>" License Gemma Terms of Use Last modified: February 21, 2024 1
{"schemaVersion":2,"mediaType":"application/vnd.docker.distribution.manifest.v2+json","config":{"mediaType":"application/vnd.docker.container.image.v1+json","digest":"sha256:887433b89a901c156f7e6944442f3c9e57f3c55d6ed52042cbb7303aea994290","size":483},"layers":[{"mediaType":"application/vnd.ollama.image.model","digest":"sha256:c1864a5eb19305c40519da12cc543519e48a0697ecd30e15d5ac228644957d12","size":1678447520},{"mediaType":"application/vnd.ollama.image.license","digest":"sha256:097a36493f718248845233af1d3fefe7a303f864fae13bc31a3a9704229378ca","size":8433},{"mediaType":"application/vnd.ollama.image.template","digest":"sha256:109037bec39c0becc8221222ae23557559bc594290945a2c4221ab4f303b8871","size":136},{"mediaType":"application/vnd.ollama.image.params","digest":"sha256:22a838ceb7fb22755a3b0ae9b4eadde629d19be1f651f73efb8 1
{"schemaVersion":2,"mediaType":"application/vnd.docker.distribution.manifest.v2+json","config":{"mediaType":"application/vnd.docker.container.image.v1+json","digest":"sha256:887433b89a901c156f7e6944442f3c9e57f3c55d6ed52042cbb7303aea994290","size":483},"layers":[{"mediaType":"application/vnd.ollama.image.model","digest":"sha256:c1864a5eb19305c40519da12cc543519e48a0697ecd30e15d5ac228644957d12","size":1678447520},{"mediaType":"application/vnd.ollama.image.license","digest":"sha256:097a36493f718248845233af1d3fefe7a303f864fae13bc31a3a9704229378ca","size":8433},{"mediaType":"application/vnd.ollama.image.template","digest":"sha256:109037bec39c0becc8221222ae23557559bc594290945a2c4221ab4f303b8871","size":136},{"mediaType":"application/vnd.ollama.image.params","digest":"sha256:22a838ceb7fb22755a3b0ae9b4eadde629d19b} 1
pulling 3e38718d00bb... 1
gemma phi3 1
4.0K 1
{"schemaVersion":2,"mediaType":"application/vnd.docker.distribution.manifest.v2+json","config":{"mediaType":"application/vnd.docker.container.image.v1+json","digest":"sha256:887433b89a901c156f7e6944442f3c9e57f3c55d6ed52042cbb7303aea994290","size":483},"layers":[{"mediaType":"application/vnd.ollama.image.model","digest":"sha256:c1864a5eb19305c40519da12cc543519e48a0697ecd30e15d5ac228644957d12","size":1678447520},{"mediaType":"application/vnd.ollama.image.license","digest":"sha256:097a36493f718248845233af1d3fefe7a303f864fae13bc31a3a9704229378ca","size":8433},{"mediaType":"application/vnd.ollama.image.template","digest":"sha256:109037bec39c0becc8221222ae23557559bc594290945a2c4221ab4f303b8871","size":136},{"mediaType":"application/vnd.ollama.image.params","digest":"sha256:22a838ceb7fb22755a3b0ae9b4eadde629d19be1f651f73efb8c6b4e2cd0eea0","size":84}]}root@57df78d63c47:~/.ollama/models/manifests/registry.ollama.ai/library/gemma# 1
{"schemaVersion":2,"mediaType":"application/vnd.docker.distribution.manifest.v2+json", "config":{"mediaType":"application/vnd.docker.container.image.v1+json", "digest":"sha256:887433b89a901c156f7e6944442f3c9e57f3c55d6ed52042cbb7303aea994290","size":483}, "layers":[{"mediaType":"application/vnd.ollama.image.model", "digest":"sha256:c1864a5eb19305c40519da12cc543519e48a0697ecd30e15d5ac228644957d12", "size":1678447520},{"mediaType":"application/vnd.ollama.image.license", "digest":"sha256:097a36493f718248845233af1d3fefe7a303f864fae13bc31a3a9704229378ca","size":8433}, {"mediaType":"application/vnd.ollama.image.template", "digest":"sha256:109037bec39c0becc8221222ae23557559bc594290945a2c4221ab4f303b8871","size":136}, {"mediaType":"application/vnd.ollama.image.params", "digest":"sha256:22a838ceb7fb22755a3b0ae9b4eadde629d19be1f651f73efb8c6b4e2cd0eea0","size":84}]} 1
{"schemaVersion":2,"mediaType":"application/vnd.docker.distribution.manifest.v2+json","config":{"mediaType":"application/vnd.docker.container.image.v1+json"," digest":"sha256:887433b89a901c156f7e6944442f3c9e57f3c55d6ed52042cbb7303aea994290","size":483},"layers":[{"mediaType":"application/vnd.ollama.image.model","dig est":"sha256:c1864a5eb19305c40519da12cc543519e48a0697ecd30e15d5ac228644957d12","size":1678447520},{"mediaType":"application/vnd.ollama.image.license","digest" :"sha256:097a36493f718248845233af1d3fefe7a303f864fae13bc31a3a9704229378ca","size":8433},{"mediaType":"application/vnd.ollama.image.template","digest":"sha256: 109037bec39c0becc8221222ae23557559bc594290945a2c4221ab4f303b8871","size":136},{"mediaType":"application/vnd.ollama.image.params","digest":"sha256:22a838ceb7fb 22755a3b0ae9b4eadde629d19be1f651f73efb8c6b4e2cd0eea0","size":84}] 1
{"schemaVersion":2,"mediaType":"application/vnd.docker.distribution.manifest.v2+json","config":{"mediaType":"application/vnd.docker.container.image.v1+json","digest":"sha256:887433b89a901c156f7e6944442f3c9e57f3c55d6ed52042cbb7303aea994290","size":483},"layers":[{"mediaType":"application/vnd.ollama.image.model","digest":"sha256:c1864a5eb19305c40519da12cc543519e48a0697ecd30e15d5ac228644957d12","size":1678447520},{"mediaType":"application/vnd.ollama.image.license","digest":"sha256:097a36493f718248845233af1d3fefe7a303f864fae13bc31a3a9704229378ca","size":8433},{"mediaType":"application/vnd.ollama.image.template","digest":"sha256:109037bec39c0becc8221222ae23557559bc594290945a2c4221ab4f303b8871","size":136},{"mediaType":"application/vnd.ollama.image.params","digest":"sha256:22a838ceb7fb22755a3b0ae9b4eadde629d19be1f651f73efb8c6b4e2cd0eea0","size":84}]}root@a2c43df67e27:~/.ollama/models/manifests/registry.o 1
{"schemaVersion":2,"mediaType":"application/vnd.docker.distribution.manifest.v2+json","config":{"mediaType":"application/vnd.docker.container.image.v1+json","digest":"sha256:887433b89a901c156f7e6944442f3c9e57f3c55d6ed52042cbb7303aea994290","size":483},"layers":[{"mediaType":"application/vnd.ollama.image.model","digest":"sha256:c1864a5eb19305c40519da12cc543519e48a0697ecd30e15d5ac228644957d12","size":1678447520},{"mediaType":"application/vnd.ollama.image.license","digest":"sha256:097a36493f718248845233af1d3fefe7a303f864fae13bc31a3a9704229378ca","size":8433},{"mediaType":"application/vnd.ollama.image.template","digest":"sha256:109037bec39c0becc8221222ae23557559bc594290945a2c4221ab4f303b8871","size":136},{"mediaType":"application/vnd.ollama.image.params","digest":"sha256:22a838ceb7fb22755a3b0ae9b4eadde629d19be1f651f73efb8c6b4e2cd0eea0","size":84}]}root@67f0b9a5b0f7:~/.ollama/models/manifests/registry.ol 1
{"schemaVersion":2,"mediaType":"application/vnd.docker.distribution.manifest.v2+json","config":{"mediaType":"application/vnd.docker.container.image.v1+json","digest":"sha256:887433b89a901c156f7e6944442f3c9e57f3c55d6ed52042cbb7303aea994290","size":483},"layers":[{"mediaType":"application/vnd.ollama.image.model","digest":"sha256:c1864a5eb19305c40519da12cc543519e48a0697ecd30e15d5ac228644957d12","size":1678447520},{"mediaType":"application/vnd.ollama.image.license","digest":"sha256:097a36493f718248845233af1d3fefe7a303f864fae13bc31a3a9704229378ca","size":8433},{"mediaType":"application/vnd.ollama.image.template","digest":"sha256:109037bec39c0becc8221222ae23557559bc594290945a2c4221ab4f303b8871","size":136},{"mediaType":"application/vnd.ollama.image.params","digest":"sha256:22a838ceb7fb22755a3b0ae9b4eadde629d19be1f651f73efb,"size":483},"layers":[{"mediaType":"application/vnd.ollama.image.model","digest":"sha256:c1864a5eb19305c40519da12cc543519e48a0697ecd30e15d5ac228644957d12","size":1678447520},{"mediaType":"application/vnd.ollama.image.license","digest":"sha256:097a36493f718248845233af1d3fefe7a303f864fae13bc31a3a9704229378ca","size":8433},{"mediaType":"application/vnd.ollama.image.template","digest":"sha256:109037bec39c0becc8221222ae23557559bc594290945a2c4221ab4f303b8871","size":136},{"mediaType":"application/vnd.ollama.image.params","digest":"sha256:22a838ceb7fb22755a3b0ae9b4eadde629d19be1f651f73efb8c6b4e2cd0eea0","size":84}]} 1
{"schemaVersion":2,"mediaType":"application/vnd.docker.distribution.manifest.v2+json","config":{"mediaType":"application/vnd.docker.container.image.v1+json","digest":"sha256:887433b89a901c156f7e6944442f3c9e57f3c55d6ed52042cbb7303aea994290","size":483},"layers":[{"mediaType":"application/vnd.ollama.image.model","digest":"sha256:c1864a5eb19305c40519da12cc543519e48a0697ecd30e15d5ac228644957d12","size":1678447520},{"mediaType":"application/vnd.ollama.image.license","digest":"sha256:097a36493f718248845233af1d3fefe7a303f864fae13bc31a3a9704229378ca","size":8433},{"mediaType":"application/vnd.ollama.image.template","digest":"sha256:109037bec39c0becc8221222ae23557559bc594290945a2c4221ab4f303b8871","size":136},{"mediaType":"application/vnd.ollama.image.params","digest":"sha256:22a838ceb7fb22755a3b0ae9b4eadde629d19be1f651f73efb8c6b4e2cd0eea0","size":84}]}root@ac9af5c3374e:~/.ollama/models/manifests/registry.ollama.ai/library/gemma# 1
A file called 2b, with a manifest with metadata in JSON-like style 1
{"schemaVersion":2,"mediaType":"application/vnd.docker.distribution.manifest.v2+json","config":{"mediaType":"application/vnd.docker.container.image.v1+json","digest":"sha256:887433b89a901c156f7e6944442f3c9e57f3c55d6ed52042cbb7303aea994290","size":483},"layers":[{"mediaType":"application/vnd.ollama.image.model","digest":"sha256:c1864a5eb19305c40519da12cc543519e48a0697ecd30e15d5ac228644957d12","size":1678447520},{"mediaType":"application/vnd.ollama.image.license","digest":"sha256:097a36493f718248845233af1d3fefe7a303f864fae13bc31a3a9704229378ca","size":8433},{"mediaType":"application/vnd.ollama.image.template","digest":"sha256:109037bec39c0becc8221222ae23557559bc594290945a2c4221ab4f303b8871","size":136},{"mediaType":"application/vnd.ollama.image.params","digest":"sha256:22a838ceb7fb22755a3b0ae9b4eadde629d19be1f651f73efb8c6b4e2cd0eea0","size":84}]}root@1bed5d652301:~/.ollama/model 1
gemma folder 1
{"schemaVersion":2,"mediaType":"application/vnd.docker.distribution.manifest.v2+json","config":{"mediaType":"application/vnd.docker.container.image.v1+json","digest":"sha256:887433b89a901c156f7e6944442f3c9e57f3c55d6ed52042cbb7303aea994290","size":483},"layers":[{"mediaType":"application/vnd.ollama.image.model","digest":"sha256:c1864a5eb19305c40519da12cc543519e48a0697ecd30e15d5ac228644957d12","size":1678447520},{"mediaType":"application/vnd.ollama.image.license","digest":"sha256:097a36493f718248845233af1d3fefe7a303f864fae13bc31a3a9704229378ca","size":8433},{"mediaType":"application/vnd.ollama.image.template","digest":"sha256:109037bec39c0becc8221222ae23557559bc594290945a2c4221ab4f303b8871","size":136},{"mediaType":"application/vnd.ollama.image.params","digest":"sha256:22a838ceb7fb22755a3b0ae9b4eadde629d19be1f651f73efb8c6b4e2cd0eea0","size":84}]}root@3017773a0afc:~/.ollama/models/manifests/registry.ollama.ai/library/gemma# 1
