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
Download tokenizer.json from Diffuspace/Clover-35B-A3B-Preview: direct link, hf CLI and curl.
- Browser
- Download file 20 MB
-
https://huggingface.co/Diffuspace/Clover-35B-A3B-Preview/resolve/main/tokenizer.json
- Command line
-
hf download hf://Diffuspace/Clover-35B-A3B-Preview/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/Diffuspace/Clover-35B-A3B-Preview/resolve/main/tokenizer.json
20 MB
- Xet hash:
- eda1a40bb8ba2fa7bac5745da85d32bff39827698864a3102415a3969a6d81fd
- Size of remote file:
- 20 MB
- SHA256:
- 7c62ba7d8bb13291c94459896cd27537858beb58a88a037b09a150e213420988
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