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Training in progress, step 50

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README.md ADDED
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+ ---
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+ base_model: dicta-il/dictalm2.0-instruct
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+ library_name: transformers
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+ model_name: amlk-e4-raw
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+ tags:
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+ - generated_from_trainer
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+ - sft
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+ - trl
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+ - hf_jobs
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+ licence: license
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+ ---
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+
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+ # Model Card for amlk-e4-raw
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+
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+ This model is a fine-tuned version of [dicta-il/dictalm2.0-instruct](https://huggingface.co/dicta-il/dictalm2.0-instruct).
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+ It has been trained using [TRL](https://github.com/huggingface/trl).
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+
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+ ## Quick start
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+
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+ ```python
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+ from transformers import pipeline
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+
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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="avreymi/amlk-e4-raw", 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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+
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+ ## Training procedure
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+
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+ [<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="150" height="24"/>](https://wandb.ai/avreymi-asraf-hebrew-university-of-jerusalem/amlk-dictalm2-instruct/runs/v1lpqdan)
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+
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+
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+
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+ This model was trained with SFT.
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+
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+ ### Framework versions
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+
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+ - TRL: 1.9.2
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+ - Transformers: 5.14.1
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+ - Pytorch: 2.13.0
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+ - Datasets: 5.0.1
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+ - Tokenizers: 0.22.2
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+
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+ ## Citations
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+
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+
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+
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+ Cite TRL as:
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+
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+ ```bibtex
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+ @software{vonwerra2020trl,
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+ title = {{TRL: Transformers Reinforcement Learning}},
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+ author = {von Werra, Leandro and Belkada, Younes and Tunstall, Lewis and Beeching, Edward and Thrush, Tristan and Lambert, Nathan and Huang, Shengyi and Rasul, Kashif and Gallouédec, Quentin},
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+ license = {Apache-2.0},
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+ url = {https://github.com/huggingface/trl},
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+ year = {2020}
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+ }
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+ ```
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chat_template.jinja ADDED
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+ {{ bos_token }}{% for message in messages %}{% if (message['role'] == 'user') != (loop.index0 % 2 == 0) %}{{ raise_exception('Conversation roles must alternate user/assistant/user/assistant/...') }}{% endif %}{% if message['role'] == 'user' %}{{ '[INST] ' + message['content'] + ' [/INST]
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+ ' }}{% elif message['role'] == 'assistant' %}{{ message['content'] + eos_token + ' ' }}{% else %}{{ raise_exception('Only user and assistant roles are supported!') }}{% endif %}{% endfor %}
tokenizer.json ADDED
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tokenizer_config.json ADDED
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