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\n\n| Name | Size | Context Length | Weights | Ollama |\n|---|---|---|---|---|\n| Gemma 2 | 2B | 8k | [🤗 HF](https://huggingface.co/google/gemma-2-2b-it) | [Model](https://ollama.com/library/gemma2:2b)\n| Phi-3.5 Mini | 3.8B | 128k | [🤗 HF](https://huggingface.co/microsoft/Phi-3.5-mini-instruct) | [Model](https://ollama.com/library/phi3.5)\n\n\n## Small Models (recommended for desktop)\n(model sizes 7b-10b) \n\n| Name | Size | Context Length | Weights | Ollama |\n|---|---|---|---|---|\n| Mistral v0.3 | 7.3B | 32k | [🤗 HF](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.3) | [Model](https://ollama.com/library/mistral:7b)\n| Llama 3.1 | 8B | 128k | [🤗 HF](https://huggingface.co/meta-llama/Meta-Llama-3.1-8B-Instruct) | [Model](https://ollama.com/library/llama3.1:8b)\n| Gemma 2 | 9B | 8k | [🤗 HF](https://huggingface.co/google/gemma-2-9b-it) | [Model](https://ollama.com/library/gemma2:9b)\n\n## Medium Models\n(model sizes 11b-16b)\n\n| Name | Size | Context Length | Weights | Ollama |\n|---|---|---|---|---|\n| Mistral NeMo | 12B | 128k | [🤗 HF](https://huggingface.co/mistralai/Mistral-Nemo-Instruct-2407) | [Model](https://ollama.com/library/mistral-nemo)\n\n## Large Models\n(model sizes 17b-33b)\n\n| Name | Size | Context Length | Weights | Ollama |\n|---|---|---|---|---|\n| Gemma 2 | 27B | 8k | [🤗 HF](https://huggingface.co/google/gemma-2-27b-it) | [Model](https://ollama.com/library/gemma2:27b)\n\n## Huge Models (not for GPU-poor)\n(model sizes \u003e33b)\n| Name | Size | Context Length | Weights | Ollama |\n|---|---|---|---|---|\n| Llama 3.1 | 70B | 128k | [🤗 HF](https://huggingface.co/meta-llama/Meta-Llama-3.1-70B) | [Model](https://ollama.com/library/llama3.1:70b)\n\n\u003cbr /\u003e\n\n# Best Finetunes (accepting PRs)\n| Name | Size | Context Length | Weights | Ollama |\n|---|---|---|---|---|\n\n## FAQ\n\n### What makes a model \"good\"?\n\u003cblockquote class=\"twitter-tweet\"\u003e\u003cp lang=\"en\" dir=\"ltr\"\u003eI pretty much only trust two LLM evals right now: Chatbot Arena and r/LocalLlama comments section\u003c/p\u003e\u0026mdash; Andrej Karpathy (@karpathy) \u003ca href=\"https://twitter.com/karpathy/status/1737544497016578453?ref_src=twsrc%5Etfw\"\u003eDecember 20, 2023\u003c/a\u003e\u003c/blockquote\u003e\n\n[r/LocalLLaMA](https://www.reddit.com/r/LocalLLaMA) discussions and [Chatbot Arena](https://chat.lmsys.org/)\n\nDon't trust benchmarks. Download the model and decide for yourself. Models are free so there's no downside lol.\n\n### What's Ollama and why should I use it?\n[Ollama](https://ollama.com/) just makes using LLMs that much more accessible for beginners. It's built on top of [llama.cpp](https://github.com/ggerganov/llama.cpp) and is [FOSS](https://github.com/ollama/ollama).\n\n### Which model should I use?\nThis one depends on *you*. How powerful is your hardare? How complicated is your use-case? A **small** model should is a good starting point. Then, if you need more speed you look at smaller models; if you need more power you turn to larger ones.\n\n### What does \"size\" mean?\nIt means how powerful the model is, how large the download size will be, and how much system resources you will need. According to [Ollama](https://github.com/ollama/ollama#:~:text=You%20should%20have%20at%20least%208%20GB%20of%20RAM%20available%20to%20run%20the%207B%20models%2C%2016%20GB%20to%20run%20the%2013B%20models%2C%20and%2032%20GB%20to%20run%20the%2033B%20models.): You should have at least 8 GB of RAM available to run the 7B models, 16 GB to run the 13B models, and 32 GB to run the 33B models.\n\n### What does \"context length\" mean?\nIt refers to how long your prompts to the LLM can be. Higher is better (but may be slower and lower quality).\n\n### Which quant should I use?\nIf you don't know what `quant` means, stick to defaults. If you do, the largest your computer can handle. For most people, that's `q5_K_M`. Ollama's default is `q4_0`.\n\n\n\n\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fshouryan01%2Fawesome-7b","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fshouryan01%2Fawesome-7b","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fshouryan01%2Fawesome-7b/lists"}