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

Libraries

How to use ArchiveStudio/Qwen3-Coder-Next-Base with Transformers:

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

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

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("ArchiveStudio/Qwen3-Coder-Next-Base")
model = AutoModelForCausalLM.from_pretrained("ArchiveStudio/Qwen3-Coder-Next-Base", 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=256)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use ArchiveStudio/Qwen3-Coder-Next-Base with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/ArchiveStudio/Qwen3-Coder-Next-Base

SGLang

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

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

Qwen3-Coder-Next-Base

File size: 2,747 Bytes

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---
library_name: transformers
license: apache-2.0
license_link: https://huggingface.co/Qwen/Qwen3-Coder-Next-Base/blob/main/LICENSE
pipeline_tag: text-generation
---

# Qwen3-Coder-Next-Base

## Highlights

Today, we're announcing **Qwen3-Coder-Next-Base**, an open-weight language model designed specifically for coding agents and local development. It features the following key enhancements:  

- **Advanced architecture**: It integrates the Hybrid Attention with highly sparse MoE, enabling high throughput and strong ultra-long-context modeling.

- **Robust data foundation**: Trained on highly diverse, broad-coverage corpora, with native 256K context and support for 370+ languages, it leaves ample headroom for post-training.

- **Agentic coding capability**: With a carefully designed training recipe, it has strong capabilities in tool calling, scaffold/template adaptation, and error detection/recovery, making it a strong backbone for reliable coding agents.

## Model Overview

**Qwen3-Coder-Next-Base** has the following features:
- Type: Causal Language Models
- Training Stage: Pretraining
- Number of Parameters: 80B in total and 3B activated
- Number of Parameters (Non-Embedding): 79B
- Hidden Dimension: 2048
- Number of Layers: 48
  - Hybrid Layout: 12 \* (3 \* (Gated DeltaNet -> MoE) -> 1 \* (Gated Attention -> MoE))
- Gated Attention:
  - Number of Attention Heads: 16 for Q and 2 for KV
  - Head Dimension: 256
  - Rotary Position Embedding Dimension: 64
- Gated DeltaNet:
  - Number of Linear Attention Heads: 32 for V and 16 for QK
  - Head Dimension: 128
- Mixture of Experts:
  - Number of Experts: 512
  - Number of Activated Experts: 10
  - Number of Shared Experts: 1
  - Expert Intermediate Dimension: 512
- Context Length: 262,144 natively

**NOTE: This model supports only non-thinking mode and does not generate ``<think></think>`` blocks in its output. Meanwhile, specifying `enable_thinking=False` is no longer required.**

For more details, including benchmark evaluation, hardware requirements, and inference performance, please refer to our [blog](https://qwen.ai/blog?id=qwen3-coder-next), [GitHub](https://github.com/QwenLM/Qwen3-Coder), and [Documentation](https://qwen.readthedocs.io/en/latest/).

## Best Practices

To achieve optimal performance, we recommend the following sampling parameters: `temperature=1.0`, `top_p=0.95`, `top_k=40`.


## Citation

If you find our work helpful, feel free to give us a cite.

```
@techreport{qwen_qwen3_coder_next_tech_report,
  title        = {Qwen3-Coder-Next Technical Report},
  author       = {{Qwen Team}},
  url          = {https://github.com/QwenLM/Qwen3-Coder/blob/main/qwen3_coder_next_tech_report.pdf},
  note         = {Accessed: 2026-02-03}
}
```