{"schemaVersion":2, "mediaType":"application/vnd.docker.distribution.manifest.v2+json", "config":{"mediaType":"application/vnd.docker.container.image.v1+json", "digest":"sha256:887433b89a901c156f7e6944442f3c9e57f3c55d6ed52042cbb7303aea994290", "size":483}, "layers":[{"mediaType":"application/vnd.ollama.image.model", "digest":"sha256:c1864a5eb19305c40519da12cc543519e48a0697ecd30e15d5ac228644957d12", "size":1678447520}, {"mediaType":"application/vnd.ollama.image.license", "digest":"sha256:097a36493f718248845233af1d3fefe7a303f864fae13bc31a3a9704229378ca", "size":8433}, {"mediaType":"application/vnd.ollama.image.template", "digest":"sha256:109037bec39c0becc8221222ae23557559bc594290945a2c4221ab4f303b8871", "size":136}, {"mediaType":"application/vnd.ollama.image.params", "digest":"sha 1
{"schemaVersion":2,"mediaType":"application/vnd.docker.distribution.manifest.v2+json","config":{"mediaType":"application/vnd.docker.container.image.v1+json","digest":"sha256:887433b89a901c156f7e6944442f3c9e57f3c55d6ed52042cbb7303aea994290","size":483},"layers":[{"mediaType":"application/vnd.ollama.image.model","digest":"sha256:c1864a5eb19305c40519da12cc543519e48a0697ecd30e15d5ac228644957d12","size":1678447520},{"mediaType":"application/vnd.ollama.image.license","digest":"sha256:097a36493f718248845233af1d3fefe7a303f864fae13bc31a3a9704229378ca","size":8433},{"mediaType":"application/vnd.ollama.image.template","digest":"sha256:109037bec39c0becc8221222ae23557559bc594290945a2c4221ab4f303b8871","size":136},{"mediaType":"application/vnd.ollama.image.params","digest":"sha256:22a838ceb7fb22755a3b0ae9b4eadde629d19be1f651f73efb8c6b4e2cd0eea0","size":84}]}root@e7755e6ae244:~/.ollama/models/manifests/registry.ollama.ai/library/gemma# 1
root@ce5532af80ad:~/.ollama/models/manifests/registry.ollama.ai/library/gemma# cat 2b {"schemaVersion":2,"mediaType":"application/vnd.docker.distribution.manifest.v2+json","config":{"mediaType":"application/vnd.docker.container.image.v1+json","digest":"sha256:887433b89a901c156f7e6944442f3c9e57f3c55d6ed52042cbb7303aea994290","size":483},"layers":[{"mediaType":"application/vnd.ollama.image.model","digest":"sha256:c1864a5eb19305c40519da12cc543519e48a0697ecd30e15d5ac228644957d12","size":1678447520},{"mediaType":"application/vnd.ollama.image.license","digest":"sha256:097a36493f718248845233af1d3fefe7a303f864fae13bc31a3a9704229378ca","size":8433},{"mediaType":"application/vnd.ollama.image.template","digest":"sha256:109037bec39c0becc8221222ae23557559bc594290945a2c4221ab4f303b8871","size":136},{"mediaType":"application/vnd.ollama.image.params","digest":"sha256:22a838ceb7fb22755a3b0ae9b4eadde629d19be1f651f73efb8c6b4e2cd0eea0","size":84}]}root@ce5532af80ad:~/.ollama/models/manifests/registry.ollama.ai/library/gemma# 1
2024/07/08 12:06:35 routes.go:1064: INFO server config env="map[CUDA_VISIBLE_DEVICES: GPU_DEVICE_ORDINAL: HIP_VISIBLE_DEVICES: HSA_OVERRIDE_GFX_VERSION: OLLAMA_DEBUG:false OLLAMA_FLASH_ATTENTION:false OLLAMA_HOST:http://0.0.0.0:11434 OLLAMA_INTEL_GPU:false OLLAMA_KEEP_ALIVE: OLLAMA_LLM_LIBRARY: OLLAMA_MAX_LOADED_MODELS:1 OLLAMA_MAX_QUEUE:512 OLLAMA_MAX_VRAM:0 OLLAMA_MODELS:/root/.ollama/models OLLAMA_NOHISTORY:false OLLAMA_NOPRUNE:false OLLAMA_NUM_PARALLEL:1 OLLAMA_ORIGINS:[http://localhost https://localhost http://localhost:* https://localhost:* http://127.0.0.1 https://127.0.0.1 http://127.0.0.1:* https://127.0.0.1:* http://0.0.0.0 https://0.0.0.0 http://0.0.0.0:* https://0.0.0.0:* app://* file://* tauri://*] OLLAMA_RUNNERS_DIR: OLLAMA_SCHED_SPREAD:false OLLAMA_TMPDIR: ROCR_VISIBLE_DEVICES:]" time=2024-07-08T12:06:35.823Z level=INFO source=images.go:730 msg="total blobs: 0" time=2024-07-08T12:06:35.823Z level=INFO source=images.go:737 msg="total unused blobs removed: 0" time=2024-07-08T12:06:35.824Z level=INFO source=routes.go:1111 msg="Listening on [::]:11434 (version 0.1.48)" time=2024-07-08T12:06:35.824Z level=INFO source=payload.go:30 msg="extracting embedded files" dir=/tmp/ollama2264159103/runners time=2024-07-08T12:06:39.252Z level=INFO source=payload.go:44 msg="Dynamic LLM libraries [cpu cpu_avx cpu_avx2 cuda_v11 rocm_v60101]" time=2024-07-08T12:06:39.254Z level=WARN source=amd_linux.go:58 msg="ollama recommends running the https://www.amd.com/en/support/linux-drivers" error="amdgpu version file missing: /sys/module/amdgpu/version stat /sys/module/amdgpu/version: no such file or directory" time=2024-07-08T12:06:39.255Z level=WARN source=amd_linux.go:186 msg="amdgpu too old gfx000" gpu=0 time=2024-07-08T12:06:39.255Z level=INFO source=amd_linux.go:345 msg="no compatible amdgpu devices detected" time=2024-07-08T12:06:39.255Z level=INFO source=types.go:98 msg="inference compute" id=0 library=cpu compute="" driver=0.0 name="" total="13.6 GiB" available="10.7 GiB" 1
{"schemaVersion":2,"mediaType":"application/vnd.docker.distribution.manifest.v2+json","config":{"mediaType":"application/vnd.docker.c ontainer.image.v1+json","digest":"sha256:887433b89a901c156f7e6944442f3c9e57f3c55d6ed52042cbb7303aea994290","size":483},"layers":[{"me diaType":"application/vnd.ollama.image.model","digest":"sha256:c1864a5eb19305c40519da12cc543519e48a0697ecd30e15d5ac228644957d12","siz e":1678447520},{"mediaType":"application/vnd.ollama.image.license","digest":"sha256:097a36493f718248845233af1d3fefe7a303f864fae13bc31 a3a9704229378ca","size":8433},{"mediaType":"application/vnd.ollama.image.template","digest":"sha256:109037bec39c0becc8221222ae2355755 9bc594290945a2c4221ab4f303b8871","size":136},{"mediaType":"application/vnd.ollama.image.params","digest":"sha256:22a838ceb7fb22755a3b 0ae9b4eadde629d19be1f651f73efb8c6b4e2cd0eea0","size":84}]} 1
{"schemaVersion":2,"mediaType":"application/vnd.docker.distribution.manifest.v2+json","config":{"mediaType":"application/vnd.docker.container.image.v1+json","digest":"sha256:887433b89a901c156f7e6944442f3c9e57f3c55d6ed52042cbb7303aea994290","size":483},"layers":[{"mediaType":"application/vnd.ollama.image.model","digest":"sha256:c1864a5eb19305c40519da12cc543519e48a0697ecd30e15d5ac228644957d12","size":1678447520},{"mediaType":"application/vnd.ollama.image.license","digest":"sha256:097a36493f718248845233af1d3fefe7a303f864fae13bc31a3a9704229378ca","size":8433},{"mediaType":"application/vnd.ollama.image.template","digest":"sha256:109037bec39c0becc8221222ae23557559bc594290945a2c4221ab4f303b8871","size":136},{"mediaType":"application/vnd.ollama.image.params","digest":"sha256:22a838ceb7fb22755a3b0ae9b4eadde629d19be1f651f73efb8c6b4e2cd0eea0","size":84}]}root@d2559bbd0098:~/.ollama/models/manifests/registry.ollama.ai/library/gemma# 1
{"schemaVersion":2,"mediaType":"application/vnd.docker.distribution.manifest.v2+json","config":{"mediaType":"application/vnd.docker.container.image.v1+json","digest":"sha256:887433b89a901c156f7e6944442f3c9e57f3c55d6ed52042cbb7303aea994290","size":483},"layers":[{"mediaType":"application/vnd.ollama.image.model","digest":"sha256:c1864a5eb19305c40519da12cc543519e48a0697ecd30e15d5ac228644957d12","size":1678447520},{"mediaType":"application/vnd.ollama.image.license","digest":"sha256:097a36493f718248845233af1d3fefe7a303f864fae13bc31a3a9704229378ca","size":8433},{"mediaType":"application/vnd.ollama.image.template","digest":"sha256:109037bec39c0becc8221222ae23557559bc594290945a2c4221ab4f303b8871","size":136},{"mediaType":"application/vnd.ollama.image.params","digest":"sha256:22a838ceb7fb22755a3b0ae9b4eadde629d19be1f651f73efb8c6b4e2cd0eea0","size":84}]}root@2d6fb9dc8867:~/.ollama/models/manifests/registry.ollama.ai/library/gemma# 1
{"schemaVersion":2,"mediaType":"application/vnd.docker.distribution.manifest.v2+json","config":{"mediaType":"application /vnd.docker.container.image.v1+json","digest":"sha256:887433b89a901c156f7e6944442f3c9e57f3c55d6ed52042cbb7303aea994290", "size":483},"layers":[{"mediaType":"application/vnd.ollama.image.model","digest":"sha256:c1864a5eb19305c40519da12cc54351 9e48a0697ecd30e15d5ac228644957d12","size":1678447520},{"mediaType":"application/vnd.ollama.image.license","digest":"sha2 56:097a36493f718248845233af1d3fefe7a303f864fae13bc31a3a9704229378ca","size":8433},{"mediaType":"application/vnd.ollama.i mage.template","digest":"sha256:109037bec39c0becc8221222ae23557559bc594290945a2c4221ab4f303b8871","size":136},{"mediaTyp e":"application/vnd.ollama.image.params","digest":"sha256:22a838ceb7fb22755a3b0ae9b4eadde629d19be1f651f73efb8c6b4e2cd0ee 1
Gemma>2B: {"schemaVersion":2,"mediaType":"application/vnd.docker.distribution.manifest.v2+json","config":{"mediaType":"application/vnd.docker.container.image.v1+json","digest":"sha256:887433b89a901c156f7e6944442f3c9e57f3c55d6ed52042cbb7303aea994290","size":483},"layers":[{"mediaType":"application/vnd.ollama.image.model","digest":"sha256:c1864a5eb19305c40519da12cc543519e48a0697ecd30e15d5ac228644957d12","size":1678447520},{"mediaType":"application/vnd.ollama.image.license","digest":"sha256:097a36493f718248845233af1d3fefe7a303f864fae13bc31a3a9704229378ca","size":8433},{"mediaType":"application/vnd.ollama.image.template","digest":"sha256:109037bec39c0becc8221222ae23557559bc594290945a2c4221ab4f303b8871","size":136},{"mediaType":"application/vnd.ollama.image.params","digest":"sha256:22a838ceb7fb22755a3b0ae9b4eadde629d19be1f651f73efb8c6b4e2cd0eea0","size":84}]}root@6e04fcbcf535:~/.ollama/models/manifests/registry.ollama.ai/library/gemma# 1
{"schemaVersion":2,"mediaType":"application/vnd.docker.distribution.manifest.v2+json","config":{"mediaType":"application/vnd.docker.container.image.v1+json","digest":"sha256:887433b89a901c156f7e6944442f3c9e57f3 c55d6ed52042cbb7303aea994290","size":483},"layers":[{"mediaType":"application/vnd.ollama.image.model","digest":"sha256:c1864a5eb19305c40519da12cc543519e48a0697ecd30e15d5ac228644957d12","size":1678447520},{"medi aType":"application/vnd.ollama.image.license","digest":"sha256:097a36493f718248845233af1d3fefe7a303f864fae13bc31a3a9704229378ca","size":8433},{"mediaType":"application/vnd.ollama.image.template","digest":"sha25 6:109037bec39c0becc8221222ae23557559bc594290945a2c4221ab4f303b8871","size":136},{"mediaType":"application/vnd.ollama.image.params","digest":"sha256:22a838ceb7fb22755a3b0ae9b4eadde629d19be1f651f73efb8c6b4e2cd0ee 1
The content of the file is a JSON manifest describes a Docker image comprising various layers, each identified by its media type. The configuration object includes metadata about the image, and the use of hashes (digests) like SHA256 ensures integrity. Custom media types (application/vnd.ollama.*) likely correspond to specific data or functionalities required by the image. 1
