Llama-2-7b-chat-finetune

Llama-2-7b-chat-finetune

This model is a fine-tuned version of NousResearch/Llama-2-7b-chat-hf using the 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'])