From 0e8a36283c62d280406e2df82e7cf3bd6a5c0703 Mon Sep 17 00:00:00 2001 From: "@k33g" Date: Mon, 21 Oct 2024 09:27:32 +0200 Subject: [PATCH] =?UTF-8?q?=F0=9F=9B=9F=20Updated.?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- .vscode/settings.json | 4 +- README.md | 72 +++++++++++++----------- data.js | 124 ++++++++++++++++++++++++++++++++++++++++++ 3 files changed, 167 insertions(+), 33 deletions(-) diff --git a/.vscode/settings.json b/.vscode/settings.json index 12c163b..334a1f1 100644 --- a/.vscode/settings.json +++ b/.vscode/settings.json @@ -1,8 +1,8 @@ { "workbench.iconTheme": "material-icon-theme", "workbench.colorTheme": "GitHub Dark Colorblind (Beta)", - "terminal.integrated.fontSize": 10, - "editor.fontSize": 10, + "terminal.integrated.fontSize": 14, + "editor.fontSize": 14, "files.autoSave": "afterDelay", "files.autoSaveDelay": 1000, "editor.insertSpaces": true, diff --git a/README.md b/README.md index 077b9a1..62bddc4 100644 --- a/README.md +++ b/README.md @@ -1,35 +1,45 @@ # Awesome SMLs -This is the list of the SMLs I use on my Raspberry Pi5 (8GB RAM) with [Ollama](https://ollama.com/): +This is the list of the SMLs I use on my Raspberry Pi5 (8GB RAM) with [Ollama](https://ollama.com/):# Awesome SMLs -| Name | Size | tag | Remark | kind | URL | Good on Pi5 | Usable on Pi5 | +This is the list of the SMLs I use on my Raspberry Pi5 (8GB RAM) with Ollama + +| Name | Size | Tag | Remark | Kind | URL | Good on Pi5 | Usable on Pi5 | | --- | --- | --- | --- | --- | --- | --- | --- | -| CodeGemma 2b | 1.6GB | 2B | Fill-in-the-middle code completion | code | https://ollama.com/library/codegemma:2b |   | x | -| Gemma 2b | 1.7GB | 2B |   |   | https://ollama.com/library/gemma:2b |   | x | -| Gemma2 2b | 1.6GB | 2B |   |   | https://ollama.com/library/gemma2:2b |   | x | -| All-Minilm 22m | 46MB | 22M | Only Embeddings | embedding | https://ollama.com/library/all-minilm:22m | x | x | -| All-Minilm 33m | 67MB | 33M | Only Embeddings | embedding | https://ollama.com/library/all-minilm:33m | x | x | -| DeepSeek Coder 1.3b | 776MB | 1.3B | Trained on both 87% code and 13% natural language | code | https://ollama.com/library/deepseek-coder | x | x | -| TinyLlama 1.1b | 638MB | 1.1B |   |   | https://ollama.com/library/tinyllama | x | x | -| TinyDolphin 1.1b | 637MB | 1.1B |   |   | https://ollama.com/library/tinydolphin | x | x | -| Phi3 Mini | 2.4GB | 3B |   |   | https://ollama.com/library/phi3:mini |   | x | -| Phi3.5 | 2.2GB | 3B |   |   | https://ollama.com/library/phi3.5 |   | x | -| Granite-code 3b | 2.0GB | 3B |   | code | https://ollama.com/library/granite-code |   | x | -| Qwen2 0.5b | 352MB | 0.5B |   |   | https://ollama.com/library/qwen2:0.5b | x | x | -| Qwen2 1.5b | 934MB | 1.5B |   |   | https://ollama.com/library/qwen2:1.5b |   | x | -| Qwen 0.5b | 395MB | 0.5B |   |   | https://ollama.com/library/qwen:0.5b | x | x | -| Qwen2 Math 1.5b | 935MB | 1.5B | Specialized math language model | math | https://ollama.com/library/qwen2-math:1.5b |   | x | -| StarCoder 1b | 726MB | 1B | Code generation