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. 
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🧬 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.”
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🧠 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
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⚙️ 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)
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🚀 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
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🛠️ 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
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🧪 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
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📊 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
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🔗 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.