--- datasets: - bigcode/the-stack-v2 - yulan-team/YuLan-Mini-Datasets - HuggingFaceFW/fineweb-edu - bigcode/the-stack-v2 - mlfoundations/dclm-baseline-1.0 - math-ai/AutoMathText - gair-prox/open-web-math-pro - RUC-AIBOX/long_form_thought_data_5k - internlm/Lean-Workbook - internlm/Lean-Github - deepseek-ai/DeepSeek-Prover-V1 - ScalableMath/Lean-STaR-base - ScalableMath/Lean-STaR-plus - ScalableMath/Lean-CoT-base - ScalableMath/Lean-CoT-plus - opencsg/chinese-fineweb-edu - liwu/MNBVC - vikp/textbook_quality_programming - HuggingFaceTB/smollm-corpus - OpenCoder-LLM/opc-annealing-corpus - OpenCoder-LLM/opc-sft-stage1 - OpenCoder-LLM/opc-sft-stage2 - XinyaoHu/AMPS_mathematica - deepmind/math_dataset - mrfakename/basic-math-10m - microsoft/orca-math-word-problems-200k - AI-MO/NuminaMath-CoT - HuggingFaceTB/cosmopedia - MU-NLPC/Calc-ape210k - manu/project_gutenberg - storytracer/LoC-PD-Books - allenai/dolma --- Agent.Nano.Coder-2B (GGUF) 📌 Model Overview Model Name: WithinUsAI/Agent.Nano.Coder-2B-gguf Organization: Within Us AI Model Type: Lightweight Agentic Code LLM Parameter Size: 2B Format: GGUF (quantized for local inference) Primary Focus: Ultra-efficient coding + agent workflows This model is a compact, high-efficiency coding agent, designed to deliver useful software engineering reasoning in extremely small compute environments. It belongs to the Within Us AI family of agentic coders, emphasizing action-oriented outputs over passive text generation.  ⸻ 🧬 Architecture & Lineage * Model Class: Small-scale transformer (2B parameter range) * Design Goal: Maximize reasoning-per-parameter * Format Conversion: GGUF quantization for local runtime compatibility Ecosystem Context Part of a broader WithinUsAI lineup including: * 4B agentic coders * reasoning-distilled Gemma variants * nano-scale experimental models The Nano series focuses on: “Minimum size, maximum usefulness.” ⸻ 🧠 Core Design Philosophy This model is built around a sharp constraint: If a model only has 2B parameters… every neuron has to earn its place. Key ideas: * Prioritize coding over general chat * Bias toward structured outputs * Encourage step-based reasoning * Optimize for tool-augmented environments ⸻ ⚙️ Key Capabilities 💻 Coding * Python, JavaScript, C++, and more * Function generation and refactoring * Lightweight debugging assistance 🤖 Agentic Behavior * Task decomposition * Instruction-following for multi-step tasks * Compatible with external tool pipelines 🧠 Reasoning (Compact) * Basic chain-of-thought patterns * Logical step breakdowns * Efficient problem-solving within tight parameter limits ⸻ 📦 GGUF Format & Deployment Designed for fast, local inference with minimal hardware. Compatible Runtimes: * llama.cpp * LM Studio * Ollama (GGUF-compatible builds) Typical Quantization Sizes (2B class): * Q4_K_M (~1.1–1.4GB) * Q5_K_M (~1.3–1.6GB) ⸻ 🚀 Intended Use ✅ Ideal Use Cases * Low-resource coding assistants * Embedded / edge AI systems * Fast iteration environments * Local copilots on consumer hardware * Multi-agent systems with many small models ⚠️ Limitations * Smaller parameter count limits deep reasoning depth * Not suited for highly complex multi-domain reasoning * Performance depends heavily on prompt clarity ⸻ 🛠️ Usage Example (llama.cpp) ./main -m Agent.Nano.Coder-2B.Q4_K_M.gguf \ -p "Write a Python function to validate email addresses using regex." \ -n 256 ⸻ 🧪 Training & Methodology Within Us AI approach emphasizes: * Agentic coding datasets * Instruction-tuned workflows * Reasoning traces (lightweight) * Evaluation-driven refinement Data Sources * Proprietary datasets created by Within Us AI * Third-party datasets may be used without ownership claims * Focus on: * Code tasks * Debugging patterns * Structured outputs ⸻ 📊 Expected Performance Profile Capability Strength Coding (basic–intermediate) High Speed / efficiency Very High Reasoning depth Moderate General knowledge Moderate Tool-use readiness High ⸻ 📜 License License Type: Custom / Other (Within Us AI License Model)** Terms: * Base architectures originate from third-party LLM ecosystems * Within Us AI developed: * Fine-tuning methodology * Merging processes * Training pipelines * Third-party datasets are used without ownership claims * Full credit belongs to original creators ⸻ 🙏 Acknowledgements * Open-source LLM community * GGUF / llama.cpp ecosystem * Dataset contributors across Hugging Face * Researchers advancing small-model efficiency ⸻ 🔗 Links * Model: https://huggingface.co/WithinUsAI/Agent.Nano.Coder-2B-gguf * Organization: https://huggingface.co/WithinUsAI ⸻ 🧩 Closing Note This model is like a pocket-sized engineer 🧰⚡ Not built to dominate benchmarks… but to quietly get things done fast, locally, and efficiently.