This repository contains a fine-tuned LoRA adapter built on top of a LLaMA-based model using efficient parameter-efficient fine-tuning (PEFT). The model is trained to improve structured reasoning and solution generation from problem-based inputs.

⚠️ This repository contains LoRA adapter weights only, not the full base model.

🧩 Base Model

Base Model: meta-llama/Meta-Llama-3-3B (or your exact base model name — update if needed)
Architecture: LLaMA (Causal Language Model)
Parameters: ~3 Billion
Precision: float16
Quantization: 4-bit (bitsandbytes - NF4)

⚙️ Model Architecture

Component	Value
Hidden Size	3072
Layers	28
Attention Heads	24
KV Heads	8
Intermediate Size	8192
Activation	SiLU
Max Context Length	131072
Vocabulary Size	128256

⚡ Quantization Details

Method: bitsandbytes (4-bit NF4)
Compute dtype: float16
Double Quantization: Enabled

This allows the model to run efficiently on low-memory systems.

🧠 Training Details

Method: LoRA (Low-Rank Adaptation)
Framework: Unsloth + Hugging Face Transformers
Objective: Improve reasoning, structured outputs, and problem-solving capability
Dataset: Custom dataset with:
- Problem
- Reasoning (thought process)
- Final solution

📦 What’s Included

✅ LoRA adapter weights (~110 MB)
❌ Full base model weights (NOT included)

🚀 How to Use

Step 1 — Install dependencies

pip install transformers peft bitsandbytes

Step 2 — Load model

python

from transformers import AutoModelForCausalLM, AutoTokenizer
from peft import PeftModel

base_model_name = "meta-llama/Meta-Llama-3-3B"

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

tokenizer = AutoTokenizer.from_pretrained(base_model_name)

model = PeftModel.from_pretrained(base_model, "musheer/your-model-name")

Step 3 — Inference

python

input_text = "Solve: What is 2+2?"

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

outputs = model.generate(
    **inputs,
    max_new_tokens=200
)

print(tokenizer.decode(outputs[0]))

🔄 Optional: Merge Model

To create a standalone full model:

python

model = model.merge_and_unload()
model.save_pretrained("merged_model")

⚠️ This will increase size to ~7+ GB

💻 System Requirements

Minimum:

8 GB RAM (with 4-bit quantization)
CPU inference supported

⚠️ Limitations

Requires base model to run
Performance depends heavily on dataset quality
Not optimized for long-context reasoning beyond training distribution

📊 Notes

This is a parameter-efficient fine-tuned model, not a full retrained LLM
Adapter modifies only a small subset of weights (~1–2%)

📌 Future Improvements

Better dataset curation and filtering
Evaluation benchmarks (BLEU, ROUGE, etc.)
Deployment optimization (GGUF / inference acceleration)

🤝 Acknowledgements

Hugging Face Transformers
Unsloth
Meta AI (LLaMA)

📬 Contact

Mohd. Musheer

Email: musheerayan@gmail.com
GitHub: https://github.com/mohd-musheer
LinkedIn: https://linkedin.com/in/musheer

mohd-musheer/Llama-3b-q

🧠 Musheer LLaMA Fine-Tuned Model (LoRA Adapter)

📌 Overview

🧩 Base Model

⚙️ Model Architecture

⚡ Quantization Details

🧠 Training Details

📦 What’s Included

🚀 How to Use

Step 1 — Install dependencies

Step 2 — Load model

Step 3 — Inference

🔄 Optional: Merge Model

💻 System Requirements

Minimum:

Recommended:

⚠️ Limitations

📊 Notes

📌 Future Improvements

🤝 Acknowledgements

📬 Contact

mohd-musheer/
Llama-3b-q