# Model Card: Model ID

## License

MIT License

## Languages Supported

- English (en)

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## 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.

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## 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