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 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

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:

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