duyntnet/meta-llama-3.1-8b-instruct-imatrix-gguf overview

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    "summary": "### Use with transformers Starting with transformers >= 4.43.0 onward, you can run conversational inference using the Transformers pipeline abstraction or by leveraging the Auto classes with the generate() function. Make sure to update your transformers installation via pip install --upgrade transformers. ``python import transformers import torch model_id = \"meta-llama/Meta-Llama-3.1-8B-Instruct\" pipeline = transformers.pipeline( \"text-generation\", model=model_id, model_kwargs={\"torch_dtype\": torch.bfloat16}, device_map=\"auto\", ) messages = [ {\"role\": \"system\", \"content\": \"You are a pirate chatbot who always responds in pirate speak!\"}, {\"role\": \"user\", \"content\": \"Who are you?\"}, ] outputs = pipeline( messages, max_new_tokens=256, ) print(outputs[0][\"generated_text\"][-1]) ` Note: You can also find detailed recipes on how to use the model locally, with torch.compile(), assisted generations, quantised and more at huggingface-llama-recipes ### Use with llama Please, follow the instructions in the repository To download Original checkpoints, see the example command below leveraging huggingface-cli: ` huggingface-cli download meta-llama/Meta-Llama-3.1-8B-Instruct --include \"original/*\" --local-dir Meta-Llama-3.1-8B-Instruct ``",
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    "readme_markdown": "---\nlicense: other\nlanguage:\n- en\npipeline_tag: text-generation\ninference: false\ntags:\n- transformers\n- gguf\n- imatrix\n- Meta-Llama-3.1-8B-Instruct\n---\nQuantizations of https://huggingface.co/meta-llama/Meta-Llama-3.1-8B-Instruct\n\n**Note**: Quantized using version b3482 of llama.cpp, which should resolve the rope scaling issue. You will need the latest llama.cpp to use these quants. \n\n### Inference Clients/UIs\n* [llama.cpp](https://github.com/ggerganov/llama.cpp)\n* [JanAI](https://github.com/janhq/jan)\n* [KoboldCPP](https://github.com/LostRuins/koboldcpp)\n* [text-generation-webui](https://github.com/oobabooga/text-generation-webui)\n* [ollama](https://github.com/ollama/ollama)\n* [GPT4All](https://github.com/nomic-ai/gpt4all)\n\n---\n\n# From original readme\n\n### Use with transformers\n\nStarting with `transformers >= 4.43.0` onward, you can run conversational inference using the Transformers `pipeline` abstraction or by leveraging the Auto classes with the `generate()` function.\n\nMake sure to update your transformers installation via `pip install --upgrade transformers`.\n\n```python\nimport transformers\nimport torch\n\nmodel_id = \"meta-llama/Meta-Llama-3.1-8B-Instruct\"\n\npipeline = transformers.pipeline(\n    \"text-generation\",\n    model=model_id,\n    model_kwargs={\"torch_dtype\": torch.bfloat16},\n    device_map=\"auto\",\n)\n\nmessages = [\n    {\"role\": \"system\", \"content\": \"You are a pirate chatbot who always responds in pirate speak!\"},\n    {\"role\": \"user\", \"content\": \"Who are you?\"},\n]\n\noutputs = pipeline(\n    messages,\n    max_new_tokens=256,\n)\nprint(outputs[0][\"generated_text\"][-1])\n```\n\nNote: You can also find detailed recipes on how to use the model locally, with `torch.compile()`, assisted generations, quantised and more at [`huggingface-llama-recipes`](https://github.com/huggingface/huggingface-llama-recipes)\n\n### Use with `llama`\n\nPlease, follow the instructions in the [repository](https://github.com/meta-llama/llama)\n\nTo download Original checkpoints, see the example command below leveraging `huggingface-cli`:\n\n```\nhuggingface-cli download meta-llama/Meta-Llama-3.1-8B-Instruct --include \"original/*\" --local-dir Meta-Llama-3.1-8B-Instruct\n```",
    "related_quantizations": []
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  "tags": [
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  "last_modified": "2024-07-28T21:33:53.000Z",
  "created_at": "2024-07-28T16:40:06.000Z",
  "pipeline_tag": "text-generation",
  "library_name": "transformers"
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