File size: 1,829 Bytes
5e9dcb6 1d826fa 5e9dcb6 1d826fa 088c92b 1d826fa 088c92b 1d826fa 088c92b 1d826fa 088c92b 1d826fa 088c92b 1d826fa 5e9dcb6 1d826fa 5e9dcb6 1d826fa 5e9dcb6 1d826fa 5e9dcb6 1d826fa f570089 1d826fa |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 |
---
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 |