---
license: mit
tags:
- llama
- text-generation
- instruction-following
- llama-2
- lora
- peft
- trl
- sft
---

# Llama-2-7b-chat-finetune

This model is a fine-tuned version of [NousResearch/Llama-2-7b-chat-hf](https://huggingface.co/NousResearch/Llama-2-7b-chat-hf) using the [mlabonne/guanaco-llama2-1k](https://huggingface.co/datasets/mlabonne/guanaco-llama2-1k) dataset. It has been fine-tuned using LoRA (Low-Rank Adaptation) with the PEFT library and the SFTTrainer from TRL.

## Model Description

This model is intended for text generation and instruction following tasks. It has been fine-tuned on a dataset of 1,000 instruction-following examples.

## Intended Uses & Limitations

This model can be used for a variety of text generation tasks, including:

* Generating creative text formats, like poems, code, scripts, musical pieces, email, letters, etc. 
* Answering your questions in an informative way, even if they are open ended, challenging, or strange.
* Following your instructions and completing your requests thoughtfully.


Limitations:

* The model may generate biased or harmful content.
* The model may not be able to follow all instructions perfectly.
* The model may not be able to generate text that is factually accurate.

## Training and Fine-tuning

This model was fine-tuned using the following parameters:

* LoRA attention dimension (lora_r): 64
* Alpha parameter for LoRA scaling (lora_alpha): 16
* Dropout probability for LoRA layers (lora_dropout): 0.1
* 4-bit precision base model loading (use_4bit): True
* Number of training epochs (num_train_epochs): 1
* Batch size per GPU for training (per_device_train_batch_size): 4
* Learning rate (learning_rate): 2e-4

## How to Use

You can use this model with the following code:
```
from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline

model_name = "chaitanya42/Llama-2-7b-chat-finetune"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

prompt = "What is a large language model?"
pipe = pipeline(task="text-generation", model=model, tokenizer=tokenizer, max_length=200)
result = pipe(f"[INST] {prompt} [/INST]")
print(result[0]['generated_text'])
```