Transformers
GGUF
Inference Endpoints
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+ ---
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+ license: apache-2.0
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+ library_name: transformers
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+ base_model:
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+ - mistralai/Mistral-7B-Instruct-v0.2
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+ datasets:
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+ - jondurbin/gutenberg-dpo-v0.1
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+ - nbeerbower/gutenberg2-dpo
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+
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+ ---
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+ [![QuantFactory Banner](https://lh7-rt.googleusercontent.com/docsz/AD_4nXeiuCm7c8lEwEJuRey9kiVZsRn2W-b4pWlu3-X534V3YmVuVc2ZL-NXg2RkzSOOS2JXGHutDuyyNAUtdJI65jGTo8jT9Y99tMi4H4MqL44Uc5QKG77B0d6-JfIkZHFaUA71-RtjyYZWVIhqsNZcx8-OMaA?key=xt3VSDoCbmTY7o-cwwOFwQ)](https://hf.co/QuantFactory)
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+
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+ # QuantFactory/Mistral-Gutenberg-Doppel-7B-FFT-GGUF
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+ This is quantized version of [nbeerbower/Mistral-Gutenberg-Doppel-7B-FFT](https://huggingface.co/nbeerbower/Mistral-Gutenberg-Doppel-7B-FFT) created using llama.cpp
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+
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+ # Original Model Card
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+ ![image/png](https://huggingface.co/nbeerbower/Mistral-Small-Gutenberg-Doppel-22B/resolve/main/doppel-header?download=true)
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+ # Mistral-Gutenberg-Doppel-7B-FFT
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+ [mistralai/Mistral-7B-Instruct-v0.2](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2) finetuned on [jondurbin/gutenberg-dpo-v0.1](https://huggingface.co/datasets/jondurbin/gutenberg-dpo-v0.1) and [nbeerbower/gutenberg2-dpo](https://huggingface.co/datasets/nbeerbower/gutenberg2-dpo).
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+ This is a full finetune rather than my usual QLoRA tunes. Mostly for learning purposes.
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+ ### Method
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+ [ORPO tuned](https://mlabonne.github.io/blog/posts/2024-04-19_Fine_tune_Llama_3_with_ORPO.html) with 4x A100 for 2 epochs.