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@@ -9,15 +9,6 @@ tags:
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  - merged
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  ---
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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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- ---
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-
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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.
@@ -44,7 +35,19 @@ inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
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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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  - 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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+ ```
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+ ## 🏋️ Training Procedure
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+
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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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+
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  ## 📚 Citations
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  This model was fine-tuned using [TRL](https://github.com/huggingface/trl).