Instructions to use vito95311/Qwen3-Omni-30B-A3B-Thinking-INT8FP16 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use vito95311/Qwen3-Omni-30B-A3B-Thinking-INT8FP16 with Transformers:

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

pipe = pipeline("text-generation", model="vito95311/Qwen3-Omni-30B-A3B-Thinking-INT8FP16")

# Load model directly
from transformers import AutoProcessor, AutoModelForMultimodalLM

processor = AutoProcessor.from_pretrained("vito95311/Qwen3-Omni-30B-A3B-Thinking-INT8FP16")
model = AutoModelForMultimodalLM.from_pretrained("vito95311/Qwen3-Omni-30B-A3B-Thinking-INT8FP16")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use vito95311/Qwen3-Omni-30B-A3B-Thinking-INT8FP16 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "vito95311/Qwen3-Omni-30B-A3B-Thinking-INT8FP16"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "vito95311/Qwen3-Omni-30B-A3B-Thinking-INT8FP16",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/vito95311/Qwen3-Omni-30B-A3B-Thinking-INT8FP16

SGLang

How to use vito95311/Qwen3-Omni-30B-A3B-Thinking-INT8FP16 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 "vito95311/Qwen3-Omni-30B-A3B-Thinking-INT8FP16" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "vito95311/Qwen3-Omni-30B-A3B-Thinking-INT8FP16",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

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 "vito95311/Qwen3-Omni-30B-A3B-Thinking-INT8FP16" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "vito95311/Qwen3-Omni-30B-A3B-Thinking-INT8FP16",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use vito95311/Qwen3-Omni-30B-A3B-Thinking-INT8FP16 with Docker Model Runner:
```
docker model run hf.co/vito95311/Qwen3-Omni-30B-A3B-Thinking-INT8FP16
```

vito95311 commited on Sep 24, 2025

Commit

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Update README.md

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@@ -9,7 +9,6 @@ tags:
 - text-generation
 - multimodal
 - quantized
-- gguf
 - ollama
 - llama-cpp
 - qwen
@@ -29,7 +28,7 @@ model-index:
       value: 25.3
 library_name: llama.cpp
 base_model:
-  - Qwen/Qwen3-Omni-30B-A3B-Thinking
 ---
 # 🔥 Qwen3-Omni **量化版本** - 智能GPU/CPU混合推理

 - text-generation
 - multimodal
 - quantized
 - ollama
 - llama-cpp
 - qwen
       value: 25.3
 library_name: llama.cpp
 base_model:
+- Qwen/Qwen3-Omni-30B-A3B-Thinking
 ---
 # 🔥 Qwen3-Omni **量化版本** - 智能GPU/CPU混合推理