Instructions to use 0xSero/Qwen3.5-264B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use 0xSero/Qwen3.5-264B with Transformers:

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

pipe = pipeline("text-generation", model="0xSero/Qwen3.5-264B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("0xSero/Qwen3.5-264B")
model = AutoModelForCausalLM.from_pretrained("0xSero/Qwen3.5-264B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.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(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use 0xSero/Qwen3.5-264B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "0xSero/Qwen3.5-264B"
# 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/Qwen3.5-264B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/0xSero/Qwen3.5-264B

SGLang

How to use 0xSero/Qwen3.5-264B 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/Qwen3.5-264B" \
    --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/Qwen3.5-264B",
		"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/Qwen3.5-264B" \
        --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/Qwen3.5-264B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use 0xSero/Qwen3.5-264B with Docker Model Runner:
```
docker model run hf.co/0xSero/Qwen3.5-264B
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Support this work → · X · GitHub · REAP paper · Cerebras REAP

Qwen3.5-264B

REAP-pruned Qwen/Qwen3.5-397B-A17B.

At a glance


Base model	Qwen/Qwen3.5-397B-A17B
Format	BF16
Total params	264B
Active / token	—
Experts / layer	336
Layers	60
Hidden size	4096
Context	262,144
On-disk size	527 GB

Which variant should I pick?

Variant	Format	Link
`Qwen3.5-264B` (this)	BF16	link
`Qwen3.5-264B-FP8`	FP8	link
`Qwen3.5-264B-W4A16`	W4A16	link
`Qwen3.5-28B`	BF16	link
`Qwen3.5-35B-EXL3-4bpw`	EXL3-4bpw	link
`Qwen3.5-76B`	BF16	link
`Qwen3.5-76B-GGUF`	GGUF	link
`Qwen3.5-88B`	BF16	link
`Qwen3.5-99B`	BF16	link
`Qwen3.5-99B-GGUF`	GGUF	link

Repository: 0xSero/Qwen3.5-264B
Base model: Qwen/Qwen3.5-397B-A17B
Artifact kind: pruned
Compression ratio: 34%
Prune metric: reap

Details

Maintainer: 0xSero
Organization: Sybil Solutions
Project: REAP PR17
Hub owner: 0xSero
Summary: BF16 REAP-pruned Qwen3.5-397B-A17B with 176 of 512 experts removed per MoE layer, retaining 336 experts per layer, for an estimated 264B total parameters.

Provenance

Observer state: /home/ubuntu/qwen397-full/observer-calibv1/qwen397-pr17-calibv1-23k-16k-observer-state.raw.pt
Detail state: /home/ubuntu/qwen397-full/observer-calibv1/qwen397-pr17-calibv1-23k-16k-detail-state.raw.pt

Benchmarks

No benchmark summary was found.

Custom Stress

No custom stress summary was found.

Usage

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("0xSero/Qwen3.5-264B", trust_remote_code=True)
tokenizer = AutoTokenizer.from_pretrained("0xSero/Qwen3.5-264B", trust_remote_code=True)

License & citation

License inherited from the base model.

@misc{lasby2025reap,
  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},
  year   = {2025}, eprint = {2510.13999}, archivePrefix = {arXiv}
}

Model tree for 0xSero/Qwen3.5-264B

Base model

Qwen/Qwen3.5-397B-A17B

Finetuned

(30)

this model

Quantizations

1 model

Datasets used to train 0xSero/Qwen3.5-264B

Space using 0xSero/Qwen3.5-264B 1

Collection including 0xSero/Qwen3.5-264B

Qwen — REAP

Collection

REAP-pruned & quantized Qwen3.5 / 3.6 / Coder variants. • 15 items • Updated 3 days ago

Paper for 0xSero/Qwen3.5-264B

REAP the Experts: Why Pruning Prevails for One-Shot MoE compression

Paper • 2510.13999 • Published Oct 15, 2025 • 20

0xSero
/

Qwen3.5-264B