Instructions to use RangerX/Qwen3.6-35B-REAP-Pruned-ratio-0.5 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use RangerX/Qwen3.6-35B-REAP-Pruned-ratio-0.5 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-REAP-Pruned-ratio-0.5")
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-REAP-Pruned-ratio-0.5")
model = AutoModelForMultimodalLM.from_pretrained("RangerX/Qwen3.6-35B-REAP-Pruned-ratio-0.5")
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-REAP-Pruned-ratio-0.5 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-REAP-Pruned-ratio-0.5"
# 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-REAP-Pruned-ratio-0.5",
		"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-REAP-Pruned-ratio-0.5

SGLang

How to use RangerX/Qwen3.6-35B-REAP-Pruned-ratio-0.5 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-REAP-Pruned-ratio-0.5" \
    --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-REAP-Pruned-ratio-0.5",
		"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-REAP-Pruned-ratio-0.5" \
        --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-REAP-Pruned-ratio-0.5",
		"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-REAP-Pruned-ratio-0.5 with Docker Model Runner:
```
docker model run hf.co/RangerX/Qwen3.6-35B-REAP-Pruned-ratio-0.5
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Qwen3.6-35B-A3B REAP Pruned Ratio 0.5

This repository contains a REAP-pruned version of Qwen/Qwen3.6-35B-A3B. The checkpoint was produced with routed-expert pruning using REAP (Router-weighted Expert Activation Pruning), which scores routed experts with router weights and expert activation norms.

Pruning Settings

Setting	Value
Base model	`Qwen/Qwen3.6-35B-A3B`
Compression / pruning ratio	`0.50`
Pruning method	`reap`
Calibration samples	`1024`
Calibration sequence length	`2048`
Seed	`42`
Router weight renormalization	`true`
Routed experts per MoE layer	`256 -> 128`
Routed experts selected per token	`8`
Shared experts	Preserved
Precision	BF16
Quantization	None

Calibration Data

The calibration set used the REAP paper/code mixture with 1024 total samples:

theblackcat102/evol-codealpaca-v1: 171 samples
Salesforce/xlam-function-calling-60k: 171 samples
open-r1/Mixture-of-Thoughts[code]: 171 samples
open-r1/Mixture-of-Thoughts[math]: 171 samples
open-r1/Mixture-of-Thoughts[science]: 170 samples
SWE-bench/SWE-smith-trajectories(tool): 170 samples

Integration Notes

This checkpoint was generated with packed Qwen3.5/Qwen3.6 REAP support. The packed routed expert tensors and router rows were sliced while preserving the shared expert and the vision-language configuration. The saved model uses the Transformers qwen3_5_moe architecture and includes tokenizer and processor files.

Citation

@inproceedings{
    lasby2026reap,
    title={{REAP} the Experts: Why Pruning Prevails for One-Shot MoE compression},
    author={Mike Lasby and Ivan Lazarevich and Nish Sinnadurai and Sean Lie and Yani Ioannou and Vithursan Thangarasa},
    booktitle={The Fourteenth International Conference on Learning Representations},
    year={2026},
    url={https://openreview.net/forum?id=ukGxWd2aDG}
}

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Safetensors

Model size

19B params

Tensor type

BF16

Model tree for RangerX/Qwen3.6-35B-REAP-Pruned-ratio-0.5

Base model

Qwen/Qwen3.6-35B-A3B

Finetuned

(141)

this model

Quantizations

3 models