NIM-1-3B

NIM-1 (3B)

NIM-1 is a high-efficiency 3-billion parameter local reasoning model engineered for rapid, deterministic task execution, code intelligence, and structured agentic workflows. Built to deliver flagship reasoning density within consumer hardware limits, NIM-1 runs completely offline with ultra-low latency.

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Highlights

• High-Density Reasoning: Tuned on high-signal chain-of-thought demonstrations to handle multi-step logic, Python algorithms, and complex instruction following.
• Consumer-Ready Edge Inference: Consumes under 4 GB of VRAM in 8-bit quantization, delivering 60–90 tokens/sec on entry-level GPUs (such as RTX 3060/4060) or Apple Silicon.
• Zero-Degradation Precision: Packaged with Q8_0 GGUF quantization for maximum output stability and numerical fidelity.
• Autonomous Agent Ready: Built with native support for tool orchestration, structured schema outputs, and multi-turn conversational memory.

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Quickstart with Ollama

Run NIM-1 instantly from Hugging Face:

ollama run hf.co/NIM-AI/NIM-1-3B:NIM-1-3B-Q8_0.gguf

Or build and run directly from the local repository:

ollama create nim-1 -f Modelfile
ollama run nim-1

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Model Specifications

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Architectural & Training Methodology

NIM-1 was trained using parameter-efficient fine-tuning (QLoRA) with custom Triton-accelerated backpropagation kernels:

1. Curated High-Signal Supervision: Trained against an curated dataset of algorithmic logic puzzles, step-by-step math derivations, and software architecture patterns.
2. Quantized Fine-Tuning: Trained using 4-bit base model weight caching, rank-16 target projection adapters (q, k, v, o, gate, up, down), and fused cross-entropy loss.
3. High-Fidelity Export: LoRA adapters merged back into 16-bit floating-point space before single-pass quantization to Q8_0 GGUF format to avoid quantization degradation.

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Hardware Compatibility

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Reproducing Training & Packaging

# Clone the repository
git clone [https://github.com/NIM-AI/NIM-1.git](https://github.com/NIM-AI/NIM-1.git)
cd NIM-1

# Install requirements
pip install -r requirements.txt

# Run the training pipeline
python scripts/train_student.py

# Export weights to GGUF format
python scripts/export_gguf.py