Instructions to use cerebras/Qwen3-Coder-REAP-363B-A35B-FP8 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use cerebras/Qwen3-Coder-REAP-363B-A35B-FP8 with Transformers:

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

pipe = pipeline("text-generation", model="cerebras/Qwen3-Coder-REAP-363B-A35B-FP8")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("cerebras/Qwen3-Coder-REAP-363B-A35B-FP8")
model = AutoModelForCausalLM.from_pretrained("cerebras/Qwen3-Coder-REAP-363B-A35B-FP8", device_map="auto")
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 Settings

vLLM

How to use cerebras/Qwen3-Coder-REAP-363B-A35B-FP8 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "cerebras/Qwen3-Coder-REAP-363B-A35B-FP8"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "cerebras/Qwen3-Coder-REAP-363B-A35B-FP8",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/cerebras/Qwen3-Coder-REAP-363B-A35B-FP8

SGLang

How to use cerebras/Qwen3-Coder-REAP-363B-A35B-FP8 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 "cerebras/Qwen3-Coder-REAP-363B-A35B-FP8" \
    --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": "cerebras/Qwen3-Coder-REAP-363B-A35B-FP8",
		"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 "cerebras/Qwen3-Coder-REAP-363B-A35B-FP8" \
        --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": "cerebras/Qwen3-Coder-REAP-363B-A35B-FP8",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use cerebras/Qwen3-Coder-REAP-363B-A35B-FP8 with Docker Model Runner:
```
docker model run hf.co/cerebras/Qwen3-Coder-REAP-363B-A35B-FP8
```

Qwen3 235B 2507 Please

by Cleanable - opened Jan 9

Discussion

Cleanable

Jan 9

Dear Sirs,
Do you plan to release a 25% REAP of the qwen3 235B-2507 (not the VL) please?
Thank you very much for the great work
Best regards

Upload images, audio, and videos by dragging in the text input, pasting, or clicking here.

Tap or paste here to upload images

· Sign up or log in to comment