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README.md
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# Uploaded model
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- **Developed by:**
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- **License:** Hippocratic 3.0 CL-Eco-Extr
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[![Hippocratic License HL3-CL-ECO-EXTR](https://img.shields.io/static/v1?label=Hippocratic%20License&message=HL3-CL-ECO-EXTR&labelColor=5e2751&color=bc8c3d)](https://firstdonoharm.dev/version/3/0/cl-eco-extr.html)
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https://firstdonoharm.dev/version/3/0/cl-eco-extr.html
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- **Finetuned from model :** unsloth/mistral-7b-instruct-v0.2-bnb-4bit
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[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
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# Uploaded model
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- **Developed by:** Caleb DeLeeuw; Copyleft Cultivars, a nonprofit
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- **License:** Hippocratic 3.0 CL-Eco-Extr
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[![Hippocratic License HL3-CL-ECO-EXTR](https://img.shields.io/static/v1?label=Hippocratic%20License&message=HL3-CL-ECO-EXTR&labelColor=5e2751&color=bc8c3d)](https://firstdonoharm.dev/version/3/0/cl-eco-extr.html)
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https://firstdonoharm.dev/version/3/0/cl-eco-extr.html
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- **Finetuned from model :** unsloth/mistral-7b-instruct-v0.2-bnb-4bit
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Using real-world user data from a previous farmer assistant chatbot service and additional curated datasets (prioritizing sustainable regenerative organic farming practices,) Gemma 2B and Mistral 7B LLMs were iteratively fine-tuned and tested against eachother as well as basic benchmarking, whereby the Gemma 2B fine-tune emerged victorious. LORA adapters were saved for each model.
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V3 here scored better in agriculture-focused prelim testing than V1 or V2 of the Mistral series of fine-tunes for the selected dataset.
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This mistral model was trained with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
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[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
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