neo-coder-v0.2

🚀 NEO-CODER v0.2.1 — 3.8B Autonomous Coding Agent Model

Made with ❤️ in Tamil Nadu, India 🇮🇳
Created & Developed by: Pragathiswaran B & Sriram T

NEO-CODER v0.2.1 is a state-of-the-art 3.8 Billion Parameter (3.8B) native transformer model specialized for autonomous software engineering, full-stack web/app generation, multi-file reasoning, deep root-cause debugging, and multilingual technical continuity (English, Tamil, and Tanglish).

Developed as an independent, lightweight coding model from Tamil Nadu, NEO-CODER delivers high intelligence with a minimal memory footprint (only 4.10 GB RAM), capable of running fast local inference on standard laptop CPUs without cloud dependence.

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👨‍💻 Authors & Development Credits

• Creators & Lead Developers:
  • Pragathiswaran B
  • Sriram T
• Region of Origin: Tamil Nadu, India
• Project: NEO-CODER Autonomous AI System

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✨ Key Features & Capabilities

• ⚡ Lightweight & High Speed: 3.8B parameters with Q8_0/INT8 hybrid quantization. Runs at 42.5 tokens/sec with sub-15ms first-token latency on standard laptop CPUs (Intel Core i3, 16 GB RAM).
• 🧠 7-Layer Context Hierarchy: Dynamic AST symbol-aware context selection that prunes irrelevant files while prioritizing verified terminal errors, active source files, and test suites.
• 🌐 Full-Stack & Multi-Language Coverage: 99.02% benchmark accuracy across 24 programming languages (Python, TypeScript, JavaScript, Rust, Go, C++, SQL, HTML/CSS, Dart/Flutter, Java, C#, etc.).
• 🗣️ Native Multilingual Continuity: Seamlessly understands and reasons over developer conversations switching fluidly between English, Tamil, and Tanglish (e.g. "indha API-la JWT auth add pannu bro").
• 🛡️ Zero-Hallucination & Safety Gate: Built-in evidence verification and secret scrubbing policy. Emits BLOCKED / INSUFFICIENT CONTEXT on impossible tasks rather than falsely claiming completion.

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📊 Universal Benchmark Scorecard (1,500 Evaluated Tasks)

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🏛️ Model Architecture Specifications

Architecture: NEODecoderModelV2 (Dense Transformer Decoder)
Parameters: 3,800,000,000 (3.8B)
Layers: 36
Hidden Dimension (d_model): 3,072
Attention Heads: 32
Key-Value Heads (GQA): 8
Vocabulary Size: 64,000
Max Sequence Length: 4,096 Tokens
Precision: Q8_0 / INT8 Hybrid
Active Memory Footprint: 4.10 GB RAM
Model Disk Size: ~4.10 GB
Runtime Dependency: 100% Native Independent Engine (Zero Qwen runtime imports)

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💬 Prompt Format & Usage Example

English Prompt

<|im_start|>user
Create a robust FastAPI authentication middleware with JWT verification and rate limiting.
<|im_end|>
<|im_start|>assistant

Tanglish Prompt

<|im_start|>user
bro, indha Express API endpoint-la CORS and error handling middleware add panni, test cases write pannu.
<|im_end|>
<|im_start|>assistant

Tamil Prompt

<|im_start|>user
இந்த பைதான் ஸ்கிரிப்ட்ல SQLite டேட்டாபேஸ் கனெக்ஷன் உருவாக்கி CRUD functions எழுதுங்கள்.
<|im_end|>
<|im_start|>assistant

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📜 License & Citation

This model is licensed under the MIT License.

@misc{neocoder2026,
  title={NEO-CODER v0.2.1: 3.8B Autonomous Coding Agent Model},
  author={Pragathiswaran B and Sriram T},
  location={Tamil Nadu, India},
  year={2026},
  publisher={Hugging Face},
  howpublished={\url{https://huggingface.co}}
}