Instructions to use splats/Qwen3.6-35B-A3B-Kimi-K2.6-Reasoning-Distilled-oQ8e-fp16 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use splats/Qwen3.6-35B-A3B-Kimi-K2.6-Reasoning-Distilled-oQ8e-fp16 with Transformers:

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

pipe = pipeline("text-generation", model="splats/Qwen3.6-35B-A3B-Kimi-K2.6-Reasoning-Distilled-oQ8e-fp16")
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("splats/Qwen3.6-35B-A3B-Kimi-K2.6-Reasoning-Distilled-oQ8e-fp16")
model = AutoModelForMultimodalLM.from_pretrained("splats/Qwen3.6-35B-A3B-Kimi-K2.6-Reasoning-Distilled-oQ8e-fp16")
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 splats/Qwen3.6-35B-A3B-Kimi-K2.6-Reasoning-Distilled-oQ8e-fp16 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "splats/Qwen3.6-35B-A3B-Kimi-K2.6-Reasoning-Distilled-oQ8e-fp16"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "splats/Qwen3.6-35B-A3B-Kimi-K2.6-Reasoning-Distilled-oQ8e-fp16",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/splats/Qwen3.6-35B-A3B-Kimi-K2.6-Reasoning-Distilled-oQ8e-fp16

SGLang

How to use splats/Qwen3.6-35B-A3B-Kimi-K2.6-Reasoning-Distilled-oQ8e-fp16 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 "splats/Qwen3.6-35B-A3B-Kimi-K2.6-Reasoning-Distilled-oQ8e-fp16" \
    --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": "splats/Qwen3.6-35B-A3B-Kimi-K2.6-Reasoning-Distilled-oQ8e-fp16",
		"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 "splats/Qwen3.6-35B-A3B-Kimi-K2.6-Reasoning-Distilled-oQ8e-fp16" \
        --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": "splats/Qwen3.6-35B-A3B-Kimi-K2.6-Reasoning-Distilled-oQ8e-fp16",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use splats/Qwen3.6-35B-A3B-Kimi-K2.6-Reasoning-Distilled-oQ8e-fp16 with Docker Model Runner:
```
docker model run hf.co/splats/Qwen3.6-35B-A3B-Kimi-K2.6-Reasoning-Distilled-oQ8e-fp16
```

Qwen3.6-35B-A3B-Kimi-K2.6-Reasoning-Distilled-oQ8e-fp16

This model was quantized using oQ (oMLX v0.3.8) mixed-precision quantization.

Quantization details

Model type: qwen3_5_moe
Bits: 8
Group size: 64
Format: MLX safetensors

Downloads last month: 78

Safetensors

Model size

10B params

Tensor type

F16

U32

Model tree for splats/Qwen3.6-35B-A3B-Kimi-K2.6-Reasoning-Distilled-oQ8e-fp16

Base model

Qwen/Qwen3.6-35B-A3B

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lordx64/Qwen3.6-35B-A3B-Kimi-K2.6-Reasoning-Distilled

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