---
license: apache-2.0
language:
- en
base_model:
- Qwen/Qwen3-4B
pipeline_tag: text-generation
tags:
- 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)

```bash
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:
```bash
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](https://huggingface.co/Qwen/Qwen3-4B-Thinking-2507)
- **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](https://github.com/sierra-research/tau2-bench/pull/137)
- **Implementation**: [github.com/amity-arac/amity-sigma-tau2-run](https://github.com/amity-arac/amity-sigma-tau2-run)
- **Tau2-Bench**: [github.com/sierra-research/tau2-bench](https://github.com/sierra-research/tau2-bench)

## Citation

```bibtex
@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](https://arxiv.org/abs/2512.24040)
- [Tau2-Bench: A Benchmark for Conversational AI Agents](https://github.com/sierra-research/tau2-bench)
- [Qwen3-4B-Thinking](https://huggingface.co/Qwen/Qwen3-4B-Thinking-2507)

## Contact

For questions or feedback: touchapon@amity.co
