Text Generation
Transformers
Safetensors
ceno
dna
genomics
msa
variant-effect-prediction
mamba
Mixture of Experts
custom_code
Instructions to use CladeTeam/CENO-P-1B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use CladeTeam/CENO-P-1B with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="CladeTeam/CENO-P-1B", trust_remote_code=True)# Load model directly from transformers import AutoModelForCausalLM model = AutoModelForCausalLM.from_pretrained("CladeTeam/CENO-P-1B", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use CladeTeam/CENO-P-1B with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "CladeTeam/CENO-P-1B" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "CladeTeam/CENO-P-1B", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/CladeTeam/CENO-P-1B
- SGLang
How to use CladeTeam/CENO-P-1B 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 "CladeTeam/CENO-P-1B" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "CladeTeam/CENO-P-1B", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'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 "CladeTeam/CENO-P-1B" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "CladeTeam/CENO-P-1B", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use CladeTeam/CENO-P-1B with Docker Model Runner:
docker model run hf.co/CladeTeam/CENO-P-1B
File size: 1,749 Bytes
7f8b9fb | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 | {
"architectures": [
"CENOPForCausalLM"
],
"attention_bias": false,
"attention_dropout": 0.0,
"attention_head_dim": null,
"bos_token_id": 0,
"chunk_size": 128,
"conv_kernel": 4,
"eos_token_id": 0,
"expand": 2,
"hidden_dropout": 0.0,
"hidden_size": 1024,
"hybrid_override_pattern": "MEMEM*EMEMEM*EMEMEMEMEM*EMEMEM*EMEMEME",
"initializer_range": 0.02,
"intermediate_size": 4096,
"intra_encoding_pattern": "++++---++++---++++--++---++++---++++--",
"layer_norm_epsilon": 1e-05,
"mamba_head_dim": 64,
"mamba_hidden_act": "silu",
"mamba_in_proj_layernorm": true,
"mamba_num_heads": 32,
"mamba_proj_bias": false,
"max_position_embeddings": 1048576,
"mlp_bias": false,
"mlp_fc1_layernorm": false,
"mlp_hidden_act": "relu2",
"mlp_use_swiglu": false,
"model_type": "ceno",
"moe_router_pre_softmax": false,
"moe_top_k": 2,
"n_groups": 8,
"num_attention_heads": 16,
"num_experts": 8,
"num_hidden_layers": 38,
"num_key_value_heads": 16,
"num_logits_to_keep": 1,
"pad_token_id": 1,
"qkv_layernorm": true,
"rescale_prenorm_residual": true,
"residual_in_fp32": false,
"sliding_window": null,
"ssm_state_size": 128,
"tie_word_embeddings": false,
"time_step_floor": 0.0001,
"time_step_limit": [
0.0,
1e30
],
"time_step_max": 0.1,
"time_step_min": 0.001,
"torch_dtype": "float32",
"transformers_version": "4.48.3",
"use_bias": false,
"use_cache": true,
"use_conv_bias": true,
"use_mamba_kernels": true,
"vocab_size": 512,
"auto_map": {
"AutoConfig": "configuration_ceno.CENOConfig",
"AutoModelForCausalLM": "modeling_ceno_p.CENOPForCausalLM",
"AutoTokenizer": [
"ceno_tokenizer.CENOCharLevelTokenizer",
null
]
}
}
|