🧠 Qwen3.5-4B-AgentCoder

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

• Author: Bruno Pistone
• Repository: br1-pist/Qwen3.5-4B-AgentCoder
• License: Apache 2.0
• Base model: Qwen/Qwen3.5-4B

---

Model Description

Qwen3.5-4B-AgentCoder 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 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

from transformers import AutoModelForCausalLM, AutoTokenizer
import torch
model_name = "br1-pist/Qwen3.5-4B-AgentCoder"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
model_name,
torch_dtype="auto",
device_map="auto"
)
prompt = "Give me a short introduction to large language models."
messages = [{"role": "user", "content": prompt}]
text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
generated_ids = model.generate(**model_inputs, max_new_tokens=1024)
output = tokenizer.decode(generated_ids[0], skip_special_tokens=True)
print(output)

---

🧠 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: 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: A Fine-Tuned Model for Enhanced Tool Calling, Code Generation, and Reasoning. Hugging Face Model Hub. https://huggingface.co/br1-pist/Qwen3.5-4B-AgentCoder

---

🧭 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