Instructions to use RangerX/Qwen3.6-35B-PreREAP-BNB4-Pruned-ratio-0.3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use RangerX/Qwen3.6-35B-PreREAP-BNB4-Pruned-ratio-0.3 with Transformers:

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

pipe = pipeline("image-text-to-text", model="RangerX/Qwen3.6-35B-PreREAP-BNB4-Pruned-ratio-0.3")
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("RangerX/Qwen3.6-35B-PreREAP-BNB4-Pruned-ratio-0.3")
model = AutoModelForMultimodalLM.from_pretrained("RangerX/Qwen3.6-35B-PreREAP-BNB4-Pruned-ratio-0.3")
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 RangerX/Qwen3.6-35B-PreREAP-BNB4-Pruned-ratio-0.3 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "RangerX/Qwen3.6-35B-PreREAP-BNB4-Pruned-ratio-0.3"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "RangerX/Qwen3.6-35B-PreREAP-BNB4-Pruned-ratio-0.3",
		"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/RangerX/Qwen3.6-35B-PreREAP-BNB4-Pruned-ratio-0.3

SGLang

How to use RangerX/Qwen3.6-35B-PreREAP-BNB4-Pruned-ratio-0.3 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 "RangerX/Qwen3.6-35B-PreREAP-BNB4-Pruned-ratio-0.3" \
    --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": "RangerX/Qwen3.6-35B-PreREAP-BNB4-Pruned-ratio-0.3",
		"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 "RangerX/Qwen3.6-35B-PreREAP-BNB4-Pruned-ratio-0.3" \
        --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": "RangerX/Qwen3.6-35B-PreREAP-BNB4-Pruned-ratio-0.3",
		"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 RangerX/Qwen3.6-35B-PreREAP-BNB4-Pruned-ratio-0.3 with Docker Model Runner:
```
docker model run hf.co/RangerX/Qwen3.6-35B-PreREAP-BNB4-Pruned-ratio-0.3
```

Qwen3.6-35B-A3B REAP Pruned Ratio 0.3 with Pre-REAP BNB4 Scoring

This model is derived from Qwen/Qwen3.6-35B-A3B using REAP routed-expert pruning with a pruning ratio of 0.3. Saliency scores were collected from a pre-REAP bitsandbytes 4-bit scoring model, then the original BF16 checkpoint was reloaded, pruned, and saved.

Pruning Setup

Base model: Qwen/Qwen3.6-35B-A3B
Method: REAP routed-expert pruning
Pre-REAP scoring model: bitsandbytes 4-bit NF4, BF16 compute, double quantization enabled
Final checkpoint dtype: saved from the original full-precision/BF16 model after pruning; this is not a quantized checkpoint
Pruning ratio: 0.3
Routed experts before pruning: 256 per MoE layer
Routed experts pruned: 76 per MoE layer
Routed experts retained: 180 per MoE layer
Shared experts: preserved
Calibration samples: 1024
Sequence length: 2048
Seed: 42
Router renormalization: true

Notes

This checkpoint uses the packed Qwen3.5/Qwen3.6 REAP integration. The bnb4 quantized model was used only for saliency score collection; pruning and saving were applied to the original model weights.

Downloads last month: 6

Safetensors

Model size

26B params

Tensor type

BF16

Model tree for RangerX/Qwen3.6-35B-PreREAP-BNB4-Pruned-ratio-0.3

Base model

Qwen/Qwen3.6-35B-A3B

Finetuned

(154)

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