---
base_model: unsloth/Qwen3-0.6B
library_name: transformers
pipeline_tag: text-generation
tags:
  - router
  - intent-classification
  - hinglish
  - hindi
  - qwen
  - qwen3
  - unsloth
  - gguf
  - ollama
  - edge
  - thinking
license: apache-2.0
language:
  - en
  - hi
---

# Auroic Router 0.6B

A compact, edge-deployable group chat routing model fine-tuned on Qwen3-0.6B. Given a 5-message history window and up to 3 unprocessed candidate messages, it outputs a single structured routing decision in under 4 seconds on CPU — no response generation, pure intent classification.

Part of the [Auroic](https://github.com/kaushalkrishnax/auroic) project — a personal AI assistant for Indian group and personal chats.

---

## What it does

The router sits at the front of a conversational AI pipeline. It reads the recent chat context and decides what action to take next — so downstream models only activate when actually needed.

```
H1: bhai sun
H2: kya hua bata
H3: okay
H4: arre
H5: haan bol
C1: ...
C2: ...
C3: bhai parents divorce ho raha hai adjust karna mushkil hai kya karoon

→ R: TYPE=text | TARGET=C3 | EFFORT=high
```

---

## Output Format

```
R: TYPE=text   | TARGET=C2 | EFFORT=low|medium|high
R: TYPE=react  | TARGET=C1 | TITLE=🔥
R: TYPE=media  | TARGET=C3 | TITLE=rain cozy vibes
R: TYPE=ignore
```

| Type | When | Fields |
|------|------|--------|
| `text` | Help request, advice, emotional support, technical question | `EFFORT=low/medium/high` |
| `react` | Notable moment deserving an emoji reaction | `TITLE=<emoji>` |
| `media` | Situation implying a gif, meme, or sticker | `TITLE=<2-3 word search query>` |
| `ignore` | Pure noise, spam, keyboard smash, laughter chains | none |

---

## Input Format

```
H1: <message>   ← oldest processed message
H2: <message>
H3: <message>
H4: <message>
H5: <message>   ← most recent processed message
C1: <message>   ← unprocessed candidate (use ... for filler)
C2: <message>
C3: <message>   ← newest unprocessed candidate
```

Always provide exactly 5 history messages and 3 candidates. Use `...` for empty slots — never omit them.

---

## Quickstart — Ollama

```bash
ollama create auroic-router-0.6b -f Modelfile
ollama run auroic-router-0.6b
```

**Important — the router is stateless.** Every call must be a fresh context with no conversation history. In the Ollama CLI use `/clear` between calls. In production call the API directly:

```python
response = client.chat.completions.create(
    model="auroic-router-0.6b",
    messages=[
        {"role": "system", "content": "You are the Auroic Router. Given history messages H1-H5 and candidate messages C1-C3, output exactly one routing decision."},
        {"role": "user", "content": formatted_window}  # fresh window every call
    ],
)
```

---

## Real Inference Examples

```
INPUT:
H1: lol  H2: 😂😂  H3: haha  H4: 💀  H5: ded
C1: hahaha  C2: 😭😭  C3: ...

OUTPUT: R: TYPE=ignore
```

```
INPUT:
H1: sahi hai  H2: haan bhai  H3: okay  H4: chal  H5: theek hai
C1: ...
C2: bhai salary negotiate kaise karoon first time offer mein help
C3: ...

OUTPUT: R: TYPE=text | TARGET=C2 | EFFORT=medium
```

```
INPUT:
H1: bhai hungry hun  H2: same yaar  H3: kuch khate hain  H4: haan  H5: chal
C1: ...  C2: ...
C3: bhai biryani ki yaad aa rahi hai mummy wali ghar ki bahut miss kar raha

OUTPUT: R: TYPE=media | TARGET=C3 | TITLE=biryani craving
```

```
INPUT:
H1: haha  H2: bhai sach mein  H3: lol  H4: no way  H5: 💀
C1: ...
C2: teacher ne galti se apna tiktok projector pe chala diya class mein
C3: ...

OUTPUT: R: TYPE=media | TARGET=C2 | TITLE=tiktok projector fail
```

```
INPUT:
H1: bhai sun  H2: haan  H3: bata  H4: okay  H5: hmm
C1: ...
C2: @BOT yaar dost ne 3 baje uthke help kiya exam ke liye true friendship hai
C3: ...

OUTPUT: R: TYPE=react | TARGET=C2 | TITLE=❤
```

