Text Generation
Transformers
Safetensors
clokcem
causal-lm
customer-care
thinking-head
intent-classification
quality-gate
gqa
swiglu
rmsnorm
rope
indian-languages
Instructions to use clokai/CLOK-CEM with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use clokai/CLOK-CEM with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="clokai/CLOK-CEM")# Load model directly from transformers import AutoModelForCausalLM model = AutoModelForCausalLM.from_pretrained("clokai/CLOK-CEM", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use clokai/CLOK-CEM with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "clokai/CLOK-CEM" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "clokai/CLOK-CEM", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/clokai/CLOK-CEM
- SGLang
How to use clokai/CLOK-CEM 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 "clokai/CLOK-CEM" \ --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": "clokai/CLOK-CEM", "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 "clokai/CLOK-CEM" \ --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": "clokai/CLOK-CEM", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use clokai/CLOK-CEM with Docker Model Runner:
docker model run hf.co/clokai/CLOK-CEM
File size: 616 Bytes
77c2d2e | 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 | {
"vocab_size": 32000,
"model_type": "bpe",
"special_tokens": [
"<|pad|>",
"<|unk|>",
"<|bos|>",
"<|eos|>",
"<|system|>",
"<|user|>",
"<|assistant|>",
"<|thinking|>",
"<|intent|>",
"<|quality|>",
"<|context|>",
"<|end_of_thinking|>"
],
"training_datasets": [
"bitext/Bitext-customer-support-llm-chatbot-training-dataset",
"databricks/databricks-dolly-15k",
"tatsu-lab/alpaca",
"clinc/clinc_oos",
"amazon_polarity",
"ag_news",
"imdb",
"yelp_review_full",
"dgbillings/stackoverflow-questions",
"Open-Orca/OpenOrca"
]
} |