Text Generation
Transformers
Safetensors
clover_moe
clover
mixture-of-experts
diffusion
custom-code
conversational
custom_code
Instructions to use Diffuspace/Clover-35B-A3B-Preview with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
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
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Download ATTRIBUTIONS.md from Diffuspace/Clover-35B-A3B-Preview: direct link, hf CLI and curl.
- Browser
- Download file 2.11 kB
-
https://huggingface.co/Diffuspace/Clover-35B-A3B-Preview/resolve/main/ATTRIBUTIONS.md
- Command line
-
hf download hf://Diffuspace/Clover-35B-A3B-Preview/ATTRIBUTIONS.md
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curl -L -o ATTRIBUTIONS.md https://huggingface.co/Diffuspace/Clover-35B-A3B-Preview/resolve/main/ATTRIBUTIONS.md
2.11 kB
| # Clover upstream attribution and modifications | |
| Clover is derived from [Qwen3.6-35B-A3B](https://huggingface.co/Qwen/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. | |