peft safetensors en dataset:TESTtm7873/ChatCat base_model:mistralai/Mistral-7B-Instruct-v0.2 license:mit region:us
---
library_name: peft
base_model: mistralai/Mistral-7B-Instruct-v0.2
license: mit
datasets:
- TESTtm7873/ChatCat
language:
- en
---
# 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:

## Model initialization
```python
from transformers import AutoTokenizer, AutoModelForCausalLM, GenerationConfig
from peft import PeftModel
tokenizer = AutoTokenizer.from_pretrained("mistralai/Mistral-7B-Instruct-v0.2")
model = AutoModelForCausalLM.from_pretrained(
    "mistralai/Mistral-7B-Instruct-v0.2",
    load_in_8bit=True,
    device_map="auto",
)
model = PeftModel.from_pretrained(model, "TESTtm7873/MistralCat-1v")
model.eval()
```

## Inference
```python
def evaluate(question: str) -> str:
    prompt = f"The conversation between human and Virtual Cat Companion.\n[|Human|] {question}.\n[|AI|] "
    inputs = tokenizer(prompt, return_tensors="pt")
    input_ids = inputs["input_ids"].cuda()
    generation_output = model.generate(
        input_ids=input_ids,
        generation_config=generation_config,
        return_dict_in_generate=True,
        output_scores=True,
        max_new_tokens=256
    )
    output = tokenizer.decode(generation_output.sequences[0]).split("[|AI|]")[1]
    return output
your_question: str = "You have the softest fur."
print(evaluate(your_question))
```


- **Developed by:** testtm
- **Funded by:** Project TEST
- **Model type:** Mistral
- **Language:** English
- **Finetuned from model:** mistralai/Mistral-7B-Instruct-v0.2