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
| { | |
| "architectures": [ | |
| "ClokCEMForCausalLM" | |
| ], | |
| "model_type": "clokcem", | |
| "vocab_size": 32000, | |
| "hidden_size": 1536, | |
| "intermediate_size": 2816, | |
| "num_hidden_layers": 16, | |
| "num_attention_heads": 16, | |
| "num_key_value_heads": 4, | |
| "max_position_embeddings": 1024, | |
| "rms_norm_eps": 1e-06, | |
| "rope_theta": 10000.0, | |
| "tie_word_embeddings": true, | |
| "bos_token_id": 2, | |
| "eos_token_id": 3, | |
| "pad_token_id": 0, | |
| "hidden_act": "silu", | |
| "torch_dtype": "float16", | |
| "transformers_version": "4.40.0", | |
| "use_cache": true, | |
| "thinking_max_steps": 5, | |
| "num_intents": 27, | |
| "sliding_window": 512, | |
| "intent_classes": [ | |
| "order_status", | |
| "return_request", | |
| "refund_status", | |
| "product_info", | |
| "price_inquiry", | |
| "complaint", | |
| "technical_support", | |
| "account_help", | |
| "billing_query", | |
| "cancellation", | |
| "feedback", | |
| "escalation", | |
| "shipping_query", | |
| "payment_issue", | |
| "discount_coupon", | |
| "warranty_claim", | |
| "appointment_booking", | |
| "schedule_change", | |
| "prescription_query", | |
| "insurance_claim", | |
| "loan_inquiry", | |
| "balance_check", | |
| "plan_change", | |
| "network_issue", | |
| "new_connection", | |
| "porting_request", | |
| "general_query" | |
| ] | |
| } |