README.md

---
datasets:
- nvidia/Nemotron-CC-v2
- nvidia/Nemotron-Post-Training-Dataset-v2
- nvidia/Nemotron-Instruction-Following-Chat-v1
- nvidia/Nemotron-Science-v1
- nvidia/Nemotron-Agentic-v1
- nvidia/Nemotron-Competitive-Programming-v1
- nvidia/Nemotron-Math-Proofs-v1
- nvidia/Nemotron-RL-Agentic-Conversational-Tool-Use-Pivot-v1
- 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.  

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🧬 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  

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⚙️ 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)

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📦 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)  

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🚀 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)

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🛠️ 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

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🧪 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  

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📊 Evaluation

No formal benchmark results published yet.

Expected strengths:

* Strong instruction adherence
* Lightweight agentic reasoning
* Efficient local deployment

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📚 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

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📜 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

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🙏 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

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🔗 Links

* Model: https://huggingface.co/WithinUsAI/Nvidia.Agentic.Coder-4B-GGUF
* Organization: https://huggingface.co/WithinUsAI