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--- |
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base_model: Locutusque/llama-3-neural-chat-v2.2-8B |
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inference: false |
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license: apache-2.0 |
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language: |
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- en |
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pipeline_tag: text-generation |
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tags: |
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- 4-bit |
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- AWQ |
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- text-generation |
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- autotrain_compatible |
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- endpoints_compatible |
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library_name: transformers |
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quantized_by: Suparious |
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--- |
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# Locutusque/llama-3-neural-chat-v2.2-8B AWQ |
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- Model creator: [Locutusque](https://huggingface.co/Locutusque) |
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- Original model: [llama-3-neural-chat-v2.2-8B](https://huggingface.co/Locutusque/llama-3-neural-chat-v2.2-8B) |
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## Model Details |
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I fine-tuned llama-3 8B on an approach similar to Intel's neural chat language model. I have slightly modified the data sources so it is stronger in coding, math, and writing. I use both SFT and DPO-Positive. |
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DPO-Positive dramatically improves performance over DPO. |
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- **Developed by:** Locutusque |
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- **Model type:** Built with Meta Llama 3 |
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- **Language(s) (NLP):** Many? |
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- **License:** Llama 3 license https://huggingface.co/meta-llama/Meta-Llama-3-8B/blob/main/LICENSE |
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### About AWQ |
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AWQ is an efficient, accurate and blazing-fast low-bit weight quantization method, currently supporting 4-bit quantization. Compared to GPTQ, it offers faster Transformers-based inference with equivalent or better quality compared to the most commonly used GPTQ settings. |
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AWQ models are currently supported on Linux and Windows, with NVidia GPUs only. macOS users: please use GGUF models instead. |
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It is supported by: |
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- [Text Generation Webui](https://github.com/oobabooga/text-generation-webui) - using Loader: AutoAWQ |
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- [vLLM](https://github.com/vllm-project/vllm) - version 0.2.2 or later for support for all model types. |
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- [Hugging Face Text Generation Inference (TGI)](https://github.com/huggingface/text-generation-inference) |
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- [Transformers](https://huggingface.co/docs/transformers) version 4.35.0 and later, from any code or client that supports Transformers |
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- [AutoAWQ](https://github.com/casper-hansen/AutoAWQ) - for use from Python code |
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