Instructions to use Tiiny/SmallThinker-3B-Preview with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Tiiny/SmallThinker-3B-Preview with Transformers:

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

pipe = pipeline("text-generation", model="Tiiny/SmallThinker-3B-Preview")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Tiiny/SmallThinker-3B-Preview")
model = AutoModelForCausalLM.from_pretrained("Tiiny/SmallThinker-3B-Preview")
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]:]))

Inference
Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Tiiny/SmallThinker-3B-Preview with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Tiiny/SmallThinker-3B-Preview"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Tiiny/SmallThinker-3B-Preview",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Tiiny/SmallThinker-3B-Preview

SGLang

How to use Tiiny/SmallThinker-3B-Preview 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 "Tiiny/SmallThinker-3B-Preview" \
    --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": "Tiiny/SmallThinker-3B-Preview",
		"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 "Tiiny/SmallThinker-3B-Preview" \
        --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": "Tiiny/SmallThinker-3B-Preview",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Tiiny/SmallThinker-3B-Preview with Docker Model Runner:
```
docker model run hf.co/Tiiny/SmallThinker-3B-Preview
```

Yixin Song commited on Dec 31, 2024

Commit

f7d6bfa

verified ·

1 Parent(s): 0f650b4

Update README.md

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@@ -26,6 +26,34 @@ SmallThinker is designed for the following use cases:
 1.  **Edge Deployment:** Its small size makes it ideal for deployment on resource-constrained devices.
 2.  **Draft Model for QwQ-32B-Preview:** SmallThinker can serve as a fast and efficient draft model for the larger QwQ-32B-Preview model. From my test, in llama.cpp we can get 70% speedup (from 40 tokens/s to 70 tokens/s).
 ## Limitations & Disclaimer
 Please be aware of the following limitations:

 1.  **Edge Deployment:** Its small size makes it ideal for deployment on resource-constrained devices.
 2.  **Draft Model for QwQ-32B-Preview:** SmallThinker can serve as a fast and efficient draft model for the larger QwQ-32B-Preview model. From my test, in llama.cpp we can get 70% speedup (from 40 tokens/s to 70 tokens/s).
+## Training Details
+The model was trained using 8 H100 GPUs with a global batch size of 16. The specific configuration is as follows:
+```
+neat_packing: true
+cutoff_len: 16384
+per_device_train_batch_size: 2
+gradient_accumulation_steps: 1
+learning_rate: 1.0e-5
+num_train_epochs: 3
+lr_scheduler_type: cosine
+warmup_ratio: 0.02
+bf16: true
+ddp_timeout: 180000000
+weight_decay: 0.0
+```
+The SFT (Supervised Fine-Tuning) process was conducted in two phases:
+1. First Phase:
+   - Used only the PowerInfer/QWQ-LONGCOT-500K dataset
+   - Trained for 1.5 epochs
+2. Second Phase:
+   - Combined training with PowerInfer/QWQ-LONGCOT-500K and PowerInfer/LONGCOT-Refine datasets
+   - Continued training for an additional 2 epochs
+   -
 ## Limitations & Disclaimer
 Please be aware of the following limitations: