Instructions to use ryota39/Qwen3-8B-math-RL-en with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use ryota39/Qwen3-8B-math-RL-en with Transformers:

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

pipe = pipeline("text-generation", model="ryota39/Qwen3-8B-math-RL-en")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("ryota39/Qwen3-8B-math-RL-en")
model = AutoModelForCausalLM.from_pretrained("ryota39/Qwen3-8B-math-RL-en")
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 ryota39/Qwen3-8B-math-RL-en with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "ryota39/Qwen3-8B-math-RL-en"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ryota39/Qwen3-8B-math-RL-en",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/ryota39/Qwen3-8B-math-RL-en

SGLang

How to use ryota39/Qwen3-8B-math-RL-en 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 "ryota39/Qwen3-8B-math-RL-en" \
    --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": "ryota39/Qwen3-8B-math-RL-en",
		"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 "ryota39/Qwen3-8B-math-RL-en" \
        --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": "ryota39/Qwen3-8B-math-RL-en",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use ryota39/Qwen3-8B-math-RL-en with Docker Model Runner:
```
docker model run hf.co/ryota39/Qwen3-8B-math-RL-en
```

Qwen3-8B-math-RL-en

Qwen/Qwen3-8Bを少ないトークン予算の元max_new_tokens=256で、openai/gsm8kを使って事後学習したモデルです。
tinker_cookbookのrl_basic.pyに従い、accuracy_rewardとformat_rewardを最大化するようなモデルの重みを学習しています。
詳細はTinker APIと限られたトークン長でのLLMの強化学習に記載しています。

How to use

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "ryota39/Qwen3-8B-math-RL-en"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype=torch.bfloat16,
    device_map="auto",
)

prompt = "A Christmas tree has 8 shelves for decorations, and each shelf can hold 45 ornaments. Currently, two-thirds of the total capacity is decorated. How many ornaments are on the tree in total? Write your answer in the format \\boxed{answer}."

messages = [{"role": "user", "content": prompt}]
text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
inputs = tokenizer(text, return_tensors="pt").to(model.device)

outputs = model.generate(**inputs, max_new_tokens=256)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[1]:], skip_special_tokens=True))

# 出力例
# <think>
# Okay, let's see. The Christmas tree has 8 shelves, each holding 45 ornaments. So the total capacity is 8 times 45. Let me calculate that: 8*45 is 360. So the total capacity is 360 ornaments. Now, two-thirds of that is decorated. So two-thirds of 360 is (2/3)*360. Let me compute that: 360 divided by 3 is 120, times 2 is 240. So there are 240 ornaments on the tree. The answer is \boxed{240}.
#
# **Final Answer**
# \boxed{240}
# </think>
#
# To determine how many ornaments are currently on the tree, we start by calculating the total capacity of the tree:
#
# - There are 8 shelves, and each shelf holds 45 ornaments.
# - Total capacity = $ 8 \times 45 = 360 $ ornaments.
#
# Next, we are told that two-thirds of this total capacity is currently decorated:
#
# - Two-thirds of 360 = $ \frac{2}{3} \times 360 = 240 $