Instructions to use Diffuspace/Clover-35B-A3B-Preview with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Diffuspace/Clover-35B-A3B-Preview with Transformers:

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

pipe = pipeline("text-generation", model="Diffuspace/Clover-35B-A3B-Preview", trust_remote_code=True)
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("Diffuspace/Clover-35B-A3B-Preview", trust_remote_code=True, device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Diffuspace/Clover-35B-A3B-Preview with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/Diffuspace/Clover-35B-A3B-Preview

SGLang

How to use Diffuspace/Clover-35B-A3B-Preview 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 "Diffuspace/Clover-35B-A3B-Preview" \
    --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": "Diffuspace/Clover-35B-A3B-Preview",
		"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 "Diffuspace/Clover-35B-A3B-Preview" \
        --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": "Diffuspace/Clover-35B-A3B-Preview",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Diffuspace/Clover-35B-A3B-Preview with Docker Model Runner:
```
docker model run hf.co/Diffuspace/Clover-35B-A3B-Preview
```

Clover-35B-A3B-Preview / ATTRIBUTIONS.md

Sansa

ZiruiWu

Add Clover-35B-A3B-Preview release

4b0402b about 13 hours ago

preview code

Raw History Blame Contribute Delete

2.11 kB

Clover upstream attribution and modifications

Clover is derived from Qwen3.6-35B-A3B. Upstream model assets: Copyright 2026 Alibaba Cloud. See the accompanying LICENSE for the upstream Apache License, Version 2.0, including its copyright statement.

Modified upstream assets distributed with Clover:

config.json: text-only Clover MoE architecture, ordinary RoPE, removal of vision/MTP and training settings, a 128K context limit, and custom Hugging Face inference metadata.
generation_config.json: Clover generation defaults, repetition and length controls, and version metadata.
tokenizer.json and tokenizer_config.json: token 151669 remapped to <|mask|>, affected BPE merges removed, special-token metadata updated, and multimodal processor configuration removed; context limit set to 128K. Other vocabulary IDs are preserved.
chat_template.jinja: text-only content and simplified inference rendering; the file carries its own source and modification notice.
Exported checkpoint shards and index: vision and MTP tensors removed and text tensor names normalized by Clover's conversion scripts.

modeling_clover_moe.py derives from the Hugging Face Transformers Qwen3.5-MoE implementation used for the Qwen3.6 architecture. Its upstream copyright is Copyright 2025 The Qwen Team and The HuggingFace Inc. team. All rights reserved. Clover adds block-denoising generation, fixed GDN preview and clean-state caching. The source URL and modification notice are retained in that file. Clover also aligns MoE accumulation and causal convolution activation with its SGLang serving implementation, while keeping the standalone PyTorch causal recurrence.

The right-convolution and block-size-corrected softmax GDN operations in modeling_clover_moe.py are the Clover authors' own PyTorch implementation.

sampling_clover_moe.py implements response-only penalties and confidence handling to match the SGLang Clover LowConfidence decoding semantics.

These attributions describe provenance and do not imply upstream endorsement.