Instructions to use jmeadows17/DerivationGeneration8B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use jmeadows17/DerivationGeneration8B with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("unsloth/Meta-Llama-3.1-8B-bnb-4bit") model = PeftModel.from_pretrained(base_model, "jmeadows17/DerivationGeneration8B") - Notebooks
- Google Colab
- Kaggle
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README.md
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**Overview**
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DerivationGeneration8B is a QLoRA fine-tuned from a quantised LLaMa-3.1-8B checkpoint on 15K (LaTeX) synthetic mathematical derivations (containing 4 - 10 equations) via a custom
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**How to use**
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A notebook for inference is available [here](https://github.com/jmeadows17/deriving-equations-with-LLMs/blob/main/llama_evaluation.ipynb).
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**Example prompt**
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**Overview**
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DerivationGeneration8B is a QLoRA fine-tuned from a quantised LLaMa-3.1-8B checkpoint on 15K (LaTeX) synthetic mathematical derivations (containing 4 - 10 equations) via a custom [script](https://github.com/jmeadows17/deriving-equations-with-LLMs/blob/main/llama_3.1_qlora_sft.py) using ROUGE as the validation metric for early stopping (total 6 epochs). This approach outperforms [MathT5](https://huggingface.co/jmeadows17/MathT5-large) in both in-distribution and perturbed evaluation cases presented in [related work](https://arxiv.org/abs/2307.09998).
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**How to use**
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A notebook for inference is available [here](https://github.com/jmeadows17/deriving-equations-with-LLMs/blob/main/llama_evaluation.ipynb).
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**Example prompt**
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