Instructions to use bullpoint/Qwen3-Coder-Next-AWQ-4bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use bullpoint/Qwen3-Coder-Next-AWQ-4bit with Transformers:

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

pipe = pipeline("text-generation", model="bullpoint/Qwen3-Coder-Next-AWQ-4bit")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("bullpoint/Qwen3-Coder-Next-AWQ-4bit")
model = AutoModelForCausalLM.from_pretrained("bullpoint/Qwen3-Coder-Next-AWQ-4bit")
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 bullpoint/Qwen3-Coder-Next-AWQ-4bit with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "bullpoint/Qwen3-Coder-Next-AWQ-4bit"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "bullpoint/Qwen3-Coder-Next-AWQ-4bit",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/bullpoint/Qwen3-Coder-Next-AWQ-4bit

SGLang

How to use bullpoint/Qwen3-Coder-Next-AWQ-4bit 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 "bullpoint/Qwen3-Coder-Next-AWQ-4bit" \
    --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": "bullpoint/Qwen3-Coder-Next-AWQ-4bit",
		"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 "bullpoint/Qwen3-Coder-Next-AWQ-4bit" \
        --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": "bullpoint/Qwen3-Coder-Next-AWQ-4bit",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use bullpoint/Qwen3-Coder-Next-AWQ-4bit with Docker Model Runner:
```
docker model run hf.co/bullpoint/Qwen3-Coder-Next-AWQ-4bit
```

Works great! Thannk you!

by Max-NexaEthos - opened Feb 4

Discussion

Max-NexaEthos

Feb 4

I'm testing it now on a G292-Z20 with 8x A2000 12GB, tensor parallel 8 and 131072 context lenght deliver up to 50 tps in generation, very nice local coding model for even some level of complexity (Rust Dioxus), it can manage complex tasks quite nicely!

Thanks!

Arthurandhavefun

Feb 4

Yeah, the same. THX guy.!

itsmebcc

Feb 4

I'm testing it now on a G292-Z20 with 8x A2000 12GB, tensor parallel 8 and 131072 context lenght deliver up to 50 tps in generation, very nice local coding model for even some level of complexity (Rust Dioxus), it can manage complex tasks quite nicely!

Thanks!

I notice it seems to do a lot of something in the background. When it is outputting text it is fast, and even PP is fast, but it sits and processes something for a long time before outputting text. This is the same issue I noticed with the last "next" model they put out. You mind posting your serve command?

droussis

Feb 12

•

edited Feb 12

@itsmebcc

This is probably a model-specific issue with vLLM. Qwen3 Coder Next (and the earlier Next model) can't use prefix-caching. You can verify it with a long conversation; see if the model gets progressively slower.

The issue for the original model:
https://github.com/vllm-project/vllm/issues/25874

Potential fix for the Coder Next model as well here?
https://github.com/vllm-project/vllm/pull/26807

Please let me know if it works, I think it may still be unsolved.

bullpoint changed discussion status to closed Mar 11

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