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# Model Card: Model ID |
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## License |
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MIT License |
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## Languages Supported |
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- English (en) |
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--- |
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## Overview |
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This model is part of the VCC project and has been fine-tuned on the TESTtm7873/ChatCat dataset using the `mistralai/Mistral-7B-Instruct-v0.2` as the base model. The fine-tuning process utilized QLoRA for improved performance. |
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--- |
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## Getting Started |
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To use this model, you'll need to set up your environment first: |
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``` |
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python |
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import torch |
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from transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig |
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# Base model configuration |
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base_model_id = "mistralai/Mistral-7B-Instruct-v0.2" |
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bnb_config = BitsAndBytesConfig( |
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load_in_4bit=True, |
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bnb_4bit_use_double_quant=True, |
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bnb_4bit_quant_type="nf4", |
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bnb_4bit_compute_dtype=torch.bfloat16 |
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) |
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# Loading the base model with quantization config |
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base_model = AutoModelForCausalLM.from_pretrained( |
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base_model_id, |
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quantization_config=bnb_config, |
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device_map="auto", |
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trust_remote_code=True, |
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) |
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# Setting up tokenizer |
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eval_tokenizer = AutoTokenizer.from_pretrained(base_model_id, add_bos_token=True, trust_remote_code=True) |
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from peft import PeftModel |
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# Loading the fine-tuned model |
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ft_model = PeftModel.from_pretrained(base_model, "mistral-journal-finetune/checkpoint-150") |
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# Sample evaluation |
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eval_prompt = "You have the softest fur." |
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model_input = eval_tokenizer(eval_prompt, return_tensors="pt").to("cuda") |
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ft_model.eval() |
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with torch.no_grad(): |
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print(eval_tokenizer.decode(ft_model.generate(**model_input, max_new_tokens=100, repetition_penalty=1.15)[0], skip_special_tokens=True)) |
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``` |
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- **Developed by:** testtm |
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- **Funded by:** testtm |
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- **Model type:** Mistral |
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- **Language:** English |
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- **Finetuned from model:** mistralai/Mistral-7B-Instruct-v0.2 |