Instructions to use Wladastic/Mini-Think-Base-1B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Wladastic/Mini-Think-Base-1B with Transformers:

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

pipe = pipeline("text-generation", model="Wladastic/Mini-Think-Base-1B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForMultimodalLM

tokenizer = AutoTokenizer.from_pretrained("Wladastic/Mini-Think-Base-1B")
model = AutoModelForMultimodalLM.from_pretrained("Wladastic/Mini-Think-Base-1B")
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 Wladastic/Mini-Think-Base-1B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Wladastic/Mini-Think-Base-1B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Wladastic/Mini-Think-Base-1B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Wladastic/Mini-Think-Base-1B

SGLang

How to use Wladastic/Mini-Think-Base-1B 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 "Wladastic/Mini-Think-Base-1B" \
    --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": "Wladastic/Mini-Think-Base-1B",
		"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 "Wladastic/Mini-Think-Base-1B" \
        --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": "Wladastic/Mini-Think-Base-1B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Wladastic/Mini-Think-Base-1B with Docker Model Runner:
```
docker model run hf.co/Wladastic/Mini-Think-Base-1B
```

Wladastic commited on Feb 18, 2025

Commit

86460e3

verified ·

1 Parent(s): a846975

Update README.md

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@@ -11,10 +11,10 @@ tags:
   - llama
   - think
 ---
 ![image/png](https://cdn-uploads.huggingface.co/production/uploads/646ba0d4c7f672003c851ed2/rsr_FSCzYXN5OTf5UrvCU.png)
-# MiniThink-1B-base
 MiniThink-1B is an experiment to reproduce the "Aha!" moment in AI.
 Is is trained using a modified version of the method used in the [Unsloth R1 training blog](https://unsloth.ai/blog/r1-reasoning) and the [notebook provided for training LLama 3.1 8B to learn R1 reasoning ](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Llama3.1_(8B)-GRPO.ipynb).

   - llama
   - think
 ---
+# MiniThink-1B-base
 ![image/png](https://cdn-uploads.huggingface.co/production/uploads/646ba0d4c7f672003c851ed2/rsr_FSCzYXN5OTf5UrvCU.png)
 MiniThink-1B is an experiment to reproduce the "Aha!" moment in AI.
 Is is trained using a modified version of the method used in the [Unsloth R1 training blog](https://unsloth.ai/blog/r1-reasoning) and the [notebook provided for training LLama 3.1 8B to learn R1 reasoning ](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Llama3.1_(8B)-GRPO.ipynb).