Ilya-bs1/llama4-scout-interpreter
Browse files- .gitattributes +1 -0
- README.md +58 -0
- adapter_config.json +39 -0
- adapter_model.safetensors +3 -0
- special_tokens_map.json +23 -0
- tokenizer.json +3 -0
- tokenizer_config.json +0 -0
- training_args.bin +3 -0
    	
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        README.md
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            ---
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            base_model: meta-llama/Llama-4-Scout-17B-16E-Instruct
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            library_name: transformers
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            model_name: output
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            tags:
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            - generated_from_trainer
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            - trl
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            - sft
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            licence: license
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            ---
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            # Model Card for output
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            This model is a fine-tuned version of [meta-llama/Llama-4-Scout-17B-16E-Instruct](https://huggingface.co/meta-llama/Llama-4-Scout-17B-16E-Instruct).
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            It has been trained using [TRL](https://github.com/huggingface/trl).
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            ## Quick start
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            ```python
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            from transformers import pipeline
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            question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
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            generator = pipeline("text-generation", model="Ilya-bs1/output", device="cuda")
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            output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
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            print(output["generated_text"])
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            ```
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            ## Training procedure
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            This model was trained with SFT.
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            ### Framework versions
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            - TRL: 0.17.0
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            - Transformers: 4.51.0
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            - Pytorch: 2.1.0+cu118
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            - Datasets: 3.6.0
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            - Tokenizers: 0.21.1
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            ## Citations
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            Cite TRL as:
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            ```bibtex
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            @misc{vonwerra2022trl,
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            	title        = {{TRL: Transformer Reinforcement Learning}},
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            	author       = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec},
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            	year         = 2020,
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            	journal      = {GitHub repository},
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            	publisher    = {GitHub},
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            	howpublished = {\url{https://github.com/huggingface/trl}}
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            }
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            ```
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        adapter_config.json
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