---
language: en
tags:
- fine-tuned
- causal-lm
- instruction-following
model_type: causal-lm
license: mit
datasets:
- mlabonne/guanaco-llama2-1k
metrics:
- accuracy
- loss
---

# Fine-tuned Llama-2 Model on Guanaco Instruction Dataset

## Model Description
This model is a fine-tuned version of Llama-2 designed specifically for instruction-following tasks. It has been trained on the [Guanaco Llama-2 1k dataset](https://huggingface.co./datasets/mlabonne/guanaco-llama2-1k), enabling it to generate coherent and contextually appropriate responses based on given prompts. This model aims to enhance user interactions through improved understanding of instructions and queries.

## Intended Use
This model is suitable for various applications, including:
- Instruction-following tasks
- Chatbot interactions
- Text completion based on user prompts
- Educational tools for generating explanations or summaries

### How to Use
You can easily load this model using the Transformers library:

```python
from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "gautamraj8044/Llama-2-7b-chat-finetune"  

model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Example usage
input_text = "Please explain the concept of machine learning."
inputs = tokenizer.encode(input_text, return_tensors="pt")
outputs = model.generate(inputs)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))