---

## Thinking Mode

The model uses Qwen3's native thinking capability. For ambiguous inputs it reasons through the context before deciding:

```
<think>
H1-H5 are casual affirmations. C2 asks 'salary negotiate kaise karoon first time' —
clear, actionable life advice needed. Text with medium effort because it requires
step-by-step guidance, not just emojis or media.
</think>
R: TYPE=text | TARGET=C2 | EFFORT=medium
```

For obvious cases (clear ignore, clear media) it skips thinking entirely and outputs the decision directly — keeping latency low where reasoning isn't needed.

**Thinking enabled (default):** better accuracy on ambiguous cases, ~3-4s on CPU

**Thinking disabled:** faster, minor accuracy drop on edge cases

To disable thinking add this to your Modelfile template:
```
<|im_start|>assistant
<think>

</think>
```

---

## Recommended Inference Settings

```
temperature     0.3    (deterministic routing)
top_p           0.95
top_k           20
repeat_penalty  1.1
```

---

## Model Details

| Property | Value |
|----------|-------|
| Base model | unsloth/Qwen3-0.6B |
| Total parameters | 616M |
| Trainable parameters | 20.2M (3.28%) |
| Fine-tuning framework | Unsloth + TRL SFTTrainer |
| LoRA rank | r=32, alpha=32 |
| Target modules | q/k/v/o/gate/up/down proj |
| Epochs | 2 |
| Effective batch size | 16 (batch=2, grad_accum=8) |
| Learning rate | 2e-4 cosine |
| Max sequence length | 2048 |
| Training samples | 9,300 |
| Final training loss | 0.667 |
| Quantization | Q8_0 GGUF |
| Hardware trained on | NVIDIA T4 16GB (Google Colab) |
| Training time | ~44 minutes |

---

## Dataset — v4

9,300 samples of Indian group chat routing scenarios:

| Split | Count |
|-------|-------|
| Normal windows | 7,000 |
| @BOT mention windows | 1,500 |
| Filler/sparse windows | 800 |

| Type | Count | % |
|------|-------|---|
| text | 4,042 | 43.5% |
| ignore | 1,942 | 20.9% |
| react | 1,688 | 18.2% |
| media | 1,628 | 17.5% |

Language distribution: 58.8% Hinglish, 23% English, 18.2% mixed

Think blocks annotated by `qwen/qwen3-next-80b-a3b-instruct` on NVIDIA NIM — hard tier (39.3%) gets full reasoning, medium tier (32.8%) gets short reasoning, easy tier (27.8%) gets no think block.

Dataset and generation pipeline: [github.com/kaushalkrishnax/auroic-router](https://github.com/kaushalkrishnax/auroic-router)

---

## Performance

| Metric | Value |
|--------|-------|
| Warm inference (CPU) | ~2-3 seconds |
| Cold start | ~5 seconds |
| Hardware tested | Intel i3-6100U 2.3GHz, 12GB DDR4 |
| Format compliance | 100% on test set |

---

## Known Limitations

- Optimized for Hinglish and Indian English group chat patterns
- Stateless by design — do not pass conversation history between calls
- Not designed for factual QA, coding assistance, or long-form generation

---

## License

Apache-2.0 — inherits from Qwen3 base model.

---

## Author

**Kaushal Krishna**
- GitHub: [kaushalkrishnax](https://github.com/kaushalkrishnax)
- Project: [github.com/kaushalkrishnax/auroic](https://github.com/kaushalkrishnax/auroic)
- Router research: [github.com/kaushalkrishnax/auroic-router](https://github.com/kaushalkrishnax/auroic-router)
