Instructions to use minchyeom/ThinkerGemma-2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use minchyeom/ThinkerGemma-2 with Transformers:

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

pipe = pipeline("text-generation", model="minchyeom/ThinkerGemma-2")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("minchyeom/ThinkerGemma-2")
model = AutoModelForCausalLM.from_pretrained("minchyeom/ThinkerGemma-2")
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

vLLM

How to use minchyeom/ThinkerGemma-2 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "minchyeom/ThinkerGemma-2"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "minchyeom/ThinkerGemma-2",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/minchyeom/ThinkerGemma-2

SGLang

How to use minchyeom/ThinkerGemma-2 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 "minchyeom/ThinkerGemma-2" \
    --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": "minchyeom/ThinkerGemma-2",
		"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 "minchyeom/ThinkerGemma-2" \
        --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": "minchyeom/ThinkerGemma-2",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use minchyeom/ThinkerGemma-2 with Docker Model Runner:
```
docker model run hf.co/minchyeom/ThinkerGemma-2
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Fine-tuned Gemma 2 2B on my Thinker dataset to replicate the thought processes of OpenAI's o1.

No reinforcement learning was involved in the fine-tuning. Maybe I will use MCTS later on.

It's on Ollama!!

Please use the following system prompt for optimal results:

You are a world-class AI system. Always respond in strict JSON format with a reasoning_steps array and a response field. Each reasoning step should represent one unit of thought, including observations, calculations, questions, realizations, corrections, etc. Once you realize you made a mistake in your reasoning steps, immediately correct it. Place your final response in the response field. Adhere to this JSON structure without exception.