Instructions to use palmfuture/Qwen3.6-35B-A3B-GPTQ-Int4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use palmfuture/Qwen3.6-35B-A3B-GPTQ-Int4 with Transformers:

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

pipe = pipeline("image-text-to-text", model="palmfuture/Qwen3.6-35B-A3B-GPTQ-Int4")
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("palmfuture/Qwen3.6-35B-A3B-GPTQ-Int4")
model = AutoModelForMultimodalLM.from_pretrained("palmfuture/Qwen3.6-35B-A3B-GPTQ-Int4", device_map="auto")
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 palmfuture/Qwen3.6-35B-A3B-GPTQ-Int4 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "palmfuture/Qwen3.6-35B-A3B-GPTQ-Int4"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "palmfuture/Qwen3.6-35B-A3B-GPTQ-Int4",
		"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/palmfuture/Qwen3.6-35B-A3B-GPTQ-Int4

SGLang

How to use palmfuture/Qwen3.6-35B-A3B-GPTQ-Int4 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 "palmfuture/Qwen3.6-35B-A3B-GPTQ-Int4" \
    --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": "palmfuture/Qwen3.6-35B-A3B-GPTQ-Int4",
		"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 "palmfuture/Qwen3.6-35B-A3B-GPTQ-Int4" \
        --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": "palmfuture/Qwen3.6-35B-A3B-GPTQ-Int4",
		"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 palmfuture/Qwen3.6-35B-A3B-GPTQ-Int4 with Docker Model Runner:
```
docker model run hf.co/palmfuture/Qwen3.6-35B-A3B-GPTQ-Int4
```

palmfuture commited on Apr 17

Commit

7e5600f

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1 Parent(s): b1ea1b2

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+---
+license: apache-2.0
+base_model: Qwen/Qwen3.6-35B-A3B
+tags:
+  - qwen3_5_moe
+  - gptq
+  - 4bit
+  - quantized
+library_name: transformers
+pipeline_tag: image-text-to-text
+quantized_by: palmfuture
+---
+# Qwen3.6-35B-A3B-GPTQ-Int4
+GPTQ-Int4 quantization of [Qwen/Qwen3.6-35B-A3B](https://huggingface.co/Qwen/Qwen3.6-35B-A3B).
+Quantization config derived from official [Qwen/Qwen3.5-35B-A3B-GPTQ-Int4](https://huggingface.co/Qwen/Qwen3.5-35B-A3B-GPTQ-Int4). Only MoE expert weights are quantized to INT4 — attention, shared expert, MTP, vision, embeddings, and lm_head remain in bf16.
+| | |
+|---|---|
+| Original Size | 68.97 GB |
+| Quantized Size | 21.17 GB (-68.39%) |
+| Method | GPTQ 4-bit, group_size=128, symmetric |
+| Calibration | WikiText-2, 128 samples |
+| Quantizer | GPTQModel 6.0.3 |
+## Usage
+### SGLang
+```bash
+python -m sglang.launch_server \
+  --model-path palmfuture/Qwen3.6-35B-A3B-GPTQ-Int4 \
+  --port 8000 --tp-size 4 \
+  --mem-fraction-static 0.89 \
+  --context-length 262144 \
+  --reasoning-parser qwen3 \
+  --quantization moe_wna16
+```
+### vLLM
+```bash
+vllm serve palmfuture/Qwen3.6-35B-A3B-GPTQ-Int4 \
+  --port 8000 --tensor-parallel-size 4 \
+  --max-model-len 262144 \
+  --reasoning-parser qwen3
+```
+## Acknowledgments
+- [Qwen Team](https://huggingface.co/Qwen) for the original model and quantization config reference
+- [GPTQModel](https://github.com/modelcloud/gptqmodel) for the quantization toolkit