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
Download config.json from Vi-LAB/KenT-300M-Base: direct link, hf CLI and curl.
- Browser
- Download file 669 Bytes
-
https://huggingface.co/Vi-LAB/KenT-300M-Base/resolve/main/config.json
- Command line
-
hf download hf://Vi-LAB/KenT-300M-Base/config.json
-
curl -L -o config.json https://huggingface.co/Vi-LAB/KenT-300M-Base/resolve/main/config.json
669 Bytes
| { | |
| "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 | |
| } | |