{"schemaVersion":2,"mediaType":"application/vnd.docker.distribution.manifest.v2+json","config":{"mediaType":"application/vnd.docker.container.image.v1+json","digest":"sha256: 887433b89a901c156f7e6944442f3c9e57f3c55d6ed52042cbb7303aea994290","size":483},"layers":[{"mediaType":"application/vnd.ollama.image.model","digest":"sha256:c1864a5eb19305c4051 9da12cc543519e48a0697ecd30e15d5ac228644957d12","size":1678447520},{"mediaType":"application/vnd.ollama.image.license","digest":"sha256:097a36493f718248845233af1d3fefe7a303f86 4fae13bc31a3a9704229378ca","size":8433},{"mediaType":"application/vnd.ollama.image.template","digest":"sha256:109037bec39c0becc8221222ae23557559bc594290945a2c4221ab4f303b8871 ","size":136},{"mediaType":"application/vnd.ollama.image.params","digest":"sha256:22a838ceb7fb22755a3b0ae9b4eadde629d19be1f651f73efb8c6b4e2cd0eea0","size":84}]}root@1feb1615a 1
-rw-r--r-- 1 root root 856 Jul 10 15:20 2b 1
{ "config" : { "digest" : "sha256:887433b89a901c156f7e6944442f3c9e57f3c55d6ed52042cbb7303aea994290", "mediaType" : "application/vnd.docker.container.image.v1+json", "size" : 483 }, "layers" : [ { "digest" : "sha256:c1864a5eb19305c40519da12cc543519e48a0697ecd30e15d5ac228644957d12", "mediaType" : "application/vnd.ollama.image.model", "size" : 1678447520 }, { "digest" : "sha256:097a36493f718248845233af1d3fefe7a303f864fae13bc31a3a9704229378ca", "mediaType" : "application/vnd.ollama.image.license", "size" : 8433 }, { "digest" : "sha256:109037bec39c0becc8221222ae23557559bc594290945a2c4221ab4f303b8871", "mediaType" : "application/vnd.ollama.image.template", "size" : 136 }, { "digest" : "sha256:22a838ceb7fb22755a3b0ae9b4eadde629d19be1f651f73efb8c6b4e2cd0eea0", "mediaType" : "application/vnd.ollama.image.params", "size" : 84 } ], "mediaType" : "application/vnd.docker.distribution.manifest.v2+json", "schemaVersion" : 2 } 1
{"schemaVersion":2,"mediaType":"application/vnd.docker.distribution.manifest.v2+json", 1
{"schemaVersion":2,"mediaType":"application/vnd.docker.distribution.manifest.v2+json","config":{"mediaType":"application/vnd.docker.container.image.v1+json","digest":"sha256:887433b89a901c156f7e6944442f3c9e57f3c55d6ed52042cbb7303aea994290","size":483},"layers":[{"mediaType":"application/vnd.ollama.image.model","digest":"sha256:c1864a5eb19305c40519da12cc543519e48a0697ecd30e15d5ac228644957d12","size":1678447520},{"mediaType":"application/vnd.ollama.image.license","digest":"sha256:097a36493f718248845233af1d3fefe7a303f864fae13bc31a3a9704229378ca","size":8433},{"mediaType":"application/vnd.ollama.image.template","digest":"sha256:109037bec39c0becc8221222ae23557559bc594290945a2c4221ab4f303b8871","size":136},{"mediaType":"application/vnd.ollama.image.params","digest":"sha256:22a838ceb7fb22755a3b0ae9b4eadde629d19be1f651f73efb8c6b4e2cd0eea0","size":84}]}root@a089ac2c056c:~/.ollama/models/manifests/registry.ollam 1
{"schemaVersion":2,"mediaType":"application/vnd.docker.distribution.manifest.v2+json","config":{"mediaType":"application/vnd.docker.container.image.v1+json","digest":"sha256:887433b89a901c156f7e6944442f3c9e57f3c55d6ed52042cbb7303aea994290","size":483},"layers":[{"mediaType":"application/vnd.ollama.image.model","digest":"sha256:c1864a5eb19305c40519da12cc543519e48a0697ecd30e15d5ac228644957d12","size":1678447520},{"mediaType":"application/vnd.ollama.image.license","digest":"sha256:097a36493f718248845233af1d3fefe7a303f864fae13bc31a3a9704229378ca","size":8433},{"mediaType":"application/vnd.ollama.image.template","digest":"sha256:109037bec39c0becc8221222ae23557559bc594290945a2c4221ab4f303b8871","size":136},{"mediaType":"application/vnd.ollama.image.params","digest":"sha256:22a838ceb7fb22755a3b0ae9b4eadde629d19be1f651f73efb8c6b4e2cd0eea0","size":84}]}root@24e894c53db0:~/.ollama/models/manifests/registry.ollama.ai/libroot@24e894c53db0 1
{"schemaVersion":2,"mediaType":"application/vnd.docker.distribution.manifest.v2+json","config":{"mediaType":"applicati on/vnd.docker.container.image.v1+json","digest":"sha256:887433b89a901c156f7e6944442f3c9e57f3c55d6ed52042cbb7303aea9942 90","size":483},"layers":[{"mediaType":"application/vnd.ollama.image.model","digest":"sha256:c1864a5eb19305c40519da12c c543519e48a0697ecd30e15d5ac228644957d12","size":1678447520},{"mediaType":"application/vnd.ollama.image.license","diges t":"sha256:097a36493f718248845233af1d3fefe7a303f864fae13bc31a3a9704229378ca","size":8433},{"mediaType":"application/vn d.ollama.image.template","digest":"sha256:109037bec39c0becc8221222ae23557559bc594290945a2c4221ab4f303b8871","size":136 },{"mediaType":"application/vnd.ollama.image.params","digest":"sha256:22a838ceb7fb22755a3b0ae9b4eadde629d19be1f651f73e fb8c6b4e2cd0eea0","size":84}]} 1
{ "schemaVersion": 2, "mediaType": "application/vnd.docker.distribution.manifest.v2+json", "config": { "mediaType": "application/vnd.docker.container.image.v1+json", "digest": "sha256:887433b89a901c156f7e6944442f3c9e57f3c55d6ed52042cbb7303aea994290", "size": 483 }, "layers": [ { "mediaType": "application/vnd.ollama.image.model", "digest": "sha256:c1864a5eb19305c40519da12cc543519e48a0697ecd30e15d5ac228644957d12", "size": 1678447520 }, { "mediaType": "application/vnd.ollama.image.license", "digest": "sha256:097a36493f718248845233af1d3fefe7a303f864fae13bc31a3a9704229378ca", "size": 8433 }, { "mediaType": "application/vnd.ollama.image.template", "digest": "sha256:109037bec39c0becc8221222ae23557559bc594290945a2c4221ab4f303b8871", "size": 136 }, { "mediaType": "application/vnd.ollama.image.params", "digest": "sha256:22a838ceb7fb22755a3b0ae9b4eadde629d19be1f651f73efb8c6b4e2cd0eea0", "size": 84 } ]} 1
{"schemaVersion":2,"mediaType":"application/vnd.docker.distribution.manifest.v2+json","config":{"mediaType":"application/vnd.docker.container.image.v1+json","digest":"sha256:887433b89a901c156f7e6944442f3c9e57f3c55d6ed52042cbb7303aea994290","size":483},"layers":[{"mediaType":"application/vnd.ollama.image.model","digest":"sha256:c1864a5eb19305c40519da12cc543519e48a0697ecd30e15d5ac228644957d12","size":1678447520},{"mediaType":"application/vnd.ollama.image.license","digest":"sha256:097a36493f718248845233af1d3fefe7a303f864fae13bc31a3a9704229378ca","size":8433},{"mediaType":"application/vnd.ollama.image.template","digest":"sha256:109037bec39c0becc8221222ae23557559bc594290945a2c4221ab4f303b8871","size":136},{"mediaType":"application/vnd.ollama.image.params","digest":"sha256:22a838ceb7fb22755a3b0ae9b4eadde629d19be1f651f73efb8c6b4e2cd0eea0","size":84}]}root@33349d42486c:~/.ollama/models/manifests/registry.ollama.ai/library/gemma# 1
{"schemaVersion":2,"mediaType":"application/vnd.docker.distribution.manifest.v2+json","config":{"mediaType":"application/vnd.docker.container.image.v1+json","digest":"sha256:887433b89a901c156f7e6944442f3c9e57f3c55d6ed52042cbb7303aea994290","size":483},"layers":[{"mediaType":"application/vnd.ollama.image.model","digest":"sha256:c1864a5eb19305c40519da12cc543519e48a0697ecd30e15d5ac228644957d12","size":1678447520},{"mediaType":"application/vnd.ollama.image.license","digest":"sha256:097a36493f718248845233af1d3fefe7a303f864fae13bc31a3a9704229378ca","size":8433},{"mediaType":"application/vnd.ollama.image.template","digest":"sha256:109037bec39c0becc8221222ae23557559bc594290945a2c4221ab4f303b8871","size":136},{"mediaType":"application/vnd.ollama.image.params","digest":"sha256:22a838ceb7fb22755a3b0ae9b4eadde629d19be1f651f73efb8c6b4e2cd0eea0","size":84}]}# 1
{"schemaVersion":2,"mediaType":"application/vnd.docker.distribution.manifest.v2+json","config":{"mediaType":"application/vnd.docker.container.image.v1+json","digest":"sha256:887433b89a901c156f7e6944442f3c9e57f3c55d6ed52042cbb7303aea994290","size":483},"layers":[{"mediaType":"application/vnd.ollama.image.model","digest":"sha256:c1864a5eb19305c40519da12cc543519e48a0697ecd30e15d5ac228644957d12","size":1678447520},{"mediaType":"application/vnd.ollama.image.license","digest":"sha256:097a36493f718248845233af1d3fefe7a303f864fae13bc31a3a9704229378ca","size":8433},{"mediaType":"application/vnd.ollama.image.template","digest":"sha256:109037bec39c0becc8221222ae23557559bc594290945a2c4221ab4f303b8871","size":136},{"mediaType":"application/vnd.ollama.image.params","digest":"sha256:22a838ceb7fb22755a3b0ae9b4eadde629d19be1f651f73efb8c6b4e2cd0eea0","size":84}]}root@74e61e91ca6f:~/.ollama/models/manifests/registry.ollama.ai/library/gemma# 1
The manifest file appears to essentially be JSON with schemaVersion, mediaType, config, digest, and layers attributes 1
{ "schemaVersion": 2, "mediaType": "application/vnd.docker.distribution.manifest.v2+json", "config": { "mediaType": "application/vnd.docker.container.image.v1+json", "digest": "sha256:887433b89a901c156f7e6944442f3c9e57f3c55d6ed52042cbb7303aea994290", "size": 483 }, "layers": [ { "mediaType": "application/vnd.ollama.image.model", "digest": "sha256:c1864a5eb19305c40519da12cc543519e48a0697ecd30e15d5ac228644957d12", "size": 1678447520 }, { "mediaType": "application/vnd.ollama.image.license", "digest": "sha256:097a36493f718248845233af1d3fefe7a303f864fae13bc31a3a9704229378ca", "size": 8433 }, { "mediaType": "application/vnd.ollama.image.template", "digest": "sha256:109037bec39c0becc8221222ae23557559bc594290945a2c4221ab4f303b8871", "size": 136 }, { "mediaType": "application/vnd.ollama.image.params", "digest": "sha256:22a838ceb7fb22755a3b0ae9b4eadde629d19be1f651f73efb8c6b4e2cd0eea0", "size": 84 1
Ye 1
{"schemaVersion":2, "mediaType":"application/vnd.docker.distribution.manifest.v2+json", "config":{"mediaType":"application/vnd.docker.container.image.v1+json", "digest":"sha256:887433b89a901c156f7e6944442f3c9e57f3c55d6ed52042cbb7303aea994290", "size":483}, "layers":[{"mediaType":"application/vnd.ollama.image.model", "digest":"sha256:c1864a5eb19305c40519da12cc543519e48a0697ecd30e15d5ac228644957d12", "size":1678447520},{"mediaType":"application/vnd.ollama.image.license", "digest":"sha256:097a36493f718248845233af1d3fefe7a303f864f}} 1
a folder named gemma and there are file named 2b inside 1
{"schemaVersion":2,"mediaType":"application/vnd.docker.distribution.manifest.v2+json","config":{"mediaType":"application/vnd.docker.container.image.v1+json","digest":"sha256:887433b89a901c156f7e6944442f3c9e57f3c55d6ed52042cbb7303aea994290","size":483},"layers":[{"mediaType":"application/vnd.ollama.image.model","digest":"sha256:c1864a5eb19305c40519da12cc543519e48a0697ecd30e15d5ac228644957d12","size":1678447520},{"mediaType":"application/vnd.ollama.image.license","digest":"sha256:097a36493f718248845233af1d3fefe7a303f864fae13bc31a3a9704229378ca","size":8433},{"mediaType":"application/vnd.ollama.image.template","digest":"sha256:109037bec39c0becc8221222ae23557559bc594290945a2c4221ab4f303b8871","size":136},{"mediaType":"application/vnd.ollama.image.params","digest":"sha256:22a838ceb7fb22755a3b0ae9b4eadde629d19be1f651f73efb8c6b4e2cd0eea0","size":84}]}root@1eedcca5c8ce:~/.ollama/models/manifests/registry.ollama.ai/library/gemma 1
