BlitzKode

BlitzKode is a locally fine-tuned AI coding assistant built by Sajad using the Qwen2.5-1.5B base model. It's packaged as a GGUF format model for fast local inference with llama.cpp.

Created by Abdulla Sajad
Project: sajadkoder/blitzkode

---

Model Summary

Property	Value
Model Name	BlitzKode
Version	1.6 (CPU optimized)
Base Model	Qwen/Qwen2.5-1.5B-Instruct
Model Format	GGUF (F16, ~3GB)
Primary Runtime	llama.cpp / llama-cpp-python
Artifact	blitzkode.gguf
Context Window	2048 tokens
Creator	Sajad
License	MIT

---

Architecture

• Model Type: Transformer-based LLM (1.5B parameters)
• Architecture: Qwen2
• Quantization: GGUF F16 (~3GB)
• Vocabulary: 151,936 tokens
• Inference: CPU-optimized with llama.cpp

---

Training Pipeline

BlitzKode was fine-tuned through a 4-stage pipeline:

1. SFT (Supervised Fine-Tuning)

• Script: scripts/train_sft.py
• Applies LoRA fine-tuning to coding-style prompts and responses
• Uses PEFT library for efficient parameter-efficient training

2. GRPO (Group Relative Policy Optimization)

• Script: scripts/train_grpo.py
• Uses heuristic reward functions:
  • correctness_reward - Code correctness
  • format_reward - Proper code formatting
  • reasoning_reward - Logic and reasoning

3. DPO (Direct Preference Optimization)

• Script: scripts/train_dpo.py
• Trains on handcrafted chosen/rejected preference pairs
• Improves clarity and answer quality

4. Merge & Export

• Script: scripts/export_gguf.py
• Merges LoRA adapters into base model
• Converts to GGUF format for fast inference

Training Frameworks

• HuggingFace Transformers
• PEFT (LoRA)
• TRL (DPO/GRPO)
• llama.cpp (inference/export)

---

Training Data

Local Datasets

• datasets/raw/blitzkode_sft_v1.json - Seed samples
• datasets/raw/blitzkode_sft_full.json - Extended coding samples

Data Categories

• Arrays and hash maps
• Linked lists
• Trees and graph traversal
• Dynamic programming
• Sorting and searching
• Stack and queue implementations
• Interview-style coding problems
• Code explanations

Optional External Sources

The project can optionally incorporate:

• CodeAlpaca-20k
• GSM8K
• MetaMathQA
• MathInstruct

---

Features

• Multi-language Code Generation - Python, JavaScript, Java, C++, TypeScript, HTML/CSS, SQL
• Code Explanation - Clear comments and documentation
• Bug Fixing - Debug and fix code issues
• Algorithm Help - Data structures and algorithms
• Offline Operation - Runs locally without internet
• Fast Inference - Optimized CPU inference
• Modern UI - ChatGPT-style dark interface

---

Intended Use

Best For

• Local offline coding assistance
• Algorithm and data structure help
• Code generation and explanation
• Educational programming support
• Lightweight code review
• Bug detection and fixing

Out of Scope

• Production code without expert review
• Security-critical applications
• Multi-modal tasks (images not supported)
• Long-context repository analysis
• Real-time high-assurance systems

---

API & Usage

Running the Server

# Install dependencies
pip install llama-cpp-python fastapi uvicorn pydantic

# Start server
python server.py

# Open browser
# http://localhost:7860

API Endpoints

Endpoint	Method	Description
/	GET	Web UI
/health	GET	Health check
/info	GET	API info
/generate	POST	Generate response
/generate/stream	POST	Stream tokens

API Example

# Generate code
curl -X POST http://localhost:7860/generate \
  -H "Content-Type: application/json" \
  -d '{"prompt": "Write hello world in python"}'

# Stream response
curl -X POST http://localhost:7860/generate/stream \
  -H "Content-Type: application/json" \
  -d '{"prompt": "Write a Python function"}'

Python Usage

from llama_cpp import Llama

llm = Llama(
    model_path="blitzkode.gguf",
    n_ctx=2048,
    n_threads=8,
)

prompt = """<|im_start|>system
You are BlitzKode, a coding assistant.<|im_end|>
<|im_start|>user
Write a hello world in Python<|im_end|>
<|im_start|>assistant
"""

result = llm(prompt, max_tokens=256)
print(result["choices"][0]["text"])

---

Prompt Format

Uses ChatML-style template:

<|im_start|>system
You are BlitzKode, an AI coding assistant created by Sajad. You are an expert in Python, JavaScript, Java, C++, and other programming languages. Write clean, efficient, and well-documented code. Keep responses concise and practical.<|im_end|>
<|im_start|>user
{your prompt}<|im_end|>
<|im_start|>assistant

---

Configuration

The server supports environment variables:

Variable	Default	Description
BLITZKODE_MODEL_PATH	blitzkode.gguf	Model file path
BLITZKODE_FRONTEND_PATH	frontend/index.html	UI path
BLITZKODE_HOST	0.0.0.0	Server host
BLITZKODE_PORT	7860	Server port
BLITZKODE_THREADS	CPU count	CPU threads
BLITZKODE_N_CTX	2048	Context window
BLITZKODE_BATCH	128	Batch size
BLITZKODE_MAX_PROMPT_LENGTH	4000	Max prompt chars

---

Limitations

• Text-only input - No image/vision support
• 2048 token context - CPU-friendly but limited
• Small model - May produce incorrect code occasionally
• No formal benchmarks - Not evaluated on standard datasets
• Quantization loss - F16 quantization may reduce accuracy
• Verify outputs - Always review generated code before use

---

Project Structure

BlitzKode/
├── server.py              # FastAPI backend (v1.6)
├── blitzkode.gguf         # Quantized model (~3GB)
├── frontend/
│   └── index.html        # Web UI
├── tests/
│   └── test_server.py    # HTTP tests
├── scripts/
│   ├── train_sft.py       # SFT training
│   ├── train_grpo.py     # GRPO training
│   ├── train_dpo.py      # DPO training
│   ├── export_gguf.py    # Model export
│   └── test_inference.py # Inference test
├── checkpoints/          # LoRA checkpoints
├── datasets/             # Training data
├── MODEL_CARD.md         # This file
└── README.md             # Project docs

---

Version History

Version	Date	Changes
1.6	Current	CPU optimization, faster inference
1.5	Earlier	Added streaming support
1.0	Initial	Base model release

---

License

MIT License - See README.md for details.

Also comply with upstream Qwen base model license when redistributing.

---

Contact

• GitHub: https://github.com/sajadkoder/blitzkode
• Portfolio: https://sajadkoder.vercel.app
• Issues and contributions welcome!

---

Citation

@software{blitzkode2026,
  author = {Sajad},
  title = {BlitzKode - AI Coding Assistant},
  year = {2026},
  url = {https://github.com/sajadkoder/blitzkode}
}