Qwen3-4B-I-1209

Fine-tuned variant of Qwen3-4B-Instruct-2507, optimized for tool-use and function call generation via reinforcement learning with composite reward signals.

Overview

Base Model	Qwen/Qwen3-4B-Instruct-2507
Training Method	GRPO (Group Relative Policy Optimization)
Specialization	Tool-use, function calling
License	Apache 2.0

Training

Reward Design

The model is trained with three complementary reward functions:

• Rule-based reward — Verifies correctness of function names and arguments. Partial credit is awarded for matching argument subsets.
• Self-certainty reward — Encourages confident, well-calibrated predictions.
• Tool-call reward — Validates structural correctness of generated tool calls.

Configuration

Parameter	Value
Optimizer	AdamW
Learning rate	5e-6
Scheduler	Cosine with min LR (min_lr_rate=0.1)
Generations per prompt	4

Evaluation

ACEBench

Model	Overall Accuracy
Qwen3-4B-I-1209 (this model)	0.7233
Qwen3-4B-Instruct-2507 (base)	0.6350
Salesforce/Llama-xLAM-2-8b-fc-r	0.5792

Additional benchmark results will be added as evaluation continues.

Usage

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("beyoru/Qwen3-4B-I-1209")
tokenizer = AutoTokenizer.from_pretrained("beyoru/Qwen3-4B-I-1209")

Feedback & Contributions

Feedback on model quality, edge cases, and real-world performance is welcome. Open an issue or reach out via the links below.

Citation

@misc{qwen3-4b-i-1209,
  title        = {Qwen3-4B-I-1209: Fine-tuned Qwen3-4B-Instruct with GRPO for Tool-Use and Function Calling},
  author       = {Beyoru},
  year         = {2025},
  howpublished = {\url{https://huggingface.co/beyoru/Qwen3-4B-I-1209}}
}