Instructions to use cerebras/Qwen3-Coder-REAP-25B-A3B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use cerebras/Qwen3-Coder-REAP-25B-A3B with Transformers:

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

pipe = pipeline("text-generation", model="cerebras/Qwen3-Coder-REAP-25B-A3B")
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-25B-A3B")
model = AutoModelForCausalLM.from_pretrained("cerebras/Qwen3-Coder-REAP-25B-A3B")
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 cerebras/Qwen3-Coder-REAP-25B-A3B 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-25B-A3B"
# 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-25B-A3B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/cerebras/Qwen3-Coder-REAP-25B-A3B

SGLang

How to use cerebras/Qwen3-Coder-REAP-25B-A3B 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-25B-A3B" \
    --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-25B-A3B",
		"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-25B-A3B" \
        --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-25B-A3B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use cerebras/Qwen3-Coder-REAP-25B-A3B with Docker Model Runner:
```
docker model run hf.co/cerebras/Qwen3-Coder-REAP-25B-A3B
```

Aider Polyglot Benchmark

by whoisjeremylam - opened Oct 24, 2025

Discussion

whoisjeremylam

Oct 24, 2025

Here are the results of a single pass through the current Aider benchmark. These results are for the non-quant'd as per this repo.

pass_rate_2 seems to be a little lower than the reported rate for the default BF16. In the Aider Discord, they seem to report BF16 to be 32.4.

I'm a newb when it comes to Aider and vllm, so I've also included the command lines I used in case there is something wrong.

Serve:

CUDA_VISIBLE_DEVICES=0,1 \
  vllm serve \
  --port 5000 \
  --gpu-memory-utilization=0.95 \
  --max_model_len=131072 \
  --pipeline-parallel-size 2 \
  --model=/home/ai/models/cerebras/Qwen3-Coder-REAP-25B-A3B \
  --served-model-name Qwen3-Coder-REAP-25B-A3B

Aider:

./benchmark/benchmark.py Qwen3-Coder-REAP-25B-A3B --model openai/Qwen3-Coder-REAP-25B-A3B --edit-format diff --threads 1 --exercises-dir polyglot-benchmark

Results:

- dirname: 2025-10-22-11-21-46--Qwen3-Coder-REAP-25B-A3B
  test_cases: 225
  model: openai/Qwen3-Coder-REAP-25B-A3B
  edit_format: diff
  commit_hash: 11516d6
  pass_rate_1: 12.0
  pass_rate_2: 27.6
  pass_num_1: 27
  pass_num_2: 62
  percent_cases_well_formed: 94.7
  error_outputs: 20
  num_malformed_responses: 19
  num_with_malformed_responses: 12
  user_asks: 114
  lazy_comments: 1
  syntax_errors: 0
  indentation_errors: 0
  exhausted_context_windows: 1
  prompt_tokens: 3341547
  completion_tokens: 549320
  test_timeouts: 5
  total_tests: 225
  command: aider --model openai/Qwen3-Coder-REAP-25B-A3B
  date: 2025-10-22
  versions: 0.86.2.dev
  seconds_per_case: 35.2
  total_cost: 0.0000

Doctor-Chad-PhD

Nov 11, 2025

Interesting, so it did decrease a bit in pass rate. Thank you.

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