Instructions to use QuantTrio/Qwen3.5-35B-A3B-AWQ with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use QuantTrio/Qwen3.5-35B-A3B-AWQ with Transformers:

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

pipe = pipeline("image-text-to-text", model="QuantTrio/Qwen3.5-35B-A3B-AWQ")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
pipe(text=messages)

# Load model directly
from transformers import AutoProcessor, AutoModelForMultimodalLM

processor = AutoProcessor.from_pretrained("QuantTrio/Qwen3.5-35B-A3B-AWQ")
model = AutoModelForMultimodalLM.from_pretrained("QuantTrio/Qwen3.5-35B-A3B-AWQ")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
inputs = processor.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(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use QuantTrio/Qwen3.5-35B-A3B-AWQ with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "QuantTrio/Qwen3.5-35B-A3B-AWQ"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "QuantTrio/Qwen3.5-35B-A3B-AWQ",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Use Docker

docker model run hf.co/QuantTrio/Qwen3.5-35B-A3B-AWQ

SGLang

How to use QuantTrio/Qwen3.5-35B-A3B-AWQ 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 "QuantTrio/Qwen3.5-35B-A3B-AWQ" \
    --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": "QuantTrio/Qwen3.5-35B-A3B-AWQ",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

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 "QuantTrio/Qwen3.5-35B-A3B-AWQ" \
        --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": "QuantTrio/Qwen3.5-35B-A3B-AWQ",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Docker Model Runner
How to use QuantTrio/Qwen3.5-35B-A3B-AWQ with Docker Model Runner:
```
docker model run hf.co/QuantTrio/Qwen3.5-35B-A3B-AWQ
```

tclf90 commited on Feb 26

Commit

2f4fac0

verified ·

1 Parent(s): 816421b

Update config.json

Browse files

Files changed (1) hide show

config.json +2 -2

config.json CHANGED Viewed

@@ -9,7 +9,7 @@
     "attention_bias": false,
     "attention_dropout": 0.0,
     "attn_output_gate": true,
-    "dtype": "bfloat16",
     "eos_token_id": 248044,
     "full_attention_interval": 4,
     "head_dim": 256,
@@ -96,6 +96,7 @@
   "transformers_version": "4.57.0.dev0",
   "video_token_id": 248057,
   "vision_config": {
     "deepstack_visual_indexes": [],
     "depth": 27,
     "hidden_act": "gelu_pytorch_tanh",
@@ -113,7 +114,6 @@
   },
   "vision_end_token_id": 248054,
   "vision_start_token_id": 248053,
-  "torch_dtype": "bfloat16",
   "quantization_config": {
     "quant_method": "awq",
     "bits": 4,

     "attention_bias": false,
     "attention_dropout": 0.0,
     "attn_output_gate": true,
+    "dtype": "float16",
     "eos_token_id": 248044,
     "full_attention_interval": 4,
     "head_dim": 256,
   "transformers_version": "4.57.0.dev0",
   "video_token_id": 248057,
   "vision_config": {
+    "torch_dtype": "float16",
     "deepstack_visual_indexes": [],
     "depth": 27,
     "hidden_act": "gelu_pytorch_tanh",
   },
   "vision_end_token_id": 248054,
   "vision_start_token_id": 248053,
   "quantization_config": {
     "quant_method": "awq",
     "bits": 4,