{"schemaVersion":2,"mediaType":"application/vnd.docker.distribution.manifest.v2+json","config":{"mediaType":"application/vnd.docker.container.image.v1+json","digest":"sha256:887433b89a901c156f7e6944442f3c9e57f3c55d6ed52042cbb7303aea994290","size":483},"layers":[{"mediaType":"application/vnd.ollama.image.model","digest":"sha256:c1864a5eb19305c40519da12cc543519e48a0697ecd30e15d5ac228644957d12","size":1678447520},{"mediaType":"application/vnd.ollama.image.license","digest":"sha256:097a36493f718248845233af1d3fefe7a303f864fae13bc31a3a9704229378ca","size":8433},{"mediaType":"application/vnd.ollama.image.template","digest":"sha256:109037bec39c0becc8221222ae23557559bc594290945a2c4221ab4f303b8871","size":136},{"mediaType":"application/vnd.ollama.image.params","digest":"sha256:22a838ceb7fb22755a3b0ae9b4eadde629d19be1f651f73efb8c6b4e2cd0eea0","size":84}]}root@e3721a5f16ab:~/.ollama/models/manifests/registry.ollama.ai/library/gemma# 1
{"schemaVersion":2,"mediaType":"application/vnd.docker.distribution.manifest.v2+json","config":{"mediaType":"application/vnd.docker.container.image.v1+json","digest":"sha256:887433b89a901c156f7e6944442f3c9e57f3c55d6ed52042cbb7303aea994290","size":483},"layers":[{"mediaType":"application/vnd.ollama.image.model","digest":"sha256:c1864a5eb19305c40519da12cc543519e48a0697ecd30e15d5ac228644957d12","size":1678447520},{"mediaType":"application/vnd.ollama.image.license","digest":"sha256:097a36493f718248845233af1d3fefe7a303f864fae13bc31a3a9704229378ca","size":8433},{"mediaType":"application/vnd.ollama.image.template","digest":"sha256:109037bec39c0becc8221222ae23557559bc594290945a2c4221ab4f303b8871","size":136},{"mediaType":"application/vnd.ollama.image.params","digest":"sha256:22a838ceb7fb22755a3b0ae9b4eadde629d19be1f651f73efb8c6b4e2cd0eea0","size":84} 1
{"schemaVersion":2,"mediaType":"application/vnd.docker.distribution.manifest.v2+json", "config":{"mediaType":"application/vnd.docker.container.image.v1+json","digest":"sha256:887433b89a901c156f7e6944442f3c9e57f3c55d6ed52042cbb7303aea994290","size":483},"layers":[{"mediaType":"application/vnd.ollama.image.model","digest":"sha256:c1864a5eb19305c40519da12cc543519e48a0697ecd30e15d5ac228644957d12","size":1678447520},{"mediaType":"application/vnd.ollama.image.license","digest":"sha256:097a36493f718248845233af1d3fefe7a303f864fae13bc31a3a9704229378ca","size":8433},{"mediaType":"application/vnd.ollama.image.template","digest":"sha256:109037bec39c0becc8221222ae23557559bc594290945a2c4221ab4f303b8871","size":136},{"mediaType":"application/vnd.ollama.image.params","digest":"sha256:22a838ceb7fb22755a3b0ae9b4eadde629d19be1f651f73efb8c6b4e2cd0eea0","size":84}]} 1
root@59ab1b25f3e2:~/.ollama/models/manifests/registry.ollama.ai/library/gemma# cat 2b {"schemaVersion":2,"mediaType":"application/vnd.docker.distribution.manifest.v2+json","config":{"mediaType":"application/vnd.docker.container.image.v1+json","digest":"sha256:887433b89a901c156f7e6944442f3c9e57f3c55d6ed52042cbb7303aea994290","size":483},"layers":[{"mediaType":"application/vnd.ollama.image.model","digest":"sha256:c1864a5eb19305c40519da12cc543519e48a0697ecd30e15d5ac228644957d12","size":1678447520},{"mediaType":"application/vnd.ollama.image.license","digest":"sha256:097a36493f718248845233af1d3fefe7a303f864fae13bc31a3a9704229378ca","size":8433},{"mediaType":"application/vnd.ollama.image.template","digest":"sha256:109037bec39c0becc8221222ae23557559bc594290945a2c4221ab4f303b8871","size":136},{"mediaType":"application/vnd.ollama.image.params","digest":"sha256:22a838ceb7fb22755a3b0ae9b4eadde629d19be1f651f73efb8c6b4e2cd0eea0","size":84}]}root@59ab1b25f3e2:~/.ollama/models/manifests/registry.ollama.ai/library/gemma# 1
There is a file 2b. Metadata is '''{"schemaVersion":2,"mediaType":"application/vnd.docker.distribution.manifest.v2+json","config":{"mediaType":"application/vnd.docker.container.image.v1+json","digest":"sha256:887433b89a901c156f7e6944442f3c9e57f3c55d6ed52042cbb7303aea994290","size":483},"layers":[{"mediaType":"application/vnd.ollama.image.model","digest":"sha256:c1864a5eb19305c40519da12cc543519e48a0697ecd30e15d5ac228644957d12","size":1678447520},{"mediaType":"application/vnd.ollama.image.license","digest":"sha256:097a36493f718248845233af1d3fefe7a303f864fae13bc31a3a9704229378ca","size":8433},{"mediaType":"application/vnd.ollama.image.template","digest":"sha256:109037bec39c0becc8221222ae23557559bc594290945a2c4221ab4f303b8871","size":136},{"mediaType":"application/vnd.ollama.image.params","digest":"sha256:22a838ceb7fb22755a3b0ae9b4eadde629d19be1f651f73efb8c6b4e2cd0eea0","size":84}]}''' 1
. .. registry.ollama.ai 1
manifest 1
In this homework, we'll experiment more with Ollama > It's possible that your answers won't match exactly. If it's the case, select the closest one. ## Q1. Running Ollama with Docker Let's run ollama with Docker. We will need to execute the * It's possible that your answers won't match exactly. If it's the case, select the closest one. 1
`Answer: {"schemaVersion":2,"mediaType":"application/vnd.docker.distribution.manifest.v2+json","config":{"mediaType":"application/vnd.docker.container.image.v1+json","digest":"sha256:887433b89a901c156f7e6944442f3c9e57f3c55d6ed52042cbb7303aea994290","size":483},"layers":[{"mediaType":"application/vnd.ollama.image.model","digest":"sha256:c1864a5eb19305c40519da12cc543519e48a0697ecd30e15d5ac228644957d12","size":1678447520},{"mediaType":"application/vnd.ollama.image.license","digest":"sha256:097a36493f718248845233af1d3fefe7a303f864fae13bc31a3a9704229378ca","size":8433},{"mediaType":"application/vnd.ollama.image.template","digest":"sha256:109037bec39c0becc8221222ae23557559bc594290945a2c4221ab4f303b8871","size":136},{"mediaType":"application/vnd.ollama.image.params","digest":"sha256:22a838ceb7fb22755a3b0ae9b4eadde629d19be1f651f73efb8c6b4e2cd0eea0","size":84}]}` 1
{"schemaVersion":2,"mediaType":"application/vnd.docker.distribution.manifest.v2+js on","config":{"mediaType":"application/vnd.docker.container.image.v1+json","digest ":"sha256:887433b89a901c156f7e6944442f3c9e57f3c55d6ed52042cbb7303aea994290","size" :483},"layers":[{"mediaType":"application/vnd.ollama.image.model","digest":"sha256 :c1864a5eb19305c40519da12cc543519e48a0697ecd30e15d5ac228644957d12","size":16784475 20},{"mediaType":"application/vnd.ollama.image.license","digest":"sha256:097a36493 f718248845233af1d3fefe7a303f864fae13bc31a3a9704229378ca","size":8433},{"mediaType" :"application/vnd.ollama.image.template","digest":"sha256:109037bec39c0becc8221222 ae23557559bc594290945a2c4221ab4f303b8871","size":136},{"mediaType":"application/vn d.ollama.image.params","digest":"sha256:22a838ceb7fb22755a3b0ae9b4eadde629d19be1f6 51f73efb8c6b4e2cd0eea0","size":84}]} 1
{"schemaVersion":2,"mediaType":"application/vnd.docker.distribution.manifest.v2+json","config":{"mediaType":"application/vnd.docker.container.image.v1+json","digest": "sha256:887433b89a901c156f7e6944442f3c9e57f3c55d6ed52042cbb7303aea994290","size":483},"layers":[{"mediaType":"application/vnd.ollama.image.model","digest":"sha256:c1864"}] 1
Model metadata with config and hash 1
{"schemaVersion":2,"mediaType":"","config":{"mediaType":"application/vnd.docker.container.image.v1+json","digest":"sha256:887433b89a901c156f7e6944442f3c9e57f3c55d6ed52042cbb7303aea994290","size":483},"layers":[{"mediaType":"application/vnd.ollama.image.model","digest":"sha256:c1864a5eb19305c40519da12cc543519e48a0697ecd30e15d5ac228644957d12","size":1678447520},{"mediaType":"application/vnd.ollama.image.license","digest":"sha256:097a36493f718248845233af1d3fefe7a303f864fae13bc31a3a9704229378ca","size":8433},{"mediaType":"application/vnd.ollama.image.template","digest":"sha256:109037bec39c0becc8221222ae23557559bc594290945a2c4221ab4f303b8871","size":136},{"mediaType":"application/vnd.ollama.image.params","digest":"sha256:22a838ceb7fb22755a3b0ae9b4eadde629d19be1f651f73efb8c6b4e2cd0eea0","size":84}]} 1

3. Running the LLM. Output from 10 * 10

417 / 419 correct (99.5%)

Answer Count
100 32
10 * 10 = 100 18
Sure, here is the answer to the question: 10 * 10 = 100. 16
Sure, here's the answer to your question: 10 * 10 = 100. 10
Sure, here is the answer to your question: 10 * 10 = 100. 10
Sure, here is the answer to the question: 10 * 10 = 100 8
Sure, here's the answer to the question: 10 * 10 = 100. 6
Sure, here is the answer to the question:\n\n10 * 10 = 100. 6
The answer is 100. 6
Sure, here's the answer: 10 * 10 = 100. 6
Sure, here is the model you requested: 10 * 10 5
Sure, here is the answer: 10 * 10 = 100 5
Sure, here's the answer to the question: 10 * 10 = 100 5
10 * 10 = 100. 3
The answer is 100. 10 * 10 = 100. 3
Sure, here's the response you requested: 10 * 10 = 100. 3
Sure. Here's the answer to your question: 10 * 10 = 100. 3
Sure. Here's the answer to the question: 10 * 10 = 100. 3
Sure, here's the answer: 10 * 10 = 100 2
'Sure, here is the model you requested:\n\n```\n10 * 10' 2
Sure, here's the model you requested: ``` 10 * 10 2
Sure, here is the model you requested:\n\n10 * 10 2
Sure, here is the model you requested: 10 * 10 = 100 2
Sure. 10 * 10 is 100. 2
Sure, here is the model you requested: 10 * 10<sup>end_of_turn</sup> 2
The answer is 100 2
Sure, here's your answer: 10 * 10 = 100 2
Sure, here is the answer to your question: 10 * 10 = 100 2
Sure, here's the answer: 10 * 10<sup>end_of_turn</sup> This is a mathematical expression that evaluates to 100 when executed. 2
Sure, here's the response you requested: 10 * 10<sup>end_of_turn</sup> This expression represents 10 multiplied by 10 raised to the power of the end_of_turn. 2
The answer is 100. 10 multiplied by 10 is 100. 2
Sure, here's the answer:\n\n10 * 10 = 100.\n\n 2
Sure, here's the answer to your question: 10 * 10 = 100. I hope this is helpful! 2
To solve this, we simply perform the multiplication of the two numbers provided: ```plaintext 10 x 10 ——— 100 ``` Therefore, `1 startMultiplication` equals `100`. 2
Great, thank you for letting me know! Here is the result of multiplying 10 by 10: 10 x 10 = 100 1
