Instructions to use 0xSero/Qwen3.6-28B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use 0xSero/Qwen3.6-28B with Transformers:

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

pipe = pipeline("text-generation", model="0xSero/Qwen3.6-28B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoProcessor, AutoModelForCausalLM

processor = AutoProcessor.from_pretrained("0xSero/Qwen3.6-28B")
model = AutoModelForCausalLM.from_pretrained("0xSero/Qwen3.6-28B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
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 0xSero/Qwen3.6-28B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "0xSero/Qwen3.6-28B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "0xSero/Qwen3.6-28B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/0xSero/Qwen3.6-28B

SGLang

How to use 0xSero/Qwen3.6-28B 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 "0xSero/Qwen3.6-28B" \
    --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": "0xSero/Qwen3.6-28B",
		"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 "0xSero/Qwen3.6-28B" \
        --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": "0xSero/Qwen3.6-28B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use 0xSero/Qwen3.6-28B with Docker Model Runner:
```
docker model run hf.co/0xSero/Qwen3.6-28B
```

0xSero commited on 21 days ago

Commit

c69703c

verified ·

1 Parent(s): 2ba6221

Fix model_type for vLLM compatibility (qwen3_5_moe_text -> qwen3_5_moe)

Browse files

vLLM Qwen3_5MoeConfig is registered against model_type=qwen3_5_moe. Previous value qwen3_5_moe_text instantiated Qwen3_5MoeTextConfig and raised TypeError on load. Reported in discussion #3.

Files changed (1) hide show

config.json +2 -2

config.json CHANGED Viewed

@@ -62,7 +62,7 @@
   "linear_value_head_dim": 128,
   "mamba_ssm_dtype": "float32",
   "max_position_embeddings": 262144,
-  "model_type": "qwen3_5_moe_text",
   "moe_intermediate_size": 512,
   "mtp_num_hidden_layers": 1,
   "mtp_use_dedicated_embeddings": false,
@@ -74,7 +74,7 @@
   "output_router_logits": false,
   "pad_token_id": null,
   "partial_rotary_factor": 0.25,
-  "rms_norm_eps": 1e-06,
   "rope_parameters": {
     "mrope_interleaved": true,
     "mrope_section": [

   "linear_value_head_dim": 128,
   "mamba_ssm_dtype": "float32",
   "max_position_embeddings": 262144,
+  "model_type": "qwen3_5_moe",
   "moe_intermediate_size": 512,
   "mtp_num_hidden_layers": 1,
   "mtp_use_dedicated_embeddings": false,
   "output_router_logits": false,
   "pad_token_id": null,
   "partial_rotary_factor": 0.25,
+  "rms_norm_eps": 0.000001,
   "rope_parameters": {
     "mrope_interleaved": true,
     "mrope_section": [