README.md

---
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
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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

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📦 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

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📜 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

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🙏 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

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🧩 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.