model | code | https://ollama.com/library/starcoder:1b | x | x | -| StarCoder2 3b | 1.7GB | 3B |   | code | https://ollama.com/library/starcoder2:3b |   | x | -| Stable LM 2 1.6b | 983MB | 1.6B | LLM trained on multilingual data in English, Spanish, German, Italian, French, Portuguese, and Dutch. |   | https://ollama.com/library/stablelm2 | x | x | -| Stable Code 3b | 1.6GB | 3B | Coding model | code | https://ollama.com/library/stable-code:3b |   | x | -| Replete-Coder Qwen2 1.5b | 1.9GB | 1.5B | Coding capabilities + non-coding data, fully cleaned and uncensored (mat+tool? to be tested) | code | https://ollama.com/rouge/replete-coder-qwen2-1.5b:Q8 | x | x | -| Dolphin-Phi 2.7b | 1.6GB | 2.7B | uncensored |   | https://ollama.com/library/dolphin-phi:2.7b |   | x | -| Dolphin gemma2 2b | 1.6GB | 2B |   |   | https://ollama.com/CognitiveComputations/dolphin-gemma2:2b |   | x | -| allenporter/xlam:1b | 873MB | 1B |   | tools | https://ollama.com/allenporter/xlam:1b |   | x | -| sam4096/qwen2tools:0.5b | 352MB | 0.5B |   | tools | https://ollama.com/sam4096/qwen2tools:0.5b | x | x | -| sam4096/qwen2tools:1.5b | 935MB | 1.5B |   | tools | https://ollama.com/sam4096/qwen2tools:1.5b |   | x | -| mxbai-embed-large | 670MB | 335M | Only Embeddings | embedding | https://ollama.com/library/mxbai-embed-large:335m | x | x | -| nomic-embed-text | 274MB | 137M | Only Embeddings | embedding | https://ollama.com/library/nomic-embed-text:v1.5 | x | x | -| Yi Coder 1.5b | 866MB | 1.5B | Code | code | https://ollama.com/library/yi-coder:1.5b |   | x | -| bge-m3 | 1.2GB | 567M | Only Embeddings | embedding | https://ollama.com/library/bge-m3 |   | x | \ No newline at end of file +| CodeGemma 2b | 1.6GB | 2B | Fill-in-the-middle code completion | code | [Link](https://ollama.com/library/codegemma:2b) | ❌ | ✅ | +| Gemma 2b | 1.7GB | 2B | | | [Link](https://ollama.com/library/gemma:2b) | ❌ | ✅ | +| Gemma2 2b | 1.6GB | 2B | | | [Link](https://ollama.com/library/gemma2:2b) | ❌ | ✅ | +| All-Minilm 22m | 46MB | 22M | Only Embeddings | embedding | [Link](https://ollama.com/library/all-minilm:22m) | ✅ | ✅ | +| All-Minilm 33m | 67MB | 33M | Only Embeddings | embedding | [Link](https://ollama.com/library/all-minilm:33m) | ✅ | ✅ | +| DeepSeek Coder 1.3b | 776MB | 1.3B | Trained on both 87% code and 13% natural language | code | [Link](https://ollama.com/library/deepseek-coder) | ✅ | ✅ | +| TinyLlama 1.1b | 638MB | 1.1B | | | [Link](https://ollama.com/library/tinyllama) | ✅ | ✅ | +| TinyDolphin 1.1b | 637MB | 1.1B | | | [Link](https://ollama.com/library/tinydolphin) | ✅ | ✅ | +| Phi3 Mini | 2.4GB | 3B | | | [Link](https://ollama.com/library/phi3:mini) | ❌ | ✅ | +| Phi3.5 | 2.2GB | 3B | | | [Link](https://ollama.com/library/phi3.5) | ❌ | ✅ | +| Granite-code 3b | 2.0GB | 3B | | code | [Link](https://ollama.com/library/granite-code) | ❌ | ✅ | +| Qwen2.5 0.5b | 398MB | 0.5B | | chat, tools | [Link](https://ollama.com/library/qwen2.5:0.5b) | ✅ | ✅ | +| Qwen2.5 1.5b | 986MB | 1.5B | | chat, tools | [Link](https://ollama.com/library/qwen2.5:1.5b) | ❌ | ✅ | +| Qwen2.5 3b | 1.9GB | 3B | | chat, tools | [Link](https://ollama.com/library/qwen2.5:3b) | ❌ | ✅ | +| Qwen2.5 Coder 1.5b | 986MB | 1.5B | | code, tools | [Link](https://ollama.com/library/qwen2.5-coder:1.5b) | ❌ | ✅ | +| Qwen2 0.5b | 352MB | 0.5B | | | [Link](https://ollama.com/library/qwen2:0.5b) | ✅ | ✅ | +| Qwen2 1.5b | 934MB | 1.5B | | | [Link](https://ollama.com/library/qwen2:1.5b) | ❌ | ✅ | +| Qwen 0.5b | 395MB | 0.5B | | | [Link](https://ollama.com/library/qwen:0.5b) | ✅ | ✅ | +| Qwen2 Math 1.5b | 935MB | 1.5B | Specialized math language model | math | [Link](https://ollama.com/library/qwen2-math:1.5b) | ❌ | ✅ | +| StarCoder 1b | 726MB | 1B | Code generation model | code | [Link](https://ollama.com/library/starcoder:1b) | ✅ | ✅ | +| StarCoder2 3b | 1.7GB | 3B | | code | [Link](https://ollama.com/library/starcoder2:3b) | ❌ | ✅ | +| Stable LM 2 1.6b | 983MB | 1.6B | LLM trained on multilingual data in English, Spanish, German, Italian, French, Portuguese, and Dutch. | | [Link](https://ollama.com/library/stablelm2) | ✅ | ✅ | +| Stable Code 3b | 1.6GB | 3B | Coding model | code | [Link](https://ollama.com/library/stable-code:3b) | ❌ | ✅ | +| Replete-Coder Qwen2 1.5b | 1.9GB | 1.5B | Coding capabilities + non-coding data, fully cleaned and uncensored (mat+tool? to be tested) | code | [Link](https://ollama.com/rouge/replete-coder-qwen2-1.5b:Q8) | ✅ | ✅ | +| Dolphin-Phi 2.7b | 1.6GB | 2.7B | uncensored | | [Link](https://ollama.com/library/dolphin-phi:2.7b) | ❌ | ✅ | +| Dolphin gemma2 2b | 1.6GB | 2B | | | [Link](https://ollama.com/CognitiveComputations/dolphin-gemma2:2b) | ❌ | ✅ | +| allenporter/xlam:1b | 873MB | 1B | | tools | [Link](https://ollama.com/allenporter/xlam:1b) | ❌ | ✅ | +| sam4096/qwen2tools:0.5b | 352MB | 0.5B | | tools | [Link](https://ollama.com/sam4096/qwen2tools:0.5b) | ✅ | ✅ | +| sam4096/qwen2tools:1.5b | 935MB | 1.5B | | tools | [Link](https://ollama.com/sam4096/qwen2tools:1.5b) | ❌ | ✅ | +| mxbai-embed-large | 670MB | 335M | Only Embeddings | embedding | [Link](https://ollama.com/library/mxbai-embed-large:335m) | ✅ | ✅ | +| nomic-embed-text | 274MB | 137M | Only Embeddings | embedding | [Link](https://ollama.com/library/nomic-embed-text:v1.5) | ✅ | ✅ | +| Yi Coder 1.5b | 866MB | 1.5B | Code | code | [Link](https://ollama.com/library/yi-coder:1.5b) | ❌ | ✅ | +| bge-m3 | 1.2GB | 567M | Only Embeddings | embedding | [Link](https://ollama.com/library/bge-m3) | ❌ | ✅ | +| reader-lm:0.5b | 352MB | 0.5b | convert HTML to Markdown | | [Link](https://ollama.com/library/reader-lm:0.5b) | ✅ | ✅ | +| reader-lm:1.5b | 935MB | 1.5b | convert HTML to Markdown | | [Link](https://ollama.com/library/reader-lm:1.5b) | ✅ | ✅ | +| shieldgemma:2b | 1.7GB | 2b | evaluate the safety of text | | [Link](https://ollama.com/library/shieldgemma:2b) | ❌ | ✅ | +| llama-guard3:1b | 1.6GB | 1b | evaluate the safety of text | | [Link](https://ollama.com/library/llama-guard3:1b) | ❌ | ✅ | diff --git a/data.js b/data.js index 538e1d9..39e8c89 100644 --- a/data.js +++ b/data.js @@ -113,6 +113,46 @@ const smlData = "good_on_pi5": false, "usable_on_pi5": true }, + { + "name": "Qwen2.5 0.5b", + "size": "398MB", + "tag": "0.5B", + "remark": "", + "kind": "chat, tools", + "url": "https://ollama.com/library/qwen2.5:0.5b", + "good_on_pi5": true, + "usable_on_pi5": true + }, + { + "name": "Qwen2.5 1.5b", + "size": "986MB", + "tag": "1.5B", + "remark": "", + "kind": "chat, tools", + "url": "https://ollama.com/library/qwen2.5:1.5b", + "good_on_pi5": false, + "usable_on_pi5": true + }, + { + "name": "Qwen2.5 3b", + "size": "1.9GB", + "tag": "3B", + "remark": "", + "kind": "chat, tools", + "url": "https://ollama.com/library/qwen2.5:3b", + "good_on_pi5": false, + "usable_on_pi5": true + }, + { + "name": "Qwen2.5 Coder 1.5b", + "size": "986MB", + "tag": "1.5B", + "remark": "", + "kind": "code, tools", + "url": "https://ollama.com/library/qwen2.5-coder:1.5b", + "good_on_pi5": false, + "usable_on_pi5": true + }, { "name": "Qwen2 0.5b", "size": "352MB", @@ -292,6 +332,90 @@ const smlData = "url": "https://ollama.com/library/bge-m3", "good_on_pi5": false, "usable_on_pi5": true + }, + { + "name": "reader-lm:0.5b", + "size": "352MB", + "tag": "0.5b", + "remark": "convert HTML to Markdown", + "kind": "", + "url": "https://ollama.com/library/reader-lm:0.5b", + "good_on_pi5": true, + "usable_on_pi5": true + }, + { + "name": "reader-lm:1.5b", + "size": "935MB", + "tag": "1.5b", + "remark": "convert HTML to Markdown", + "kind": "", + "url": "https://ollama.com/library/reader-lm:1.5b", + "good_on_pi5": true, + "usable_on_pi5": true + }, + { + "name": "shieldgemma:2b", + "size": "1.7GB", + "tag": "2b", + "remark": "evaluate the safety of text", + "kind": "", + "url": "https://ollama.com/library/shieldgemma:2b", + "good_on_pi5": false, + "usable_on_pi5": true + }, + { + "name": "llama-guard3:1b", + "size": "1.6GB", + "tag": "1b", + "remark": "evaluate the safety of text", + "kind": "", + "url": "https://ollama.com/library/llama-guard3:1b", + "good_on_pi5": false, + "usable_on_pi5": true } ] } + + +function generateMarkdownTable(data) { + const headers = [ + 'Name', 'Size', 'Tag', 'Remark', 'Kind', 'URL', 'Good on Pi5', 'Usable on Pi5' + ]; + + let markdown = `# ${data.title}\n\n${data.description}\n\n`; + markdown += `| ${headers.join(' | ')} |\n`; + markdown += `| ${headers.map(() => '---').join(' | ')} |\n`; + + data.models.forEach(model => { + const row = [ + model.name, + model.size, + model.tag, + model.remark, + model.kind, + `[Link](${model.url})`, + model.good_on_pi5 ? '✅' : '❌', + model.usable_on_pi5 ? '✅' : '❌' + ]; + markdown += `| ${row.join(' | ')} |\n`; + }); + + return markdown; +} + + + +function generate() { + mdContent = `# Awesome SMLs + +This is the list of the SMLs I use on my Raspberry Pi5 (8GB RAM) with [Ollama](https://ollama.com/):` + + mdContent += generateMarkdownTable(smlData) + const fs = require('fs') + fs.writeFileSync("README.md", mdContent); +} + +// Only run the main function if this script is run directly (not imported) +if (require.main === module) { + generate(); +} \ No newline at end of file