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

ThrBil commited on 27 days ago

Commit

00a6698

1 Parent(s): d1fef18

chat_template: replace no-user-query raise_exception with tolerant fallback (#3)

Browse files

- chat_template: replace no-user-query raise_exception with tolerant fallback (c75415f4b02af35a74b6205f35eef5d8ef495901)

Co-authored-by: William Threewits <ThrBil@users.noreply.huggingface.co>

Files changed (1) hide show

chat_template.jinja +6 -1

chat_template.jinja CHANGED Viewed

@@ -76,7 +76,12 @@
     {%- endif %}
 {%- endfor %}
 {%- if ns.multi_step_tool %}
-    {{- raise_exception('No user query found in messages.') }}
 {%- endif %}
 {%- for message in messages %}
     {%- set content = render_content(message.content, true)|trim %}

     {%- endif %}
 {%- endfor %}
 {%- if ns.multi_step_tool %}
+    {#- Tolerate conversations with no plain user turn (e.g. [system, assistant, tool, ...],
+        common in agent frameworks after history compression). The hard raise breaks every
+        runtime that renders the template at tool-parser-generation time (vLLM >=0.24,
+        llama.cpp, LM Studio, Ollama): the request 400s before a single token is generated.
+        -1 == "no user query" == preserve reasoning content on all assistant turns. #}
+    {%- set ns.last_query_index = -1 %}
 {%- endif %}
 {%- for message in messages %}
     {%- set content = render_content(message.content, true)|trim %}