Instructions to use kaushalkrishnax/auroic-router-0.6b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use kaushalkrishnax/auroic-router-0.6b with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="kaushalkrishnax/auroic-router-0.6b")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForMultimodalLM

tokenizer = AutoTokenizer.from_pretrained("kaushalkrishnax/auroic-router-0.6b")
model = AutoModelForMultimodalLM.from_pretrained("kaushalkrishnax/auroic-router-0.6b")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

llama-cpp-python

How to use kaushalkrishnax/auroic-router-0.6b with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="kaushalkrishnax/auroic-router-0.6b",
	filename="gguf/auroic-router-0.6b.f16.gguf",
)

llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": "What is the capital of France?"
		}
	]
)

Inference
Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use kaushalkrishnax/auroic-router-0.6b with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf kaushalkrishnax/auroic-router-0.6b:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf kaushalkrishnax/auroic-router-0.6b:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf kaushalkrishnax/auroic-router-0.6b:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf kaushalkrishnax/auroic-router-0.6b:Q4_K_M

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf kaushalkrishnax/auroic-router-0.6b:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf kaushalkrishnax/auroic-router-0.6b:Q4_K_M

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf kaushalkrishnax/auroic-router-0.6b:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf kaushalkrishnax/auroic-router-0.6b:Q4_K_M

Use Docker

docker model run hf.co/kaushalkrishnax/auroic-router-0.6b:Q4_K_M

LM Studio
Jan

vLLM

How to use kaushalkrishnax/auroic-router-0.6b with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "kaushalkrishnax/auroic-router-0.6b"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "kaushalkrishnax/auroic-router-0.6b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/kaushalkrishnax/auroic-router-0.6b:Q4_K_M

SGLang

How to use kaushalkrishnax/auroic-router-0.6b with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "kaushalkrishnax/auroic-router-0.6b" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "kaushalkrishnax/auroic-router-0.6b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "kaushalkrishnax/auroic-router-0.6b" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "kaushalkrishnax/auroic-router-0.6b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use kaushalkrishnax/auroic-router-0.6b with Ollama:
```
ollama run hf.co/kaushalkrishnax/auroic-router-0.6b:Q4_K_M
```

Unsloth Studio

How to use kaushalkrishnax/auroic-router-0.6b with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for kaushalkrishnax/auroic-router-0.6b to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for kaushalkrishnax/auroic-router-0.6b to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for kaushalkrishnax/auroic-router-0.6b to start chatting

How to use kaushalkrishnax/auroic-router-0.6b with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf kaushalkrishnax/auroic-router-0.6b:Q4_K_M

Configure the model in Pi

# Install Pi:
npm install -g @mariozechner/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "kaushalkrishnax/auroic-router-0.6b:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use kaushalkrishnax/auroic-router-0.6b with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf kaushalkrishnax/auroic-router-0.6b:Q4_K_M

Configure Hermes

# Install Hermes:
curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash
hermes setup
# Point Hermes at the local server:
hermes config set model.provider custom
hermes config set model.base_url http://127.0.0.1:8080/v1
hermes config set model.default kaushalkrishnax/auroic-router-0.6b:Q4_K_M

Run Hermes

hermes

Atomic Chat new
Docker Model Runner
How to use kaushalkrishnax/auroic-router-0.6b with Docker Model Runner:
```
docker model run hf.co/kaushalkrishnax/auroic-router-0.6b:Q4_K_M
```

Lemonade

How to use kaushalkrishnax/auroic-router-0.6b with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull kaushalkrishnax/auroic-router-0.6b:Q4_K_M

Run and chat with the model

lemonade run user.auroic-router-0.6b-Q4_K_M

List all available models

lemonade list

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