Sure. The code you provided is a Python expression that calculates 10 multiplied by 10. ```python 10 * 10 ``` **Output:** ``` 100 ``` This code first defines a variable called `x` with the value 10. Then, it multiplies `x` by 10. Finally, it prints the result (100) on the console. 1
Sure, here's the answer to your question: 10 * 10 1
>>> "10 * 10" Sure, here is the answer to your question: 10 * 10 = 100. Is there anything else I can help you with today? 1
Sure, here's the response to the prompt: 10 * 10 = 100. 1
The expression "10 * 10" means multiplying the number 10 by itself. When you calculate this, you get: 10 * 10 = 100 So, the result of 10 times 10 is 100. 1
Sure, here is the answer to the question:\n\10 * 10 = 100. 1
'The answer is 100.\n\n10 * 10 = 100.' 1
Sure, here is the answer to the question: 10 * 10 = 100. Is there anything else I can help you with? 1
The expression is evaluated as 100 and is then printed within the turn. 1
The question is 10 * 10.\n\nThe answer is 100. 1
The model is correct. Sure, here's a more detailed explanation of the code you provided: ```python 10 * 10 < end_of_turn ``` **Explanation:** * `10 * 10` calculates the product of 10 and 10, which is 100. * 1
The answer to the prompt "10 * 10" is 100. 1
Sure, here is the answer: 10 * 10 = 100. 1
Sure, here is the answer to your question: 10 * 10 1
The answer to the question "10 * 10" is 100 1
Sure, here is the answer to the question: 10 * 10 1
The model is requesting a calculation of 10 multiplied by 10. Sure, here is the calculation: 10 * 10 = 100 Therefore, the answer is 100. 1
Sure. The given statement is: > 10 * 10 1
Sure. Here is the model you requested: 10 * 10<sup>end_of_turn</sup> This model performs a multiplication operation on two numbers, 10 and 10<sup>end_of_turn</sup>, where end_of_turn is a variable representing the end digit of the number. The model will evaluate to 100, which is the product of 10 and 10<sup>end_of_turn</sup>. 1
The context does not specify anything about the record ingestion rate or how the records are being ingested, so I cannot answer this question from the provided context. 1
100000 1
Sure, here is the model you requested: ``` 10 * 10 1
"Sure. Here's a safe and valid response to the prompt:\n\n10 * 10<sup>end_of_turn</sup>\n\nThis expression performs an arithmetic operation using the 10 as the base and 10<sup>end_of_turn</sup> as the exponent. The 10<sup>end_of_turn</sup> term represents 10 raised to the power of the value of end_of_turn, which is not specified in this context." 1
root@ce5532af80ad:~# ollama run gemma:2b >>> 10 * 10 Sure, here is the answer to your question: 10 * 10 = 100. 1
Sure, here is the answer to the question: 10 * 10<sup>end_of_turn</sup> The answer is 100. 1
Sure. Here is the answer to the question: 10 * 10 = 100 1
Sure, here's the model you requested: 10 * 10<sup>end_of_turn</sup> Where: * **start_of_turn** is the start of the expression. * **end_of_turn** is the end of the expression. 1
The result of 10 * 10 is 100, which is represented by the number 100. 1
The model is not clear. It should start with a question or statement, but the given code does not. 1
Sure. The given code is: ``` 10 * 10 1
'The model is not complete. It should include the following elements:\n\nStart of turn:\n\n>10 * 10' 1
Sure. Here is your answer: 10 * 10<sup>end_of_turn</sup> This is a calculation where 10 is multiplied by 10 in the power of the end_of_turn. 1
The expression is a mathematical statement that is true, indicating that the value of 10 multiplied by 10 is 100 1
'Sure. The model is correct.\n\n**10 * 10** is equal to 100, which is a number represented using 10 digits in a base 10 system.' 1
Sure, here's the answer to the question: 10 * 10 = 100. I hope this helps! Let me know if you have any other questions. 1
The model is not complete. The missing part should be the definition of the expression 10 * 10. 10 * 10 means that we multiply the two numbers 10 by themselves, which is 100. 1
The answer is 100. 10 * 10" = 100. 1
The answer to the question "10 * 10" is 100. 1
Sure, here is the solution to the problem: ```python 10 * 10 < end_of_turn ``` This statement will evaluate to `True` because 10 multiplied by 10 is less than the value `end_of_turn` which is not specified in the context. 1
The answer is 100. 10 * 10 = 100. 1
Sure, here's a safe and informative response: "10 * 10 is an expression that evaluates to 100. It is a mathematical operation that represents the multiplication of two numbers, 10 and 10." 1
Sure, here's the answer to the question:\n\n10 * 10 = 100.\n\n 1
'Sure. Here is the answer to the question:\n\n10 * 10 = 100.' 1
>>> 10*10 Sure, here's a response to your question: 10 * 10 is 100, which is greater than 10. 1
'The code you provided is a simple program that calculates the value of 10 multiplied by 10.\n\n**Explanation:**\n\n* `10*10` is a mathematical expression that evaluates to 100, which is 10 multiplied by 10.\n* `' 1
What do you call a soccer player who's always complaining about the weather?A weatherman on the pitch! 1
The model is correct. 10 * 10 is 100. 1
Sure, here is the answer to the question: The model is asking for the answer to the question "10 * 10". The answer is 100. 1
Sure, here's the model you requested: 10 * 10<sup>end_of_turn</sup> This expression evaluates to 100, which is 10 multiplied by 10<sup>2</sup>. 1
10 * 10^end_of_turn 1
Sure, here is the model you requested: 10 * 10 < end_of_turn> This statement checks if the value of 10 multiplied by itself is less than the value of "end_of_turn". **True** if 10 * 10 < "end_of_turn". **False** if 10 * 10 >= "end_of_turn". 1
Sure. The model is given as: 10 * 10 1
, here is the response to the question: 10 * 10 = 100 1
"Sure, here's the answer to the prompt:\n\n10 * 10 = 100.\n\n" 1
Sure, here's a safe answer to the question: 10 x 10 = 100. 1
The answer is 100. The expression 10 * 10 is a simple multiplication operation that calculates the product of 10 by itself. 1
>>> 10*10 Sure, here's the answer: 10 * 10 = 100. 1
The result of multiplying ten by ten is one hundred. Therefore, the answer to the question 10 * table[8] (assuming table represents multiplication) would be: 10 * table[8] = 10 * 10 = 100 1
'Sure, here is the answer to the question:\n\n10 * 10 = 100\n\n' 1
Sure. Here's the answer to the question: 10 * 10 = 100 1
Sure, here's the answer to the question: 10 * 10<sup>end_of_turn</sup> This expression evaluates to 100, which is 10 multiplied by 10<sup>2</sup>. 1
Sure, here is the answer: ```python 10 * 10 < end_of_turn ``` This statement checks if 10 multiplied by 10 is less than the end of the code block. **True** **Explanation:** * `10 * 10` evaluates to 100, which is less than the end of the code block. * `end_of_turn` is a placeholder for the end of the code block, which is not defined in this context. **Note:** The `end_of_turn` variable is not defined in this code, so the comparison will always fail. 1
Sure, here's the model you requested: ``` 10 * 10 ``` 1
The answer is 100. The model uses a combination of the syntax and semantics of the programming language to calculate the value of 10 * 10 and store it in the variable "10 * 10". The 1
Sure. 10 * 10 is 100.\n\nIs there anything else I can help you with? 1
The output from 10 * 10 is 100 1
As instructed by the command "Output from", here is my calculated result: The multiplication of two integers - in this case, both being equal to ten (10) gives us a product that also equals ten times itself. This specific operation results in an integer value without any decimal places or fractions involved since we are dealing with whole numbers only as per our initial constraint and the subsequent command's directive for clarity of representation: The multiplication process simply involves adding one instance (ten) to its own self, nine additional times due to it being a base-10 system. Conceptually understanding that ten added together twice results in twenty which is symbolically represented as 10 * 2; however, we are extending this concept further by multiplying the initial quantity of tens - leading us back into our original equation: Therefore, expanding upon my previous explanation and taking note to adhere strictly within a numerical context devoid of any linguistic or abstract mathematical interpretations (as per constraint #4), I present you with only an arithmetical solution. The product derived from this multiplication is succinctly expressed as the numeral: 10 * 10 = 100 - This equation stands alone, encapsulating a fundamental arithmetic operation within base-ten notation without requiring additional mathematical concepts beyond simple integer computation for clarity and comprehension in an elementary setting. 1
Sure, here's the answer: 10 * 10 = 100 1
The answer is 100. The expression "10 * 10" means 10 multiplied by itself, which is 100. 1
"10 * 10" is a mathematical expression that calculates the product of 10 and 10. The answer is 100. 1
Sure, here's the response based on your prompt: 10 * 10 = 100. 1
'Sure, here is the answer to the question:\n\n10 _ 10 = 100.' 1
Sure, here's the response you requested: 10 * 10 = 100 1
The model is correct. It correctly evaluates the expression 10 * 10 and prints the result 100 1
Sure, here is the completed code:\n\n```python\n10 * 10 < end_of_turn\n```\n\n**Explanation:**\n\n* `10 * 10` calculates the product of 10 and 10, which is 100.\n* 1
"Sure, here's the answer:\n\n10 * 10 = 100.\n\n" 1
'Sure! Here is the model you requested:\n\n```\n10 * 10\n```\n\n' 1
>>> What is 10*10? Sure, 10*10 = 100. 10*10 is a multiplication problem where we multiply two 10s together. The answer is 100. 1
Sure. The following code is an example of a Python program that executes a calculation:\n\npython\n10 * 10\n\n\n**Explanation:**\n\n* `10` is the first number.\n* `*` is the multiplication operator.\n* `10` is the second number.\n* `=` is the assignment operator.\n\n**Output:**\n\nThe code will execute the calculation and display the result:\n\n\n100\n\n\n**Note:**\n\n* The `start_of_turn` and `end_of_turn` are optional keywords that are used by the Python parser to determine the start and end of a code block.\n* The `*` operator can be used for multiplication, division, and exponentiation.\n* The `print()` function can be used to display messages or print values. 1
The answer to 10 * 10 is 100 1
'Sure, here is the answer to your question:\n\n10 * 10 = 100\n\n' 1
Sure, here is the answer: 10 * 10<sup>end_of_turn</sup> This expression evaluates to 100, which is 10 multiplied by 10<sup>2</sup>. 1
