OpenThinker-32B-Unverified

Instructions to use ryanmarten/OpenThinker-32B-Unverified with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use ryanmarten/OpenThinker-32B-Unverified with Transformers:

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

pipe = pipeline("text-generation", model="ryanmarten/OpenThinker-32B-Unverified")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("ryanmarten/OpenThinker-32B-Unverified")
model = AutoModelForCausalLM.from_pretrained("ryanmarten/OpenThinker-32B-Unverified")
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 ryanmarten/OpenThinker-32B-Unverified with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "ryanmarten/OpenThinker-32B-Unverified"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ryanmarten/OpenThinker-32B-Unverified",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/ryanmarten/OpenThinker-32B-Unverified

SGLang

How to use ryanmarten/OpenThinker-32B-Unverified 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 "ryanmarten/OpenThinker-32B-Unverified" \
    --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": "ryanmarten/OpenThinker-32B-Unverified",
		"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 "ryanmarten/OpenThinker-32B-Unverified" \
        --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": "ryanmarten/OpenThinker-32B-Unverified",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use ryanmarten/OpenThinker-32B-Unverified with Docker Model Runner:
```
docker model run hf.co/ryanmarten/OpenThinker-32B-Unverified
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

OpenThinker-32B-Unverified

This model is a fine-tuned version of Qwen/Qwen2.5-32B-Instruct on the OpenThoughts-Unverified-173k dataset.

Please see OpenThinker-32B for more information.

Model Name	Dataset size	AIME24 I/II	AIME25 I	MATH500	GPQA Diamond	LCBv2
OpenThinker-7B	114k	31.3	30.7	84.4	38.9	41.8
OpenThinker-7B-Unverified	173k	34	29.33	83	39.4	43.8
OpenThinker-32B	114k	66.7	53.3	90.6	61.6	68.9
OpenThinker-32B-Unverified	173k	60.7	44	90	60.6	69.2

Intended uses & limitations

Apache 2.0

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 1e-05
train_batch_size: 1
eval_batch_size: 8
seed: 42
distributed_type: multi-GPU
num_devices: 384
total_train_batch_size: 384
total_eval_batch_size: 3072
optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
lr_scheduler_type: cosine
lr_scheduler_warmup_ratio: 0.1
num_epochs: 3.0

Training results

Framework versions

Transformers 4.46.1
Pytorch 2.5.0a0+b465a5843b.nv24.09
Datasets 3.0.2
Tokenizers 0.20.3

Downloads last month: 13

Safetensors

Model size

33B params

Tensor type

BF16

Model tree for ryanmarten/OpenThinker-32B-Unverified

Base model

Qwen/Qwen2.5-32B

Finetuned

Qwen/Qwen2.5-32B-Instruct

Finetuned

(1217)

this model

Quantizations

3 models