🧠 Qwen3.5-4B-AgentCoder-Q6-GGUF

A Quantized, Fine-Tuned Model for Enhanced Tool Calling, Code Generation, and Reasoning

• Author: Bruno Pistone
• License: Apache 2.0
• Base model: Qwen/Qwen3.5-4B

Model Description

Qwen3.5-4B-AgentCoder-Q6-K is a fine-tuned version of the Qwen/Qwen3.5-4B model, optimized for: - 🧮 Complex reasoning tasks - 🧰 Tool calling - 💻 Code generation

The model was developed through sequential fine-tuning, followed by a Direct Preference Optimization (DPO) post-training stage to improve alignment, coherence, and reasoning accuracy.

Highlights

• Post-trained with DPO using chosen/rejected pairs for better alignment
• Excellent balance between tool use, code generation, and reasoning

🚀 Direct Use

Qwen3.5-4B-AgentCoder-Q6-K can be used directly for: - ✅ Tool calling in complex reasoning tasks - ✅ Code generation for Python, JS, and other languages - ✅ Multi-domain reasoning (math, logic, Q&A)

⚠️ Out-of-Scope Use

• ❌ Highly sensitive or confidential data
• ❌ Domains requiring expert-level specialization
• ❌ Tasks where full explainability is mandatory

💻 Getting Started

ollama run brnpistone/Qwen3-4B-AgentCoder-q6-k

🧠 Training Details

Training Procedure

Phase 1 — Post-Training - Direct Preference Optimization (DPO)

After sequential fine-tuning, the model underwent a DPO phase to enhance response alignment, reasoning robustness, and factual consistency.

• Learning rate: 3e-6
• Batch size: 1
• Gradient accumulation: 4
• Epochs: 1
• Beta: 0.1
• Loss type: sigmoid
• Warmup steps: 27
• Sequence length: ~2.5K tokens

DPO Data

• ~2.5K chosen/rejected response pairs
• Rejected samples synthetically generated to represent poor or incoherent answers
• Chosen samples tagged from real conversations

Objective - Encourage the model to prefer chosen completions - Improve clarity, correctness, and helpfulness - Reduce hallucinations and verbosity

🖥️ Technical Specifications

Model Architecture

• Model type: Causal language model
  
• Parameters: 4.0B
  
• Context length: ~264K tokens
  
• Thinking mode: Enabled
  

Compute Infrastructure

Hardware - GPU: NVIDIA H100 (80 GB VRAM)
- System RAM: 2 TiB
- Memory per vCPU: 10.67 GiB

Software - Python: 3.12
- Transformers: 5.3.0
- Libraries: bitsandbytes, safetensors, torch, trl, scikit-learn, tokenizers, psutil, py7zr

🧾 Citation

BibTeX

@article{qwen3.5-4b-thinking-2507-toolcode,
  title={Qwen3.5-4B-AgentCoder-Q6-K: A Fine-Tuned Model for Enhanced Tool Calling, Code Generation, and Reasoning},
  author={Bruno Pistone},
  year={2025},
  journal={Hugging Face Model Hub}
}

APA

Bruno Pistone. (2026). Qwen3.5-4B-AgentCoder-Q6-K: A Fine-Tuned Model for Enhanced Tool Calling, Code Generation, and Reasoning.

🧭 Recommendations

• Tool use accuracy depends on task complexity
  
• Code generation may occasionally produce minor syntax issues
  
• Reasoning strongest in structured, logical, and mathematical contexts
  
• Avoid using this model for confidential or safety-critical applications
  

🧠 Qwen3.5-4B-AgentCoder — created by Bruno Pistone
Enhanced reasoning, tool calling, and code generation — refined with DPO alignment