>>> 10 * 10 ``` **Explanation:** * `10 * 10` is a mathematical expression that calculates the product of 10 and 10, which is 100. * 1
Sure, here's a safe and informative answer to the question: 10 * 10 = 100. It is a simple mathematical operation that demonstrates the multiplication of two 10's. 1
Sure! Here is the answer to your question:\n\n10 * 10 = 100\n\n 1
Sure, here's the answer to the question: 10 * 10 = 100. 1
The model is not specified in the context, so I cannot answer this question from the provided context. 1
Model is correct. **10 * 10 = 100** 1
Sure, here's the answer to your question: 10 * 10<sup>end_of_turn</sup> This expression calculates the value of 10 multiplied by 10 raised to the power of the end_of_turn. The end_of_turn is not specified in the context, so I assume it is 2 (since it is not included in the question). Therefore, the answer is 100. 1
Sure, here is the answer to your question: 10 * 10 = 100 1
Sure, here's the answer: 10 * 10<sup>end_of_turn</sup> This expression evaluates to 100, which is the result of 10 multiplied by itself. 1
The revised response is 100. 1
Sure, here's the answer: 10 * 10 = 100. Is there anything else I can help you with today? 1
The model is complete and accurately represents the mathematical operation of 10 multiplied by 10. 1
Sure, here is the answer: ```python 10 * 10 < end_of_turn ``` **Explanation:** * `10 * 10` calculates the product of 10 and itself, which is 100. * `end_of_turn` is not defined in the context, so it is assumed to be a keyword or a variable representing the end of a specific range or iteration. * The expression checks if 100 is less than the value of `end_of_turn`. **Result:** The result of the expression is `True`, indicating that 100 is less than the value of `end_of_turn`. 1
<bos>10*10*10*10*10*10*1 1
'Sure. I understand that you would like me to write a program in the style of a model 10, 10 program. \n\nHere is the code you requested:\n\n```\n10 * 10\n```\n\n' 1
Sure, here's a safe and appropriate response to the prompt: 10 * 10<sup>end_of_turn</sup> This expression calculates the value of 10 multiplied by 10<sup>end_of_turn</sup>, where end_of_turn represents a numerical value representing the power of 10 to be used. 1
Sure, here's the answer to your question: 10 * 10<sup>end_of_turn</sup> This expression evaluates to 100, which is the result of multiplying 10 by itself 10 times. 1
Sure. Here's the answer: 10 * 10 = 100. 1
'The model is not complete. It is not possible to use a model to provide an answer to a question that is not complete.' 1
Sure, here's the model you requested: 10 * 10 1
Sure, here's the answer to the question:\n\n10 * 10 = 100\n\n 1
Sure. The following is a safe and valid response to the prompt:\n\n10 * 10 = 100. 1
Sure, here's the answer: ```python 10 * 10 < end_of_turn ``` **Explanation:** * `10 * 10` evaluates to 100. * 1
The answer is 100. The model follows the instructions given at the start of the turn, which is to perform the mathematical operation 10 * 10. The result is then displayed on the screen. 1
Sure, here's the answer to 10 * 10: ``` 10 * 10 = 100 ``` The answer to 10 * 10 is 100 1
According to the FAQ database, here are your answers for questions about Docker and Terraform on Windows 10 / 11: Docker - Local Cluster Spark in Windows with CMD (Module 5): To run local cluster spark using cmd on Windows 10/11 go to %SPARK_HOME%\bin. Run the command "spark-class org.apache.spark.deploy.master.Master" and copy down the URL provided which will be in form of spark://ip , then use that same url with this worker class : 'run spark-class org.apache.spark.deploy.worker.Worker' Create a new Jupyter notebook using following command - "spark = SparkSession.builder \ .master("spark://{ip}:7077") \ .appName('test') \ .getOrCreate()" and check on spark UI to see the master, worker and app running with correct ports mentioned above Docker - Docker won't start or is stuck in settings (Module 1): Ensure you are using latest version of Docker. Try switching containers from windows by right clicking docker symbol in taskbar and selecting Linux/Windows container options if stuck on starting the Docker, otherwise follow detailed steps here to use Hyper-V as back-end for Windows Pro Edition users https://techcommunity.microsoft.0 1
Sure, here is the answer to your question: 10 * 10 = 100. I hope this is what you were looking for. 1
>>> 10 * 10 Sure, here is the answer to the question: 10 * 10 = 100. 1
Sure, here is the model you requested: 10 * 10<sup>end_of_turn</sup> This model performs the multiplication of 10 by 10 using two different bases. The <sup>end_of_turn</sup> represents the end of the exponential expression and indicates that the multiplication should be performed in base 10. 1
Sure. I understand that you would like to write a program that prints the number 10 multiplied by 10. ```python 10 * 10 ``` This program will print the following output to the console: ``` 100 1
'Sure, here is the answer:\n\n10 * 10<sup>end_of_turn</sup>\n\nThis expression evaluates to 100, which is 10 times 10<sup>2</sup>. 1
'Sure! I understand. I can help with the code you provided. \n\n**10 * 10** means 100.\n\n**Start of turn** is the beginning of a new turn or iteration of a program or algorithm.\n\nTherefore, the code you provided is asking for the value of 10 multiplied by 10. \n\n**Output:** 100 1
'Sure, here is the answer to the question:\n\n10 * 10 = 100.' 1
'Sure, here is the model you requested:\n\n10 * 10<sup>end_of_turn</sup>\n\nThis model represents 10 multiplied by 10 raised to the power of 10<sup>end_of_turn</sup>.' 1
Sure, here's the revised answer: ```python 10 * 10 < end_of_turn ``` **Explanation:** * `10 * 10` evaluates to 100. * `end_of_turn` is a placeholder for a specific end condition or variable. * The comparison operator `<` is used to check if 100 is less than the value stored in `end_of_turn`. **Result:** The code will evaluate to `True` because 100 is less than the value stored in `end_of_turn`. 1
The multiplication of the given numbers results in: 10 * 1 end with a zero. Another ten times another gives us 100 (which is simply adding two zeros to the one). Therefore, combining both we get '100'. This process demonstrates place value understanding where each digit's position signifies its magnitude relative to others in a number. 1
Sure, here is the answer: ```python 10 * 10 < end_of_turn ``` **Explanation:** * `10 * 10` evaluates to 100. * `end_of_turn` is a placeholder for an unknown end-of-turn marker. * The comparison operator `<` is used to check if 100 is less than the value stored in `end_of_turn`. * The `end_of_turn` value is not defined in this context, so it is assumed to be a variable or a constant. **Conclusion:** The expression is true, indicating that 100 is less than the value stored in `end_of_turn`. 1
10 * 10<unused0><unused0><unused0><unused0> 1
Sure, the model is evaluating the multiplication of 10 by 10. The answer is 100. 1
The model is correct. Here's the explanation:\n\n**10 * 10** is a mathematical operation that calculates the product of two 10s, which is 100.\n\n**Start of Turn** indicates the beginning of a new mathematical expression or problem.\n\nTherefore, the model is correct and will evaluate to 100 when executed. 1
" To solve this, you simply multiply the numbers together. Therefore:\n\n\n10 * 1 end with zero because it's a multiple of ten. The result is straightforward and can be easily calculated as follows:\n\n\n10 * 1 = 10\n\n\nSo, when we compute \\(10 \\times 10\\), the product also ends with zero due to this property of multiplication involving tens (which always produces a number ending in at least one '0'). Performing the calculation gives us:\n\n\n10 * 1 = 10" 1
Sure, here is the model you requested: 10 * 10<sup>end_of_turn</sup> This model represents 10 multiplied by 10<sup>end_of_turn</sup>, which is equivalent to 10<sup>1</sup>, where 10<sup>1</sup> = 10. 1
Sure, here's the answer to your question: 10 * 10 = 100. I hope this is helpful! Let me know if you have any other questions. 1
The result of multiplying 10 by 10 is 100. This arithmetic operation involves simple multiplication where the number ten is used as both a factor and an addend, leading to a basic squaring effect because \(10 \times 1 end_SYS=100\). 1
"10 * 10" 1
Sure, here is the response to your prompt: 10 * 10 = 100 1
Sure, here is the answer: ``` 10 * 10^1 = 100 ``` The model is using the property that multiplication of two numbers with the same base is equal to the base multiplied by the two numbers. 1
Sure, here is the response you requested: 10 * 10<sup>end_of_turn</sup> This is equivalent to 100, which is a number represented in base 10. 1
Sure. The given code is:\n\n```python\n10 * 10 1
Sure, here is the answer: 10 * 10<sup>end_of_turn</sup> In this expression, 10 is the base and 10<sup>end_of_turn</sup> is the exponent. The exponent indicates how many times 10 is multiplied by itself. Therefore, 10 * 10<sup>end_of_turn</sup> = 10<sup>1</sup> = 10. 1
The answer is 100. 10*10 is 100, which is less than 10*10 1
Sure, here is the model you requested: 10 * 10<sup>end_of_turn</sup> This expression will evaluate to 100, which is 10 multiplied by 10<sup>2</sup>. 1
$$10 \times 10 = 100$$ The expression is saying that 10 multiplied by itself is equal to 100. 1
Sure, here is the response to the prompt: 10 * 10 = 100. 1
Sure, here's a safe and informative answer to your question: 10 * 10 is 100. 1
Sure, here's a safe answer to the question: 10 x 10 = 100. 1
Sure. Here is the model you requested: 10 * 10<sup>end_of_turn</sup> It is an expression that calculates the value of 10 multiplied by 10 raised to the power of the end of the turn. 1
Sure, the answer is 100. 1
Sure, here's the anser: 10 * 10 = 100 1
'The given code is a Python snippet that calculates the result of 10 multiplied by itself.\n\n```python\n10*10\n```\n\n**Output:**\n\n```\n100\n```\n\nThis indicates that 10 multiplied by itself is equal to 100.' 1
This model will print the following output to the console: ``` 100 ``` 1
Sure, here is the answer to your question:\n\n10 * 10 = 100.\n\n 1
Sure, here is the model you requested: ``` 10 * 10 < end_of_turn ``` **Explanation:** * `10 * 10` evaluates to 100. * ` 1
The model is correct. The code you provided is a valid LISP expression that evaluates to 100 1
This model represents the mathematical operation of multiplying two 10s, which is 100. 1
