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--- |
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language: |
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- en |
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library_name: transformers |
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pipeline_tag: text-generation |
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datasets: |
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- yahma/alpaca-cleaned |
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license: apache-2.0 |
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--- |
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<p><h1> speechless-mistral-moloras-7b </h1></p> |
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* [AWQ model(s) for GPU inference.](https://huggingface.co/TheBloke/speechless-mistral-moloras-7B-AWQ) |
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* [GPTQ models for GPU inference, with multiple quantisation parameter options.](https://huggingface.co/TheBloke/speechless-mistral-moloras-7B-GPTQ) |
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* [2, 3, 4, 5, 6 and 8-bit GGUF models for CPU+GPU inference](https://huggingface.co/TheBloke/speechless-mistral-moloras-7B-GGUF) |
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[4-bit GGUF models for CPU+GPU inference](https://huggingface.co/uukuguy/speechless-mistral-moloras-7b/tree/main/GGUF) |
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This model is the static version of moloras (Mixture-of-multi-LoRAs) based on the following 6 Mistral-based LoRa modules. |
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- Intel/neural-chat-7b-v3-1 |
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- migtissera/SynthIA-7B-v1.3 |
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- jondurbin/airoboros-m-7b-3.1.2 |
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- bhenrym14/mistral-7b-platypus-fp16 |
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- teknium/CollectiveCognition-v1.1-Mistral-7B |
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- uukuguy/speechless-mistral-dolphin-orca-platypus-samantha-7b |
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Totally 6 LoRA modules from [speechless-mistral-7b-dare-0.85](https://huggingface.co/speechlessai/speechless-mistral-7b-dare-0.85) |
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The router of mixture-of-multi-loras enables an automatic assembling of LoRA modules, using a gradientfree approach to obtain the coefficients of LoRA modules and requiring only a handful of inference steps for unseen tasks. |
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Code: https://github.com/uukuguy/multi_loras?tab=readme-ov-file#mixture-of-multi-loras |
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## LM-Evaluation-Harness |
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[Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard) |
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| Metric | Value | |
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| --- | --- | |
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| ARC | 59.98 | |
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| HellaSwag | 83.29 | |
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| MMLU | 64.12 | |
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| TruthfulQA | 42.15 | |
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| Winogrande | 78.37 | |
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| GSM8K | 37.68 | |
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| Average | 60.93 | |
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