Instructions to use SmallAICreator/AuroraGPT-Think-v3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use SmallAICreator/AuroraGPT-Think-v3 with Transformers:

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

pipe = pipeline("text-generation", model="SmallAICreator/AuroraGPT-Think-v3")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("SmallAICreator/AuroraGPT-Think-v3")
model = AutoModelForCausalLM.from_pretrained("SmallAICreator/AuroraGPT-Think-v3", device_map="auto")
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]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use SmallAICreator/AuroraGPT-Think-v3 with llama.cpp:

Install (macOS, Linux)

curl -LsSf https://llama.app/install.sh | sh
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf SmallAICreator/AuroraGPT-Think-v3:Q8_0
# Run inference directly in the terminal:
llama cli -hf SmallAICreator/AuroraGPT-Think-v3:Q8_0

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf SmallAICreator/AuroraGPT-Think-v3:Q8_0
# Run inference directly in the terminal:
llama cli -hf SmallAICreator/AuroraGPT-Think-v3:Q8_0

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 SmallAICreator/AuroraGPT-Think-v3:Q8_0
# Run inference directly in the terminal:
./llama-cli -hf SmallAICreator/AuroraGPT-Think-v3:Q8_0

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 SmallAICreator/AuroraGPT-Think-v3:Q8_0
# Run inference directly in the terminal:
./build/bin/llama-cli -hf SmallAICreator/AuroraGPT-Think-v3:Q8_0

Use Docker

docker model run hf.co/SmallAICreator/AuroraGPT-Think-v3:Q8_0

LM Studio
Jan

vLLM

How to use SmallAICreator/AuroraGPT-Think-v3 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "SmallAICreator/AuroraGPT-Think-v3"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "SmallAICreator/AuroraGPT-Think-v3",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/SmallAICreator/AuroraGPT-Think-v3:Q8_0

SGLang

How to use SmallAICreator/AuroraGPT-Think-v3 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 "SmallAICreator/AuroraGPT-Think-v3" \
    --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": "SmallAICreator/AuroraGPT-Think-v3",
		"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 "SmallAICreator/AuroraGPT-Think-v3" \
        --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": "SmallAICreator/AuroraGPT-Think-v3",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use SmallAICreator/AuroraGPT-Think-v3 with Ollama:
```
ollama run hf.co/SmallAICreator/AuroraGPT-Think-v3:Q8_0
```
Unsloth Desktop

How to use SmallAICreator/AuroraGPT-Think-v3 with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf SmallAICreator/AuroraGPT-Think-v3:Q8_0

Configure the model in Pi

# Install Pi:
npm install -g @earendil-works/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "SmallAICreator/AuroraGPT-Think-v3:Q8_0"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use SmallAICreator/AuroraGPT-Think-v3 with Docker Model Runner:
```
docker model run hf.co/SmallAICreator/AuroraGPT-Think-v3:Q8_0
```

Lemonade

How to use SmallAICreator/AuroraGPT-Think-v3 with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull SmallAICreator/AuroraGPT-Think-v3:Q8_0

Run and chat with the model

lemonade run user.AuroraGPT-Think-v3-Q8_0

List all available models

lemonade list

Hermes Agent

How to use SmallAICreator/AuroraGPT-Think-v3 with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf SmallAICreator/AuroraGPT-Think-v3:Q8_0

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 SmallAICreator/AuroraGPT-Think-v3:Q8_0

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use SmallAICreator/AuroraGPT-Think-v3 with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf SmallAICreator/AuroraGPT-Think-v3:Q8_0

Configure OpenClaw

# Install OpenClaw:
npm install -g openclaw@latest
# Register the local server and set it as the default model:
openclaw onboard --non-interactive --mode local \
  --auth-choice custom-api-key \
  --custom-base-url http://127.0.0.1:8080/v1 \
  --custom-model-id "SmallAICreator/AuroraGPT-Think-v3:Q8_0" \
  --custom-provider-id llama-cpp \
  --custom-compatibility openai \
  --custom-text-input \
  --accept-risk \
  --skip-health

Run OpenClaw

openclaw agent --local --agent main --message "Hello from Hugging Face"

AuroraGPT-Think-v3 (700M)

A 707M-parameter model by UltraLabs that reasons inside <think>...</think> on every prompt, trained with full-parameter SFT on top of AuroraGPT-Qwen-Distill.

This card leads with the limitations, because they are large and they are the most useful thing to know before you download 1.4GB.

