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sft_model_rlaif

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README.md ADDED
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+ ---
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+ license: mit
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+ base_model: HuggingFaceH4/zephyr-7b-alpha
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+ tags:
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+ - generated_from_trainer
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+ model-index:
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+ - name: sft_zephyr
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+ results: []
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+ ---
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+
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+ <!-- This model card has been generated automatically according to the information the Trainer had access to. You
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+ should probably proofread and complete it, then remove this comment. -->
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+
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+ # sft_zephyr
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+
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+ This model is a fine-tuned version of [HuggingFaceH4/zephyr-7b-alpha](https://huggingface.co/HuggingFaceH4/zephyr-7b-alpha) on an unknown dataset.
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+
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+ ## Model description
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+
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+ More information needed
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+
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+ ## Intended uses & limitations
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+
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+ More information needed
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+
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+ ## Training and evaluation data
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+
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+ More information needed
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+
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+ ## Training procedure
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+
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+ ### Training hyperparameters
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+
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+ The following hyperparameters were used during training:
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+ - learning_rate: 0.0002
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+ - train_batch_size: 8
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+ - eval_batch_size: 8
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+ - seed: 42
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+ - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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+ - lr_scheduler_type: constant
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+ - num_epochs: 5
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+
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+ ### Training results
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+
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+
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+
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+ ### Framework versions
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+
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+ - Transformers 4.35.2
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+ - Pytorch 2.1.0+cu118
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+ - Datasets 2.15.0
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+ - Tokenizers 0.15.0
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+ "base_model_name_or_path": "HuggingFaceH4/zephyr-7b-alpha",
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+ "bias": "none",
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+ "inference_mode": true,
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+ "lora_dropout": 0.1,
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+ "peft_type": "LORA",
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+ "r": 64,
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+ "revision": null,
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+ "target_modules": [
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+ "v_proj",
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+ "q_proj"
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+ ],
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+ "task_type": "CAUSAL_LM"
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+ }
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+ "chat_template": "{% for message in messages %}\n{% if message['role'] == 'user' %}\n{{ '<|user|>\n' + message['content'] + eos_token }}\n{% elif message['role'] == 'system' %}\n{{ '<|system|>\n' + message['content'] + eos_token }}\n{% elif message['role'] == 'assistant' %}\n{{ '<|assistant|>\n' + message['content'] + eos_token }}\n{% endif %}\n{% if loop.last and add_generation_prompt %}\n{{ '<|assistant|>' }}\n{% endif %}\n{% endfor %}",
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+ "tokenizer_class": "LlamaTokenizer",
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