llama-2-7b-qlora-instruct

Karishma0112/Llama-2-7B QLoRA Instruct Model

language:

• en license: llama2 base_model: meta-llama/Llama-2-7b-hf tags:
• llama-2
• qlora
• peft
• fine-tuning
• instruction-tuning
• text-generation

---

Model Details

Developed by: Karishma Chougule Model type: Large Language Model (LLM)

Model Description

This model is a QLoRA fine-tuned version of Meta's Llama 2 7B language model. The model was fine-tuned using Parameter Efficient Fine-Tuning (PEFT) with Low-Rank Adaptation (LoRA) and 4-bit quantization to reduce memory requirements while maintaining model performance.

The objective of this fine-tuning is to improve the model's ability to follow instructions and generate more relevant, coherent responses.

Model Sources

Base Model: https://huggingface.co/meta-llama/Llama-2-7b-hf Fine-tuning Framework: Hugging Face Transformers + PEFT + BitsAndBytes

Direct Use

This model can be used for:

• Instruction-based text generation
• Question answering
• Conversational AI applications
• Text completion tasks
• Domain-specific assistant applications

Downstream Use

Potential applications:

• Chatbots
• AI assistants
• Document question answering systems
• Knowledge assistants
• RAG-based applications

Out-of-Scope Use

This model should not be used for:

• Generating harmful or misleading content
• Making medical, legal, or financial decisions without human validation
• Producing sensitive information without proper safeguards

Bias, Risks, and Limitations

Large language models may:

• Generate incorrect or hallucinated information
• Reflect biases present in the training data
• Produce unsafe responses in certain scenarios

Users should validate model outputs before using them in production systems.

How to Get Started with the Model

from transformers import AutoModelForCausalLM, AutoTokenizer from peft import PeftModel

base_model = "NousResearch/Llama-2-7b-chat-hf"

adapter_model = "Karishma0112/llama-2-7b-qlora-instruct"

tokenizer = AutoTokenizer.from_pretrained( base_model )

model = AutoModelForCausalLM.from_pretrained( base_model, load_in_4bit=True, device_map="auto" )

model = PeftModel.from_pretrained( model, adapter_model )

prompt = "Explain machine learning in simple terms"

inputs = tokenizer( prompt, return_tensors="pt" ).to(model.device)

outputs = model.generate( **inputs, max_new_tokens=150 )

print( tokenizer.decode( outputs[0], skip_special_tokens=True ) )

Preprocessing [optional]

Training data was:

• Tokenized using the Llama tokenizer
• Converted into input-output instruction format
• Processed with appropriate padding and truncation

Training Method

The model was trained using:

• QLoRA
• LoRA adapters
• 4-bit quantization
• PEFT library

Training Hyperparameters

Example:

• Quantization: 4-bit NF4
• LoRA Rank (r): 8/16
• LoRA Alpha: 16/32
• Learning Rate: 2e-4
• Batch Size: [your batch size]
• Epochs: [your epochs]

Metrics

The model was evaluated based on:

• Response quality
• Instruction following ability
• Relevance
• Coherence

How to Use

Install dependencies:

pip install transformers peft accelerate bitsandbytes