Update README.md

README.md

@@ -32,4 +32,190 @@ datasets:
 - 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.