license, language, base_model, pipeline_tag, tags

license	language	base_model	pipeline_tag	tags
apache-2.0	en	Qwen/Qwen3-4B	text-generation	conversational agent customer-service tool-use reasoning tau2-bench

Amity Sigma Thinking v3r

A 4-billion parameter model fine-tuned for agentic customer service tasks, achieving 78.51% Pass@1 on the Tau2-Bench retail domain - outperforming models 50-400x its size.

Model Highlights

Metric	Value
Parameters	4B
Tau2-Bench Retail Pass@1	78.51%
Base Model	Qwen3-4B-Thinking
Training	ROAD + GRPO Fine-tuning

Performance Comparison

Tau2-Bench Retail Domain Pass@1 scores:

Model	Size	Retail Pass@1
Claude Opus 4	~200B	81.4%
Claude Sonnet 4	~70-100B	80.5%
Amity Sigma v3r	4B	78.51%
Qwen3-Max	~340-407B	75.44%
GPT-4.1	~1.7T	74.0%
Claude-3.7-Sonnet	~50-70B	72.1%
o4-mini	~25-50B	68.3%
GPT-4.1-mini	~8-20B	61.4%

Key Achievements

• Outperforms GPT-4.1 (1.7T parameters) by 4.5 percentage points
• Outperforms Qwen3-Max (340-407B parameters) by 3.1 percentage points
• Only 2.9% behind Claude Opus 4, which is approximately 50x larger

Full Tau2-Bench Results

Domain	Pass@1	Pass@2	Pass@3	Pass@4
Retail	78.51%	67.40%	60.53%	56.14%
Airline	55.50%	45.00%	38.50%	34.00%
Telecom	32.89%	24.71%	19.96%	16.67%

Usage

vLLM Serving (Recommended)

vllm serve amityco/amity-sigma-thinking-v3r \
    --reasoning-parser deepseek_r1 \
    --enable-auto-tool-choice \
    --tool-call-parser hermes \
    --max-model-len 45000 \
    --trust-remote-code

For multi-GPU setups:

vllm serve amityco/amity-sigma-thinking-v3r \
    --reasoning-parser deepseek_r1 \
    --enable-auto-tool-choice \
    --tool-call-parser hermes \
    --max-model-len 45000 \
    --trust-remote-code \
    --tensor-parallel-size 8

Hardware Requirements

Setup	GPU Requirements	Notes
Minimum	1x A100 40GB	Full precision
Alternative	2x RTX 4090	Consumer GPUs
Recommended	1x A100 80GB	Best performance

Training Methodology

Qwen3-4B-Thinking (Base)
    ↓
ROAD Optimization (Policy Enhancement)
    ↓
Synthetic Data Generation (Amity Sigma)
    ↓
Human-in-the-Loop Feedback
    ↓
GRPO Fine-tuning
    ↓
Amity Sigma v3r

Training Details

• Base Model: Qwen3-4B-Thinking
• Optimization: ROAD (Auto Optimization for Agentic Tasks)
• Fine-tuning: GRPO on multi-turn interaction data
• Data: Synthetic customer service conversations with human-in-the-loop feedback

Cost Efficiency

Estimated Cost per 1,000 Customer Interactions

Model	Estimated Cost	Comparison
Amity Sigma v3r (self-hosted)	~$1-2	Baseline
Claude Sonnet 4	~$6-10	4-6x more
GPT-4.1	~$15-25	10-15x more
Claude Opus 4	~$30-50	20-30x more

Self-Hosting Benefits

• Run on modest hardware (single A100 or 2x RTX 4090)
• Predictable costs without per-token pricing
• Data privacy - no external API calls
• Low latency with local inference

Intended Use

This model is designed for:

• Customer service automation
• Order management and support
• Multi-turn conversational agents
• Tool-calling and function execution

Limitations

• Optimized primarily for retail domain; airline and telecom performance is lower
• Requires vLLM with specific configuration for optimal tool-calling
• Best suited for English language interactions

Links

• Tau2-Bench Submission: PR #137
• Implementation: github.com/amity-arac/amity-sigma-tau2-run
• Tau2-Bench: github.com/sierra-research/tau2-bench

Citation

@misc{amity-sigma-v3r,
  title={Amity Sigma Thinking v3r: Efficient Agentic Model for Customer Service},
  author={Amity},
  year={2026},
  url={https://huggingface.co/amityco/amity-sigma-thinking-v3r}
}

References

• ROAD: Auto Optimization for Agentic Tasks
• Tau2-Bench: A Benchmark for Conversational AI Agents
• Qwen3-4B-Thinking

Contact

For questions or feedback: touchapon@amity.co