---
license: mit
datasets:
- rudrajadon18/llama2-1k
- Cynaptics/persona-chat
language:
- en
library_name: transformers
---
# Llama-2-7b-Chat-Finetune

This is a fine-tuned version of the LLaMA-2-7b model. It has been fine-tuned on persona-based data to generate human-like conversational responses.

## Model Description
The model is based on the LLaMA 2 architecture and has been fine-tuned for conversational tasks, specifically targeting persona-based interactions.

## Intended Use
This model is designed to generate human-like text responses in a conversational context. It can be used for chatbot systems, virtual assistants, or other AI-based interaction applications.

## Model Details
- **Architecture:** LLaMA 2 7B
- **Pretraining Data:** The original model was pretrained on a large corpus of text data.
- **Fine-tuning Data:** This model was fine-tuned on persona-based conversational data.

## Library
- Framework: PyTorch
- Model: LLaMA-2
- Fine-Tuned with: LoRA (Low-Rank Adaptation)

## Example Usage
You can use this model via the Hugging Face `transformers` library.
To use the fine-tuned model for text generation based on a persona, follow these steps:

```python
from transformers import pipeline, AutoModelForCausalLM, AutoTokenizer

# Load the fine-tuned model and tokenizer
model_name = "rudrajadon18/Llama-2-7b-chat-finetune"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Define the persona and prompt
prompt = "<persona_b>I love traveling, reading romance novels, and trying new cuisines. I have a deep passion for animals and enjoy volunteering at shelters. I enjoy hiking in the mountains and spending time at the beach. I am a carnivore who loves sharing my experiences with friends over a good meal.</persona_b><s>[INST] \n What do you think about animals? \n</INST>"

# Generate response for the prompt
pipe = pipeline(task="text-generation", model=model, tokenizer=tokenizer, max_length=200)
result = pipe(f"<s>[INST] {prompt} [/INST]")

# Print the generated text
print("Response: \n")
print(result[0]['generated_text'])