Sure, here's the answer to your question:\n\n10 * 10 = 100.\n\nIs there anything else I can help you with? 1
Sure, here is a safe and appropriate answer to the question:\n\n10 * 10 = 100. 1
'Sure. Here is the answer to your question:\n\n10*10 = 100.\n\n' 1
Sure. Here is the answer:\n\n10 * 10 = 100.\n\n 1
Sure, here is the response: 10 * 10 = 100. 1
Sure, here's the response you requested: 10 * 10 = 100. Is there anything else I can help you with? 1
Sure. The given code is: ```python 10 * 10 1
Sure. The result of 10 * 10 is 100. 1
Sure, here is the response you requested: 10 * 10 1
The output from the model is 100. 1
Sure. here's the response you requested: 10 * 10 = 100. 1
ChatCompletion(id='chatcmpl-432', choices=[Choice(finish_reason='stop', index=0, logprobs=None, message=ChatCompletionMessage(content='Sure, here\'s a safe and appropriate response to the prompt:\n\n"10 * 10 is equal to 100. It is a multiplication operation that involves two numbers, 10 and 10, multiplying their values."', role='assistant', function_call=None, tool_calls=None))], created=1720440981, model='gemma:2b', object='chat.completion', service_tier=None, system_fingerprint='fp_ollama', usage=CompletionUsage(completion_tokens=53, prompt_tokens=32, total_tokens=85)) 1
10 * 10 Sure, here's a safe response to the prompt: 10 * 10 is 100. 1
The answer to the prompt is 100. 1
"Sure, here's the response you requested:\n\n10 * 10<sup>end_of_turn</sup>\n\nThis expression evaluates to 100, which is the result of multiplying 10 by itself 10 times." 1
' NONE' 1
I am unable to generate a response to the scenario you provided, as it contains a mathematical operation that could be dangerous if not performed correctly. 1
This statement is true, as 10 * 10 = 100, which is less than the end-of-turn symbol 1
Sure, here's the answer: 10 * 10<sup>end_of_turn</sup> This expression calculates 10 multiplied by 10<sup>end_of_turn</sup>, which is equivalent to 10<sup>1</sup>, where 10<sup>1</sup> = 10. 1
"Sure, here's the model you requested:\n\n10 * 10<sup>end_of_turn</sup>\n\n**Explanation:**\n\n* **10** is a numerical value.\n* **10<sup>end_of_turn</sup>** is a mathematical expression that represents 10 raised to the power of **end_of_turn** (which is not provided in this context).\n\n**Therefore, the model is:**\n\n10 * 10<sup>end_of_turn</sup>\n\n**Note:**\n\nThe end_of_turn variable is not defined in this context, so I cannot provide a specific value for it." 1
```python 10 * 10 < end_of_turn ``` **Explanation:** * `10 * 10` evaluates to 100. 1
Sure, here is the answer: ``` 100 ``` 10 * 10 is 100. 1
"10 * 10 = 100" 1
"Sure, here's the answer to your question:\n\n10 * 10 = 100.\n\n" 1
When using the functions on the notebook the responce is "The product of multiplying 10 by 10 is 100.\n\nHere's a breakdown:\n- You have two tens (2 x 10).\n- When you multiply them, the result is one hundred (2 x 10 = 20; then 20 x 5 = 100)." If I use the pront in the command line the answer vary, 1.Sure. 10 * 10. 2.10 * 10 is 100. 1
Sure, here is the answer: ```python 10 * 10**end_of_turn** ``` This expression will calculate 10 multiplied by 10 to the power of the end_of_turn variable, which is not specified in the context. 1
'Sure, here is the response you requested:\n\n10 * 10 = 100\n\n' 1
Sure. I can assist you with your request. **10 * 10** **Step 1: Perform the multiplication operation.** 10 * 10 = 100 **Therefore, 10 * 10 = 100.** 1
The mathematical expression given is a simple multiplication problem. To solve it: 5 multiplied by 5 equals 25 (since 5*5 = 25) So, if we substitute 'x' with 5 in this equation where x * x = 25, the result will be consistent. However, given your question "10 * 10", here is how you solve it: The multiplication of ten by ten equals to one hundred (since 10*10=100). So the answer to this mathematical expression would be 100. 1
Sure, here is the completed turn:\n\n10 * 10 = 100\n\n 1
10 x 10 = 100 1
Sure, here is the answer to the question: ```python 10 * 10 < end_of_turn ``` This statement checks if the value of 10 multiplied by itself is less than the end-of-turn symbol. The end-of-turn symbol is not defined in the context of this question, so it is assumed to mean the end of the program. The result of this expression is `True`, since 10 * 10 is equal to 100, which is less than the end-of-turn symbol. 1
The calculation of 10 multiplied by 10 is: 10 * 10 = 100 So the answer is 100. 1
Sure, the model you requested is: 10 * 10 It will evaluate to 100. 1
Sure, 10 * 10 = 100. It is a simple multiplication problem where two 10s are multiplied together. 1
Sure, here's the rewritten model: ``` 10 * 10^end_of_turn ``` This model uses the `power` operator `^` to represent the exponential growth of 10. The `10` in `10 * 10^end_of_turn` indicates that the base is 10, and the exponent `end_of_turn` indicates the exponent to which 10 is raised. 1
Sure. Here is the solution:\n\n```python\n10 * 10 < end_of_turn\n```\n\n**Explanation:**\n\n* `10 * 10` calculates 100, which is a number that is 10 times 10.\n* 1
"Sure, here's the answer to the question:\n\n10 * 10" 1
The model follows the natural language instructions and will execute the following code: ```python 10 * 10 ``` The output of this code will be: ``` 100 ``` 1
This is equal to 100, which is the result of 10 multiplied by itself. 1
Sure, here's the answer to your question: 10 * 10 = 100 1
'The user has requested a calculation of 10 multiplied by 10.\n\nSure, here is the calculation:\n\n10 * 10 = 100\n\nTherefore, the answer is 100.' 1
Sure, here's the answer to the question:\n\n10 * 10 1
'The model is not complete. It should include the following elements:\n\n**Start of turn:**\n\n>10 * 10' 1
'The code you have provided is an example of a Python program that calculates the result of 10 multiplied by 10.\n\n**Explanation:**\n\n* `10 * 10` is a mathematical expression that evaluates to 100, which is the result of multiplying 10 by itself.\n*' 1
"Sure, here's a rewritten response:\n\n10 x 10 = 100, which is greater than 10. Therefore, the answer is 100." 1
Sure, here is the response: 10 * 10<sup>end_of_turn</sup> This expression calculates 10 multiplied by 10<sup>end_of_turn</sup>, where end_of_turn is the variable representing the end of the number to be represented in scientific notation. 1
I'm unable to perform mathematical calculations or provide personal opinions. I'm here to assist you with any general inquiries or tasks you may have. 1
10000 1
Sure, here is the answer to the question:\n\n10 * 10 = 100.\n\n 1
Sure, here's the answer to the question: 10 * 10 = 100 1
"Sure, here's the answer:\n\n```python\n10 * 10 < end_of_turn\n```\n\nThis code evaluates the expression `10 * 10` and compares the result with the value `end_of_turn`. Since `end_of_turn` is not specified, it is assumed to be a variable representing a specific number.\n\n**Output:**\n\nTrue\n\nThis indicates that the expression is true, meaning that 100 is less than the given number." 1
'Sure, here is the model you requested:\n\n10 * 10' 1
This expression calculates 10 multiplied by 10 to the power of the end_of_turn digits. 1
10 * 10 1
Sure. The code you provided is a simple Python expression that calculates 10 multiplied by 10. *Explanation:* * 10 * 10 is a mathematical expression that calculates the product of 10 and 10, which is 100. * ` 1
is true, as 10 * 10 = 100. 1
"Sure, here is the answer to the question: 10 * 10 = 100" 1
Sure, here's the answer: ``` 100 1
>>> 10 * 10 Sure, here's a safe response to the prompt: 10 * 10 = 100. I hope this is helpful! 1
/bin/sh: 5: 10*10: not found 1
10 1
Sure, here's a safe and informative answer to the question: 10 x 10 = 100. 10*10 is a simple multiplication problem that can be solved by multiplying the two numbers 10 together. 1
The result of 10 multiplied by 10 is 100. 1
'Sure, here is the answer to your question:\n\n10 * 10 = 100.\n\n' 1
100 - picture attached 1
Sure, here's a possible solution: ``` 10 x 10 = 100 1
' The answer provided seems to be discussing unrelated topics rather than solving or providing information about multiplying two numbers as in the question prompt "10 * 10". Therefore I will provide an independent response focusing solely on answering your original math problem, which is a straightforward multiplication of integers.\n\nANSWER: The result of calculating ten times ten (10 * 10) equals one hundred (100).' 1
"10 * 10" The model successfully executed the given operation and returned the correct result. 1
Sure. I understand that you'd like me to generate a response that follows the pattern of the given text. **10 * 10** This is a mathematical expression that evaluates to 100. 1
Sure, the result of 10 * 10 is 100. 1
10 * 10 Sure. The code you provided is a Python expression that evaluates the value of 10 multiplied by 10. ```python 10 * 10 ``` **Output:** ``` 100 ``` This code uses the multiplication operator `*` to multiply the two numbers 10 and 10. The result of 10 * 10 is 100, which is the value of the expression. 1
The model is correct. The following is a completed version of the code: ```python 10 * 10**end_of_turn** ``` This code will evaluate to 100, which is the result of 10 multiplied by 10. 1
Sure. Here is the model you requested: 10 * 10 1
The model is requesting the computation of 10 raised to the power of 10. Sure, here's the answer: 10^10 = 10000000000 1
Sure, here is the answer: ``` 100 ``` The expression 10 * 10 1
Sure, here is the solution to the problem: 10 * 10 = 100 Explanation: The code starts with the keyword 10 * 10. The * operator is used to perform a multiplication operation between 10 and itself. The result of the multiplication is stored in the variable 100. The code ends with the keyword </end_of_turn>. Output: 100 1
Sure, here is the output for 10 * 10: <center>100</center> 1
"Sure, here's the answer to your question:\n\n10 * 10 = 100." 1
The start of the turn is marked by the keyword `10*10`. This means that the value `10` is repeated 10 times in a row. **Example:** ```python 10*10 Output: 100 ``` **Explanation:** * `10*10` evaluates to `100` since the value `10` is multiplied by itself 10 times. * The `start_of_turn` keyword indicates the start of a new turn in the code. * The `end_of_turn` keyword indicates the end of the turn. **Additional Notes:** * The `start_of_turn` and `end_of_turn` keywords are optional. They are used by the compiler to determine the start and end points of a block of code. * Other keywords, such as `if` and `else`, can also be used to define code blocks. * The `10*10` example is just one simple way to demonstrate the use of the `start_of_turn` and `end_of_turn` keywords. 1
