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base_model: NousResearch/Meta-Llama-3.1-8B-Instruct
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tags:
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- llama3
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- instruction-tuning
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- summarization
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- fine-tuned
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- merged
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# 🧠 FlamingNeuron / llama381binstruct_summarize_short_merged
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This is a **merged model** based on [NousResearch/Meta-Llama-3.1-8B-Instruct](https://huggingface.co/NousResearch/Meta-Llama-3.1-8B-Instruct), fine-tuned using LoRA adapters for legal-domain summarization. The LoRA weights have been merged with the base model for standalone use.
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outputs = model.generate(**inputs, max_new_tokens=128)
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print(tokenizer.decode(outputs[0], skip_special_tokens=True))
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## 📚 Citations
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```markdown
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This model was fine-tuned using [TRL](https://github.com/huggingface/trl).
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# 🧠 FlamingNeuron / llama381binstruct_summarize_short_merged
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This is a **merged model** based on [NousResearch/Meta-Llama-3.1-8B-Instruct](https://huggingface.co/NousResearch/Meta-Llama-3.1-8B-Instruct), fine-tuned using LoRA adapters for legal-domain summarization. The LoRA weights have been merged with the base model for standalone use.
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outputs = model.generate(**inputs, max_new_tokens=128)
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print(tokenizer.decode(outputs[0], skip_special_tokens=True))
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```
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## 🏋️ Training Procedure
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This model was trained using **Supervised Fine-Tuning (SFT)** on legal document summaries using the [legal_summarization](https://github.com/lauramanor/legal_summarization) dataset. LoRA adapters were applied during training and merged afterward using `merge_and_unload()`.
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### ⚙️ Framework Versions
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- TRL: 0.16.1
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- Transformers: 4.51.3
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- PyTorch: 2.6.0+cu124
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- Datasets: 3.5.0
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- Tokenizers: 0.21.1
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## 📚 Citations
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This model was fine-tuned using [TRL](https://github.com/huggingface/trl).
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