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