Llama-3b-q

🧠 Musheer LLaMA Fine-Tuned Model (LoRA Adapter)

📌 Overview

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.

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🧩 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)

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⚙️ Model Architecture

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⚡ Quantization Details

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

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

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🧠 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

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📦 What’s Included

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

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🚀 How to Use

Step 1 — Install dependencies

pip install transformers peft bitsandbytes

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Step 2 — Load model

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")

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Step 3 — Inference

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]))

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🔄 Optional: Merge Model

To create a standalone full model:

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

⚠️ This will increase size to ~7+ GB

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💻 System Requirements

Minimum:

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

Recommended:

• 16 GB RAM or GPU with ≥6 GB VRAM

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⚠️ Limitations

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

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📊 Notes

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

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📌 Future Improvements

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

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🤝 Acknowledgements

• Hugging Face Transformers
• Unsloth
• Meta AI (LLaMA)

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📬 Contact

Mohd. Musheer

• Email: [email protected]
• GitHub: https://github.com/mohd-musheer
• LinkedIn: https://linkedin.com/in/musheer

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