---
datasets:
- OpenCoder-LLM/opc-sft-stage1
- OpenCoder-LLM/opc-sft-stage2
- microsoft/orca-agentinstruct-1M-v1
- microsoft/orca-math-word-problems-200k
- NousResearch/hermes-function-calling-v1
- AI-MO/NuminaMath-CoT
- AI-MO/NuminaMath-TIR
- allenai/tulu-3-sft-mixture
- cognitivecomputations/dolphin-coder
- HuggingFaceTB/smoltalk
- cognitivecomputations/samantha-data
- m-a-p/CodeFeedback-Filtered-Instruction
- m-a-p/Code-Feedback

---
Llama3.2-Agent.Hermes.Coder-3B (GGUF)

📌 Model Overview

Model Name: WithinUsAI/Llama3.2-Agent.Hermes.Coder-3B-gguf
Organization: Within Us AI
Base Model: NousResearch/Hermes-3-Llama-3.2-3B
Architecture: LLaMA 3.2 (3B) + Hermes 3 fine-tuning
Format: GGUF (quantized for local inference)
Primary Focus: Agentic coding + structured reasoning

This model is a Hermes-enhanced LLaMA 3.2 coder, optimized for agent workflows, structured outputs, and high-control instruction following in a compact 3B footprint.

It blends:

* LLaMA 3.2’s strong foundation
* Hermes 3’s alignment + tool-use intelligence
* WithinUsAI’s agentic coding focus

⸻

🧬 Architecture & Lineage

Base Stack

* Foundation: LLaMA 3.2 (3B parameter class)
* Fine-Tune: Hermes 3 (Nous Research)
* Conversion: GGUF via llama.cpp toolchain

Hermes 3 is known for:

* Strong instruction-following
* Multi-turn conversation stability
* Tool-use and function-calling capabilities
* Improved reasoning and controllability  

What WithinUsAI Adds

This variant emphasizes:

* Coding-first behavior
* Agentic task execution
* Structured outputs (JSON, functions, steps)

⸻

🧠 Core Design Philosophy

This model operates like a disciplined junior engineer with a systems mindset 🧩💻

Not just generating code…
but thinking in steps, outputs, and actions.

Design Goals:

* High controllability (Hermes-style alignment)
* Strong coding bias
* Agent compatibility
* Efficient local deployment

⸻

⚙️ Key Capabilities

💻 Coding

* Python, JavaScript, C++, and more
* Function generation and refactoring
* Debugging and structured fixes

🤖 Agentic Behavior

* Task decomposition
* Step-by-step execution planning
* Function calling / tool-use readiness

🧠 Reasoning

* Chain-of-thought style outputs
* Logical breakdown of problems
* Instruction precision

📦 Structured Output

* JSON generation
* Schema-following responses
* Deterministic formatting (strong Hermes trait)

⸻

📦 GGUF Format & Deployment

Optimized for local inference and edge environments.

Supported Runtimes:

* llama.cpp
* LM Studio
* Ollama (GGUF-compatible builds)

Typical Quantizations (3B):

Quant	Size	Notes
Q4_K_M	~2.0 GB	Best balance
Q5_K_M	~2.3 GB	Higher quality
Q8_0	~3.4 GB	Maximum fidelity

Quantization enables large size reduction while maintaining usable performance, making local deployment practical.  

⸻

🚀 Intended Use

✅ Ideal Use Cases

* Local coding assistants
* Agent frameworks (tool-calling pipelines)
* Structured output systems (JSON APIs)
* Autonomous coding workflows
* Offline developer copilots

⚠️ Limitations

* 3B size limits deep reasoning vs larger models
* Requires good prompt structure for best results
* Tool execution must be handled externally

⸻

🛠️ Usage Example (llama.cpp)

./main -m Llama3.2-Agent.Hermes.Coder-3B.Q4_K_M.gguf \
  -p "Create a JSON schema and Python validator for user authentication." \
  -n 512

⸻

🧪 Training & Methodology

Within Us AI pipeline emphasizes:

* Instruction-tuned coding datasets
* Agentic workflow examples
* Structured output training
* Evaluation-driven refinement

Data Sources

* Proprietary Within Us AI datasets
* Third-party datasets (no ownership claimed)
* Focus areas:
    * Code reasoning
    * Tool usage patterns
    * Step-by-step problem solving

⸻

📊 Expected Performance Profile

Capability	Strength
Coding	High
Instruction following	Very High
Structured output	Very High
Reasoning depth	Moderate
Efficiency	Very High

⸻

📜 License

License Type: LLaMA 3 / Hermes 3 compatible licensing (inherits base restrictions)**

Attribution Notes:

* Base model: Meta (LLaMA 3.2)
* Fine-tune: Nous Research (Hermes 3)
* GGUF + optimization + methodology: Within Us AI
* Third-party datasets used without ownership claims
* Credit belongs to original creators

⸻

🙏 Acknowledgements

* Meta (LLaMA 3 architecture)
* Nous Research (Hermes 3 fine-tuning)
* GGUF / llama.cpp ecosystem
* Open-source AI community

⸻

🔗 Links

* Model: https://huggingface.co/WithinUsAI/Llama3.2-Agent.Hermes.Coder-3B-gguf
* Organization: https://huggingface.co/WithinUsAI

⸻

🧩 Closing Note

This model feels like a precision tool in a small chassis ⚙️

It doesn’t just answer…
it organizes, structures, and executes.