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 config.json from Diffuspace/Clover-35B-A3B-Preview: direct link, hf CLI and curl.
- Browser
- Download file 2.58 kB
-
https://huggingface.co/Diffuspace/Clover-35B-A3B-Preview/resolve/main/config.json
- Command line
-
hf download hf://Diffuspace/Clover-35B-A3B-Preview/config.json
-
curl -L -o config.json https://huggingface.co/Diffuspace/Clover-35B-A3B-Preview/resolve/main/config.json
2.58 kB
| { | |
| "architectures": [ | |
| "CloverMoeModelLM" | |
| ], | |
| "auto_map": { | |
| "AutoConfig": "configuration_clover_moe.CloverMoeConfig", | |
| "AutoModel": "modeling_clover_moe.CloverMoeModel", | |
| "AutoModelForCausalLM": "modeling_clover_moe.CloverMoeModelLM" | |
| }, | |
| "attention_bias": false, | |
| "attention_dropout": 0.0, | |
| "attn_output_gate": true, | |
| "block_size": 32, | |
| "bos_token_id": 248044, | |
| "dtype": "bfloat16", | |
| "eos_token_id": 248044, | |
| "full_attention_interval": 4, | |
| "head_dim": 256, | |
| "hidden_act": "silu", | |
| "hidden_size": 2048, | |
| "initializer_range": 0.02, | |
| "layer_types": [ | |
| "linear_attention", | |
| "linear_attention", | |
| "linear_attention", | |
| "full_attention", | |
| "linear_attention", | |
| "linear_attention", | |
| "linear_attention", | |
| "full_attention", | |
| "linear_attention", | |
| "linear_attention", | |
| "linear_attention", | |
| "full_attention", | |
| "linear_attention", | |
| "linear_attention", | |
| "linear_attention", | |
| "full_attention", | |
| "linear_attention", | |
| "linear_attention", | |
| "linear_attention", | |
| "full_attention", | |
| "linear_attention", | |
| "linear_attention", | |
| "linear_attention", | |
| "full_attention", | |
| "linear_attention", | |
| "linear_attention", | |
| "linear_attention", | |
| "full_attention", | |
| "linear_attention", | |
| "linear_attention", | |
| "linear_attention", | |
| "full_attention", | |
| "linear_attention", | |
| "linear_attention", | |
| "linear_attention", | |
| "full_attention", | |
| "linear_attention", | |
| "linear_attention", | |
| "linear_attention", | |
| "full_attention" | |
| ], | |
| "linear_conv_kernel_dim": 4, | |
| "linear_key_head_dim": 128, | |
| "linear_num_key_heads": 16, | |
| "linear_num_value_heads": 32, | |
| "linear_value_head_dim": 128, | |
| "mamba_ssm_dtype": "float32", | |
| "mask_token_id": 151669, | |
| "max_position_embeddings": 131072, | |
| "model_type": "clover_moe", | |
| "moe_block_coreset_enabled": true, | |
| "moe_block_coreset_top_p": 0.9, | |
| "moe_intermediate_size": 512, | |
| "num_attention_heads": 16, | |
| "num_experts": 256, | |
| "num_experts_per_tok": 8, | |
| "num_hidden_layers": 40, | |
| "num_key_value_heads": 2, | |
| "output_router_logits": false, | |
| "pad_token_id": null, | |
| "partial_rotary_factor": 0.25, | |
| "rms_norm_eps": 1e-06, | |
| "rope_parameters": { | |
| "partial_rotary_factor": 0.25, | |
| "rope_theta": 10000000, | |
| "rope_type": "default" | |
| }, | |
| "shared_expert_intermediate_size": 512, | |
| "tie_word_embeddings": false, | |
| "use_cache": true, | |
| "vocab_size": 248320, | |
| "transformers_version": "5.19.0", | |
| "num_shared_experts": 1, | |
| "attention_backend": "sdpa", | |
| "denoising_config": { | |
| "algorithm": "low_confidence", | |
| "threshold": 0.95 | |
| } | |
| } | |