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  ---
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  language:
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  - en
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- license: apache-2.0
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  tags:
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  - text-generation-inference
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  - transformers
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  - unsloth
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  - mistral
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- - trl
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  base_model: cgato/Thespis-CurtainCall-7b-v0.3
 
 
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  ---
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- # Uploaded model
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  - **Developed by:** maldv
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- - **License:** apache-2.0
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- - **Finetuned from model :** cgato/Thespis-CurtainCall-7b-v0.3
 
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- This mistral model was trained 2x faster 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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  ---
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  language:
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  - en
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+ license: cc-by-nc-4.0
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  tags:
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  - text-generation-inference
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  - transformers
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  - unsloth
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  - mistral
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+ - GEB
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  base_model: cgato/Thespis-CurtainCall-7b-v0.3
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+ datasets:
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+ - maldv/crabcanon
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  ---
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+ # SHRDFU-7b β
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  - **Developed by:** maldv
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+ - **License:** cc-by-nc-4.0
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+ - **Finetuned from model:** cgato/Thespis-CurtainCall-7b-v0.3
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+ - **Methodology:** Targeting attention layers with peft to condition; then small full layer tuning; extending intelligence and problem solving w/ crabcanon
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+ As I work on understanding how to layer information in to the model, I'm having a tough time with this one without making it crazy. Maybe crazy was the way to go?
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+ [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="50"/>](https://github.com/unslothai/unsloth)