Text Generation
Transformers
Safetensors
Vietnamese
English
kent
ssm
state-space-model
recurrent
linear-time
frequency-domain
dct
swiglu
custom_code
fp16
base
Instructions to use Vi-LAB/KenT-300M-Base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Vi-LAB/KenT-300M-Base with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="Vi-LAB/KenT-300M-Base", trust_remote_code=True)# pip install -U transformers accelerate # Load model directly from transformers import AutoModelForCausalLM model = AutoModelForCausalLM.from_pretrained("Vi-LAB/KenT-300M-Base", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use Vi-LAB/KenT-300M-Base with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Vi-LAB/KenT-300M-Base" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Vi-LAB/KenT-300M-Base", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/Vi-LAB/KenT-300M-Base
- SGLang
How to use Vi-LAB/KenT-300M-Base 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 "Vi-LAB/KenT-300M-Base" \ --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": "Vi-LAB/KenT-300M-Base", "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 "Vi-LAB/KenT-300M-Base" \ --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": "Vi-LAB/KenT-300M-Base", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use Vi-LAB/KenT-300M-Base with Docker Model Runner:
docker model run hf.co/Vi-LAB/KenT-300M-Base
File size: 669 Bytes
5462185 ebfe05c 5462185 99eb3d1 d8e30ac 5462185 ebfe05c 99eb3d1 | 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 | {
"model_type": "kent",
"architectures": [
"KentForCausalLM"
],
"auto_map": {
"AutoConfig": "model.KentConfig",
"AutoModel": "model.KentModel",
"AutoModelForCausalLM": "model.KentForCausalLM"
},
"vocab_size": 50257,
"hidden_size": 512,
"num_hidden_layers": 24,
"resolution_size": 8,
"feature_dim": 512,
"intermediate_size": 2048,
"rms_norm_eps": 1e-05,
"use_bias": true,
"initializer_range": 0.02,
"use_cache": true,
"tie_word_embeddings": true,
"pad_token_id": 50256,
"bos_token_id": 50256,
"eos_token_id": 50256,
"dtype": "float16",
"ema_alpha_first": 0.5,
"ema_alpha_last": 0.01,
"ema_block_size": 128
}
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