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  4. README.md +63 -0
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+ ---
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+ base_model: teknium/OpenHermes-2.5-Mistral-7B
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+ language:
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+ - en
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+ license: apache-2.0
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+ model-index:
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+ - name: wasm-OpenHermes-2.5-Mistral-7B-q4f32_1
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+ results: []
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+ model_creator: Hugging Face H4
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+ model_name: WASM OpenHermes 2.5 Mistral 7B
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+ model_type: mistral
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+ prompt_template: '<|im_start|>system
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+ You are a helpful AI assistant.<|im_end|>
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+ <|im_start|>user
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+ {prompt}<|im_end|>
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+ <|im_start|>assistant'
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+ ---
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+
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+ # OpenHermes 2.5 (Finetune of Mistral 7B) compiled for WebGPU - q4f32_1
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+
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+ - Original model: [OpenHermes 2.5 - Mistral 7B](https://huggingface.co/teknium/OpenHermes-2.5-Mistral-7B)
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+ - creator: [teknium](https://twitter.com/Teknium1): [support his work](https://github.com/sponsors/teknium1)
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+ - compiled by: Hrishi Olickel: [say hi on Twitter!](https://twitter.com/hrishioa)
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+
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+ ## Description
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+
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+ This is a quantized version of OpenHermes 2.5, a recent finetune of [Mistral-7b](https://huggingface.co/mistralai/Mistral-7B-v0.1) ready to be used for on-browser inference over WebGPU. The model showed good performance in my testing, and [shows promise for actions and RP as well](https://www.reddit.com/r/LocalLLaMA/comments/17p0gut/llm_comparisontest_mistral_7b_updates_openhermes/).
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+
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+ From Teknium:
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+
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+ ```
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+ OpenHermes 2.5 Mistral 7B is a state of the art Mistral Fine-tune, a continuation of OpenHermes 2 model, which trained on additional code datasets.
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+
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+ Potentially the most interesting finding from training on a good ratio (est. of around 7-14% of the total dataset) of code instruction was that it has boosted several non-code benchmarks, including TruthfulQA, AGIEval, and GPT4All suite. It did however reduce BigBench benchmark score, but the net gain overall is significant.
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+
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+ The code it trained on also improved it's humaneval score (benchmarking done by Glaive team) from **43% @ Pass 1** with Open Herms 2 to **50.7% @ Pass 1** with Open Hermes 2.5.
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+
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+ OpenHermes was trained on 1,000,000 entries of primarily GPT-4 generated data, as well as other high quality data from open datasets across the AI landscape. [More details soon]
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+
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+ Filtering was extensive of these public datasets, as well as conversion of all formats to ShareGPT, which was then further transformed by axolotl to use ChatML.
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+ ```
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+
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+ Another finetune, Dolphin 2.2.1 is [also available here](hrishioa/mlc-chat-dolphin-2.2.1-mistral-7b-q4f32_1, compiled for WebGPU.
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+
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+ Compiled with [mlc-llm](https://llm.mlc.ai/).
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+
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+ Very helpful direction provided by [felladrin](https://github.com/felladrin)!
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+
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+ You can use [his example](https://huggingface.co/spaces/Felladrin/Web-LLM-Mistral-7B-OpenOrca) to get quickly started with this model.
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+
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+ ## Prompt template
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+
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+ Prompt format:
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+ This model uses [ChatML](https://github.com/openai/openai-python/blob/main/chatml.md) prompt format.
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+
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+ ```
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+ <|im_start|>system
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+ You are Dolphin, a helpful AI assistant.<|im_end|>
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+ <|im_start|>user
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+ {prompt}<|im_end|>
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+ <|im_start|>assistant
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+
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+ ```
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+ "model_category": "mistral",
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