WithinUsAI/Llama3.2-Agent.Hermes.Coder-3B-gguf

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

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🧬 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)

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🧠 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

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⚙️ 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)

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📦 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. 

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🚀 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

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🛠️ 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

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🧪 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

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📊 Expected Performance Profile

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

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📜 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

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🙏 Acknowledgements

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

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🔗 Links

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

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🧩 Closing Note

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

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