Update README.md

README.md

@@ -11,4 +11,170 @@ datasets:
 - nvidia/Nemotron-RL-instruction_following
 - nvidia/Nemotron-RL-agent-calendar_scheduling
 - nvidia/Nemotron-RL-instruction_following-structured_outputs
----
+---
+Nvidia.Agentic.Coder-4B-GGUF
+
+📌 Model Overview
+
+Model Name: WithinUsAI/Nvidia.Agentic.Coder-4B-GGUF
+Organization: Within Us AI
+Model Type: Code LLM (Agentic, Instruction-Following)
+Parameter Size: 4B
+Format: GGUF (quantized for local inference)
+Primary Use: Agentic coding, tool-using workflows, software engineering reasoning
+
+This model is part of the Within Us AI ecosystem focused on building agentic, reasoning-driven coding systems designed to think, act, and verify like real engineers.  
+
+⸻
+
+🧬 Architecture & Lineage
+
+* Base Family: NVIDIA Nemotron-style 4B class models (inferred lineage from naming + ecosystem alignment)
+* Format Conversion: GGUF quantization for efficient local inference
+* Training Approach:
+    * Instruction-tuned for coding tasks
+    * Agentic workflow emphasis (multi-step reasoning, tool usage)
+    * Likely merged / fine-tuned using Within Us AI proprietary pipelines
+
+Related ecosystem models include:
+
+* NVIDIA-Nemotron-3-Nano-4B
+* Other 4B agentic coders and merges in the same class  
+
+⸻
+
+⚙️ Key Capabilities
+
+🧑‍💻 Code Intelligence
+
+* Multi-language code generation
+* Bug fixing and refactoring
+* Structured output generation
+
+🤖 Agentic Behavior
+
+* Step-by-step reasoning
+* Task decomposition
+* Tool-calling alignment (design goal)
+
+🧠 Reasoning Focus
+
+* Instruction-following with logical chaining
+* Designed for evaluation-style datasets (tests-as-truth philosophy)
+
+⸻
+
+📦 GGUF Quantization
+
+GGUF allows efficient local inference with tools like:
+
+* llama.cpp
+* LM Studio
+* Ollama (GGUF-compatible builds)
+
+Typical quantizations for 4B GGUF models include:
+
+* Q2_K (~1.8GB)
+* Q3_K (~2.0–2.3GB)
+* Q4_K (~2.5GB, recommended balance)  
+
+⸻
+
+🚀 Intended Use
+
+✅ Ideal Use Cases
+
+* Local AI coding assistants
+* Autonomous coding agents
+* SWE-bench style evaluation
+* Tool-augmented workflows
+* Offline developer copilots
+
+⚠️ Limitations
+
+* Smaller 4B parameter size limits deep reasoning vs larger models
+* Performance depends heavily on prompt structure
+* Tool-use requires external orchestration (not built-in runtime)
+
+⸻
+
+🛠️ Usage Example (llama.cpp)
+
+./main -m Nvidia.Agentic.Coder-4B.Q4_K.gguf \
+  -p "Write a Python function to parse JSON logs and extract errors." \
+  -n 512
+
+⸻
+
+🧪 Training Philosophy (Within Us AI)
+
+Within Us AI focuses on:
+
+* Agentic AI systems
+* Test-driven training (tests-as-truth)
+* Diff-first patching workflows
+* Secure and auditable code generation
+* Evaluation-first development pipelines  
+
+⸻
+
+📊 Evaluation
+
+No formal benchmark results published yet.
+
+Expected strengths:
+
+* Strong instruction adherence
+* Lightweight agentic reasoning
+* Efficient local deployment
+
+⸻
+
+📚 Datasets & Training Sources
+
+This model follows the Within Us AI methodology:
+
+* Proprietary datasets created by Within Us AI
+* May include third-party datasets for training (no ownership claimed)
+* Emphasis on:
+    * Code reasoning traces
+    * Agentic workflows
+    * Evaluation-driven samples
+
+⸻
+
+📜 License
+
+License Type: Custom / Other (Within Us AI License)
+
+Terms:
+
+* Within Us AI created the fine-tuning, merging, and training methodology
+* Base model architecture originates from third-party LLM ecosystems (e.g., NVIDIA / Nemotron class)
+* Third-party datasets may be used without claiming ownership
+* Full credit and acknowledgment belong to original dataset and base model creators
+
+⸻
+
+🙏 Acknowledgements
+
+Special thanks to:
+
+* NVIDIA Nemotron ecosystem contributors
+* Open-source GGUF tooling community
+* Dataset creators across Hugging Face
+* The broader open-source AI research community
+
+⸻
+
+🔗 Links
+
+* Model: https://huggingface.co/WithinUsAI/Nvidia.Agentic.Coder-4B-GGUF
+* Organization: https://huggingface.co/WithinUsAI
+
+⸻
+
+🧩 Closing Note
+
+This model is a compact engineer in a bottle 🧪
+Not the biggest brain in the room, but fast, focused, and built to act, not just