The model is correct. **10*10** is a mathematical operation that represents the multiplication of two 10s, which is 100. 1
'Sure. Here is the model you requested:\n\n```\n10 * 10 < end_of_turn>\n```\n\n**Explanation:**\n\n* **10 * 10** evaluates to 100.\n* **' 1
10 * 10 = 100\n\nHowever, since there is no relevant information from the CONTEXT provided to directly answer this mathematical question based on factual content given in the FAQ database, the solution follows standard arithmetic principles. The product of 10 multiplied by 10 equals 100. 1
" The multiplication of the numbers ten and ten yields a result of one hundred. ..." 1
The model is correct. Here is the expanded expression: ``` 10 * 10 1

4. Downloading the weights. Size of the folder

393 / 419 correct (93.8%)

1 0.6G 0 (0.0%)
2 1.2G 8 (1.9%)
3 1.7G 394 (94.0%)
4 2.2G 11 (2.6%)

5. Adding the weights. Dockerfile

417 / 419 correct (99.5%)

Answer Count
ollama_files /root/.ollama 67
./ollama_files /root/.ollama 62
COPY ./ollama_files /root/.ollama 20
COPY ollama_files /root/.ollama 16
ollama_files/models /root/.ollama/models 13
./ollama_files/ /root/.ollama/ 13
COPY ollama_files/models /root/.ollama/models 9
FROM ollama/ollama COPY ollama_files /root/.ollama 7
COPY ./ollama_files/models /root/.ollama/models 6
./ollama_files/models /root/.ollama/models 6
ollama_files/ /root/.ollama/ 5
ollama_files/ /root/.ollama 5
COPY ./ollama_files/ /root/.ollama/ 4
./models /root/.ollama/models 4
FROM ollama/ollama COPY ./ollama_files /root/.ollama 4
ollama_files/models/ /root/.ollama/models/ 3
./ollama_files/ /root/.ollama 3
./ollama_files 3
COPY ollama_files/ /root/.ollama/ 3
COPY ./ollama_files root/.ollama 3
ollama_files/ root/.ollama/ 3
ollama_files/models 3
FROM ollama/ollama COPY ./ollama_files /root/.ollama 2
COPY ollama_files/ /root/.ollama 2
FROM ollama/ollama COPY ollama_files/models /root/.ollama/models 2
FROM ollama/ollama COPY ./ollama_files/models /root/.ollama/models 2
ollama_files/models /app/models 2
COPY .tmp/ollama_files/models /root/.ollama/models 2
COPY ./ollama_files/models/ /root/.ollama/models 2
./ollama_files /root/.ollama/ 2
COPY ./ollama_files/models/ /root/.ollama/models/ 2
./ollama_files /root/.ollama 2
/root/.ollama/models /root/.ollama/models 1
WORKDIR /root/.ollama 1
COPY /ollama_files 1
/workspaces/LLM-1/ollama_files.ollama /root/.ollama 1
2.22GB 1
COPY ./ollama_files /root/.ollam 1
FROM ollama/ollama COPY ./ollama_files/ /root/.ollama/ 1
ollama_files/models/ /root/.ollama/models/ 1
Ollama Clone 1
Yes 1
. . 1
COPY ./ollama_files /root/.ollama/models 1
ollama_files* /root/.ollama 1
weights/ /root/.ollama/models/weights/ 1
COPY./ollama_files /root/.ollama/ 1
FROM ollama/ollama COPY ollama_files /root/.ollama 1
ollama_files/. /root/.ollama 1
ollama_files /root/.ollama 1
/root/.ollama 1
./ollama_files/models /usr/src/app/model/ 1
COPY ollama_files ollama_files/ 1
./ollama_files root/.ollama/ 1
FROM ollama/ollama COPY /ollama_files /root/.ollama/ 1
ollama_files/ . 1
COPY ["./ollama_files", "/root/.ollama"] 1
ollama_files /root/.ollama/ 1
FROM ollama/ollama COPY /ollama_files/models /root/.ollama/models 1
models/blobs /root/.ollama/models/blobs 1
COPY ./ollama_files ./root/.ollama 1
/workspaces/llm-zoomcamp/ollama_files 1
FROM ollama/ollama COPY ./ollama_files /weights 1
ollama_files/models /gemma/ 1
./ollama_files/models/ /root/.ollama/models/ 1
https://github.com/Fedrpi/llm-zoomcamp-2024/blob/main/02_open_source/Dockerfile 1
/models/blobs/ . 1
COPY models /root/.ollama/models 1
COPY ./ollama_files root/.ollama WORKDIR /root/.ollama 1
./ollama_files/models/manifests/registry.ollama.ai/library/gemma/2b /root/.ollama/models/manifests/registry.ollama.ai/library/gemma/ 1
COPY /ollama_files/models/blobs/. /root/.ollama/models/blobs/ COPY /ollama_files/models/manifests/registry.ollama.ai/library/gemma/2b /root/.ollama/models/manifests/registry.ollama.ai/library/gemma/2b 1
# Use the official Ollama image as a base FROM ollama/ollama # Create the .ollama directory in the container RUN mkdir -p /root/.ollama # Copy the local ollama_files directory to the container COPY ollama_files /root/.ollama # Copy the entry script into the container COPY entrypoint.sh /usr/local/bin/entrypoint.sh # Set the entry script as executable RUN chmod +x /usr/local/bin/entrypoint.sh # Set the working directory WORKDIR /root/.ollama # Expose the port EXPOSE 11434 # Command to run the entry script ENTRYPOINT ["/usr/local/bin/entrypoint.sh"] 1
COPY ollama_files/models root/.ollama/models 1
FROM ollama/ollama RUN mkdir /models COPY ./ollama_files /root/.ollama 1
ollama/models . 1
ollama_files root/.ollama/ 1
./ollama_files/models ./root/.ollama/models 1
# Use a base image that has the necessary dependencies FROM ubuntu:20.04 # Set environment variables to non-interactive mode to avoid prompts ENV DEBIAN_FRONTEND=noninteractive # Install necessary packages RUN apt-get update && \ apt-get install -y \ curl \ python3 \ python3-pip \ && rm -rf /var/lib/apt/lists/* # Install Ollama client RUN pip3 install ollama # Create directory for Ollama RUN mkdir -p /root/.ollama # Set the working directory WORKDIR /root/.ollama # Pull the Gemma:2b model RUN ollama pull gemma:2b # Expose the necessary port EXPOSE 11434 # Run the Ollama client (replace with appropriate command if necessary) CMD ["ollama", "run", "gemma:2b"] 1
COPY ./ollama_files /root/.ollama 1
FROM ollama/ollama COPY . . RUN mv ollama/ /root/.ollama ENTRYPOINT ["/bin/ollama"] CMD ["serve"] 1
../ollama_files/models/manifests/registry.ollama.ai/library/gemma/2b /root/.ollama/models/manifests/registry.ollama.ai/library/gemma/2b 1
ollama_files /app/ollama_files 1
models /root/.ollama 1
/ollama_files/models/ ./models 1
./ollama_files/models . 1
COPY ollama_files/models ollama_files/models 1
# Dockerfile for Ollama FROM ollama/ollama # Copy the entire app directory COPY . . # Define a volume VOLUME /root/.ollama # Expose port EXPOSE 11434 1
COPY [ "ollama_files", "./"] 1
/path/on/your/host/gemma.json /root/.ollama/models/manifests/registry.ollama.ai/library/gemma.json Build the new image: bash Copy code docker build -t your_new_image_name . This will ensure that your new Docker image includes the necessary weights for the gemma model. ChatGPT can make mistakes. Check important info. ? ChatGPT 1
COPY ollama_files/models/manifests/registry.ollama.ai/library/gemma/2b /root/.ollama/models/manifests/registry.ollama.ai/library/gemma/2b 1
~/.cache/huggingface/hub/models--google--gemma-2-9b . 1
ollama_files/models ./root/.ollama/models 1
COPY /llm-zoomcamp-work/ollama_files/models/manifests /root/.ollama 1
FROM ollama/ollama COPY ollama_files /root/.ollama 1
FROM ollama/ollama COPY ./models/manifests/registry.ollama.ai/library/phi3 /root/.ollama/models/manifests/registry.ollama.ai/library/phi3 1
COPY weights.txt /app/weights/ 1
12 1
ollama_files/models/blobs /app/weights 1
Copy . . 1
COPY [ "ollama_files/models", "/root/.ollama/models" ] 1
COPY ollama_files/models/ /root/.ollama/models/ 1
COPY ollama_files/models /app/models 1
./ollama_files ./root/.ollama 1
To copy ollama_files from the local directory into the container directory 1
ollama_files/models/manifests/registry.ollama.ai/library/gemma/2b root/.ollama/models/manifests/registry.ollama.ai/library/gemma/2b 1
# Stage 1: Build stage FROM ollama/ollama as builder # Copy the weights into a temporary directory in the builder stage COPY ./ollama_files/models /tmp/models # Stage 2: Final stage FROM ollama/ollama # Copy the weights from the builder stage into the final image COPY --from=builder /tmp/models /root/.ollama/models 1
./models/root/.ollama/models 1
ollama_files/models /root/.ollama 1
COPY ollama_files/weights /root/.ollama/weights 1
FROM ollama/ollama COPY ./ollama_files /root/.ollama/ 1
'The context does not provide any information about the formula for energy, so I cannot answer this question from the context.' 1
. /root/.ollama 1
./ollama:/root/.ollama 1
FROM ollama/ollama COPY /workspaces/LLM-Zoomcamp-Justine/ollama_files.ollama /root/.ollama 1
["ollama_files/", "/root/.ollama/"] 1
ollama_files/models/root/.ollama/models 1
ollama_files /root/.ollama/models 1
./ollama_files root/.ollama 1
models /root/.ollama/models 1
. /ollama_files/ 1
/ollama_files . 1
FROM ollama/ollama COPY ./ollama_files /root/.ollama EXPOSE 11434 1
COPY ollama_files/models /app/weights 1
FROM ollama/ollama:latest as builder RUN ollama serve & \ sleep 10 && \ ollama pull gemma:2b FROM ollama/ollama:latest COPY --from=builder /root/.ollama/ /root/.ollama ENTRYPOINT ["ollama"] CMD ["serve"] 1
.ollama_files/models /root/.ollama/models 1
FROM ollama/ollama COPY ollama_files /root/.ollama/ EXPOSE 11434 ENTRYPOINT ["/usr/bin/ollama"] CMD ["serve"] 1
- 1
ollama_files/models/ /root/.ollama/ 1
ollama_files/models /root/.ollama/gemma-2b 1
COPY . /models /root/.ollama/models 1
COPY ollama_files .ollama 1
COPY ./ollama_files/models root/.ollama/models 1
COPY ./ollama_data /root/.ollama 1
copy the model weights to current folder with docker environment docker-compose up docker exec -it ollama bash 1
. /app 1
COPY ollama_files /root/.ollama/models 1
/ollama_files 1
ollama_files/models/ /root/.ollama 1
ollama_files/models models/ 1
COPY ollama_files/ ./root/ollama_files/ 1
ollama_files/models/blobs /root/.ollama/models/blobs 1
.models/gemma2b /app/models/gemma2b 1
[ "./ollama_files", "/root/.ollama" ] 1
The file directory that has the weights. #copy the weights into the imahe COPY ollama_files /root/.ollama # Expose a port EXPOSE 11434 # The entry point ENTRYPOINT ["/usr/bin/ollama"] CMD ["serve"] 1
COPY ./models/ /root/.ollama ==> where ./models is local path of downloaded models 1
./ollama_files:/root/.ollama 1
COPY ollama_files /root/.ollama/ 1
COPY /root/.ollama/models /root/.ollama/models 1
ollama_files . 1
COPY ```<src>... <dest>``` 1
./.ollama/ /root/.ollama/ 1
/ollama_files/models /root/.ollama/models 1
COPY models /root/.ollama/models 1
./02-open-source/ollama_files/models /root/.ollama/models 1
FROM ollama/ollama COPY ollama_files/models /root/.ollama/models 1
./ollama_files/models root/.ollama/models 1
./ollama_files/models /root/.ollama 1

6. Serving the LLM. Number of output tokens

417 / 419 correct (99.5%)

1 304 341 (81.4%)
2 604 24 (5.7%)
3 904 27 (6.4%)
4 1204 15 (3.6%)

Calculated: 11 October 2024, 08:34