Instructions to use Vili-Elvis01/Qwen3.8-Max with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Vili-Elvis01/Qwen3.8-Max with Transformers:

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

pipe = pipeline("text-generation", model="Vili-Elvis01/Qwen3.8-Max")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("Vili-Elvis01/Qwen3.8-Max", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Vili-Elvis01/Qwen3.8-Max with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Vili-Elvis01/Qwen3.8-Max"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Vili-Elvis01/Qwen3.8-Max",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Vili-Elvis01/Qwen3.8-Max

SGLang

How to use Vili-Elvis01/Qwen3.8-Max 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 "Vili-Elvis01/Qwen3.8-Max" \
    --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": "Vili-Elvis01/Qwen3.8-Max",
		"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 "Vili-Elvis01/Qwen3.8-Max" \
        --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": "Vili-Elvis01/Qwen3.8-Max",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Vili-Elvis01/Qwen3.8-Max with Docker Model Runner:
```
docker model run hf.co/Vili-Elvis01/Qwen3.8-Max
```

Qwen3.8-Max

This repository contains the open-weight version of Qwen3.8-Max, officially released as Qwen3.8-2.4T-A95B.

Model Overview

Architecture: Sparse Mixture-of-Experts (MoE)
Total Parameters: 2.4 Trillion
Active Parameters: 95 Billion per token
Experts: 512 total (10 routed + 1 shared activated per token)
Context Length: 262,144 tokens natively (extensible up to ~1M tokens)
Type: Causal Language Model
Modalities: Text → Text (open weights version)
License: Qwen3.8-Max License

Note: The official hosted version of Qwen3.8-Max (available via Qwen Cloud / Alibaba Cloud) includes additional features such as native vision/video input, non-thinking mode, and built-in tools. The open-weight version focuses on text generation with strong reasoning capabilities.

Model Details

Property	Value
Model Type	Causal LM (MoE)
Total Parameters	2.4T
Activated Parameters	95B
Hidden Size	8192
Number of Layers	92
Vocabulary Size	248,320
Native Context Length	262,144
Max Context (with scaling)	~1,010,000

Usage

With Transformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "Vili-Elvis01/Qwen3.8-Max"

tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype="auto",
    device_map="auto"
)

prompt = "Explain quantum computing in simple terms."
messages = [
    {"role": "user", "content": prompt}
]

text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True
)

model_inputs = tokenizer([text], return_tensors="pt").to(model.device)

generated_ids = model.generate(
    **model_inputs,
    max_new_tokens=512
)

response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
print(response)

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