Instructions to use bknyaz/Qwen3-Coder-Next-REAM with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use bknyaz/Qwen3-Coder-Next-REAM with Transformers:

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

pipe = pipeline("text-generation", model="bknyaz/Qwen3-Coder-Next-REAM")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("bknyaz/Qwen3-Coder-Next-REAM")
model = AutoModelForCausalLM.from_pretrained("bknyaz/Qwen3-Coder-Next-REAM")
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 bknyaz/Qwen3-Coder-Next-REAM with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/bknyaz/Qwen3-Coder-Next-REAM

SGLang

How to use bknyaz/Qwen3-Coder-Next-REAM 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 "bknyaz/Qwen3-Coder-Next-REAM" \
    --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": "bknyaz/Qwen3-Coder-Next-REAM",
		"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 "bknyaz/Qwen3-Coder-Next-REAM" \
        --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": "bknyaz/Qwen3-Coder-Next-REAM",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use bknyaz/Qwen3-Coder-Next-REAM with Docker Model Runner:
```
docker model run hf.co/bknyaz/Qwen3-Coder-Next-REAM
```

Please add quality Q6/Q4/Q3 quants to this

by TomLucidor - opened Feb 12

Discussion

TomLucidor

Feb 12

•

edited Feb 12

I kinda think running this with lower RAM is possible, but not sure if the quality loss would be too dramatic. And REAP is known to be a bit sucky
(Also instead of GGUF format as some have made it, SafeTensor for MLX and vLLM-esque platforms)

meganoob1337

Feb 12

Awq or gptq would be nice :D

meganoob1337

Feb 12

@cpatonn would you be so kind to create an awq Quant of this model? <3

cpatonn

Feb 13

On it, no worries!

meganoob1337

Feb 13

Thank you <3 been trying it myself but it didn't work out yet :D

TomLucidor

Feb 13

•

edited Feb 13

@cpatonn could you also quant some Nemotron-H and Granite-4.0-H? (and maybe Jet-Nemotron-2B / Nemotron-Flash-3B-Instruct / Jet-Nemotron-4B / Nemotron-H-4B-Instruct-128K cus the quantizer on MLX-Community sometimes won't work with SSM/Linear)

cpatonn

Feb 15

Yeah sure! I do have Granite-4.0-H models quantized, but the current vllm implementation is not compatible with compressed-tensors INT4 quants.

Are you interested in MLX quants? As I am considering to make MLX quants in the future.

TomLucidor

Feb 15

@cpatonn Please do both "regular" (dynamic or UD or whatever between Q3 and Q6) vLLM quants AND MLX quants. We need sub-4B models and sub-8B linear attention models to get more popular

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