Honest evaluation

Measured, not estimated:

benchmark	score
GSM8K (40 problems, chat format, greedy)	1/40 = 2.5%
GSM8K — the base model it was trained from	0/40 = 0.0%

It cannot do GSM8K. 2.5% vs 0.0% on 40 problems is inside the noise (±7.7pp); the honest statement is that neither this model nor its base can solve grade-school word problems reliably. On a hand-picked 5-problem probe set it scores 3/5, but those probes match the shapes it was explicitly drilled on, so that number measures training coverage, not capability. Off-distribution, it falls apart.

What actually goes wrong

1. It sets problems up correctly and then fumbles the arithmetic.

17 × 23  →  <think> 17 x 20 = 340, 17 x 3 = 51, 340 + 51 = 411 </think>   (391)
clock    →  <think> 1060 - 855 = 155 </think>                             (205)

The decomposition is right every time. The digits are wrong. This is not fixable with more training data, and the reason is the tokenizer: AuroraGPT's 32k BPE splits numbers into inconsistent multi-digit chunks — 340 → ['3','40'], 391 → ['39','1'], 51 → ['51']. There is no column alignment for the model to learn. Tokenizers that split numbers into single digits make carrying learnable; this one does not, and it is baked into 22.4B tokens of pretraining.

2. On unfamiliar word problems it misreads the question, inventing operations ("16 eggs/day * 3 eggs/day = 48 eggs").

3. A known defect: shallow chat think-blocks leak into reasoning. Forcing 100% think meant synthesising <think> blocks for ~10k chat turns as a restate-and-plan stub. Those stubs are ~18% of the corpus and sometimes replace real reasoning on a math prompt:

<think>
The user asks: A robe takes 2 bolts of blue fiber and half that much white fiber. How
I'll answer directly and keep it concrete, starting from: The robe takes 2 bolts...
</think>

That is a data-design bug, not a scale limit, and it is the first thing to fix in a v4.

What it is actually good at

Always reasons. 100% of replies contain a <think> block. There is no gate to misfire — the previous version trained a 73/27 think/direct split and learned an inverted gate, skipping reasoning on exactly the hardest prompts.
Structurally sound reasoning. It converts clock times correctly, chains syllogisms correctly, and identifies the bat-and-ball trap before answering.
Verifies instead of flailing. It does not fabricate an error to "catch" — an earlier version trained on injected mistakes and learned to revise answers that were already correct (3 apples → 6 → "8"). That behaviour is gone.
Chat feel inherited from the Qwen3-4B-distilled base.

Training

Full-parameter SFT, 2 epochs, sequence-packed, on Kaggle TPU v5e-8 (JAX/Flax + optax, GSPMD sharding): 806 steps, 26.4M tokens, 8.8 minutes, loss 0.6619 → 0.2716.

Corpus: 54,238 conversations, 100% with a think block —

section	share
math (MetaMathQA, GSM8K, Orca-Math, verified-short OpenR1)	44%
chat (SmolTalk + trivial turns)	19%
code (CodeAlpaca task-anchored, MBPP)	13%
targeted drills (distributive multiply, clock, syllogism, algebra traps, multi-step)	25%

All 13,326 procedurally generated answers were independently re-validated in Python (0 mismatches) after an earlier build shipped 96 wrong answers from integer division.

Chat format

<|system|>{system}<|end|><|user|>{user}<|end|><|assistant|>{reply}<|end|>

EOS is <|end|> (id 5). Context length 2048.

from transformers import AutoModelForCausalLM, AutoTokenizer
tok = AutoTokenizer.from_pretrained("SmallAICreator/AuroraGPT-Think-v3")
model = AutoModelForCausalLM.from_pretrained("SmallAICreator/AuroraGPT-Think-v3")
ids = tok.apply_chat_template([{"role": "user", "content": "What is 25% of 120?"}],
                              tokenize=True, add_generation_prompt=True,
                              return_tensors="pt")
print(tok.decode(model.generate(ids, max_new_tokens=200)[0][ids.shape[1]:],
                 skip_special_tokens=True))

On-device

AuroraGPT-Think-v3.Q8_0.gguf (753MB) is included and runs in llama.cpp / any GGUF chat app, with the chat template embedded.

The right way to use it for arithmetic

Don't ask it to compute. The base model has working tool-calling (15/15 valid tool calls across varied system-prompt wordings) and its chat template declares tools. The correct architecture for this model is reason in the think block, delegate the digits to a calculator tool — which sidesteps the tokenizer problem entirely instead of fighting it.

Made by UltraLabs.

Downloads last month: 210

Safetensors

Model size

0.7B params

Tensor type

F16

Model tree for SmallAICreator/AuroraGPT-Think-v3

Base model

SmallAICreator/AuroraGPT-Qwen-Distill

Quantized

(1)

this model