Hugging Face's logo Hugging Face
  • Models
  • Datasets
  • Spaces
  • Buckets new
  • Docs
  • Enterprise
  • Pricing
    • Website
      • Tasks
      • HuggingChat
      • Collections
      • Languages
      • Organizations
    • Community
      • Blog
      • Posts
      • Daily Papers
      • Hardware
      • Learn
      • Discord
      • Forum
      • GitHub
    • Solutions
      • Team & Enterprise
      • Hugging Face PRO
      • Enterprise Support
      • Inference Providers
      • Inference Endpoints
      • Storage Buckets

  • Log In
  • Sign Up

KATHIR2006
/
Zenthi-AI

Text Generation
Transformers
Safetensors
PyTorch
English
qwen2
custom-slm
conversational
agentic-router
qlora
chroma-rag
local-ai
vision
multimodal
code-generation
vlm
vqa
ocr
text-generation-inference
Model card Files Files and versions
xet
Community

Instructions to use KATHIR2006/Zenthi-AI with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • Transformers

    How to use KATHIR2006/Zenthi-AI with Transformers:

    # Use a pipeline as a high-level helper
    from transformers import pipeline
    
    pipe = pipeline("text-generation", model="KATHIR2006/Zenthi-AI")
    messages = [
        {"role": "user", "content": "Who are you?"},
    ]
    pipe(messages)
    # Load model directly
    from transformers import AutoTokenizer, AutoModelForCausalLM
    
    tokenizer = AutoTokenizer.from_pretrained("KATHIR2006/Zenthi-AI")
    model = AutoModelForCausalLM.from_pretrained("KATHIR2006/Zenthi-AI")
    messages = [
        {"role": "user", "content": "Who are you?"},
    ]
    inputs = tokenizer.apply_chat_template(
    	messages,
    	add_generation_prompt=True,
    	tokenize=True,
    	return_dict=True,
    	return_tensors="pt",
    ).to(model.device)
    
    outputs = model.generate(**inputs, max_new_tokens=40)
    print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))
  • Notebooks
  • Google Colab
  • Kaggle
  • Local Apps Settings
  • vLLM

    How to use KATHIR2006/Zenthi-AI with vLLM:

    Install from pip and serve model
    # Install vLLM from pip:
    pip install vllm
    # Start the vLLM server:
    vllm serve "KATHIR2006/Zenthi-AI"
    # Call the server using curl (OpenAI-compatible API):
    curl -X POST "http://localhost:8000/v1/chat/completions" \
    	-H "Content-Type: application/json" \
    	--data '{
    		"model": "KATHIR2006/Zenthi-AI",
    		"messages": [
    			{
    				"role": "user",
    				"content": "What is the capital of France?"
    			}
    		]
    	}'
    Use Docker
    docker model run hf.co/KATHIR2006/Zenthi-AI
  • SGLang

    How to use KATHIR2006/Zenthi-AI 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 "KATHIR2006/Zenthi-AI" \
        --host 0.0.0.0 \
        --port 30000
    # Call the server using curl (OpenAI-compatible API):
    curl -X POST "http://localhost:30000/v1/chat/completions" \
    	-H "Content-Type: application/json" \
    	--data '{
    		"model": "KATHIR2006/Zenthi-AI",
    		"messages": [
    			{
    				"role": "user",
    				"content": "What is the capital of France?"
    			}
    		]
    	}'
    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 "KATHIR2006/Zenthi-AI" \
            --host 0.0.0.0 \
            --port 30000
    # Call the server using curl (OpenAI-compatible API):
    curl -X POST "http://localhost:30000/v1/chat/completions" \
    	-H "Content-Type: application/json" \
    	--data '{
    		"model": "KATHIR2006/Zenthi-AI",
    		"messages": [
    			{
    				"role": "user",
    				"content": "What is the capital of France?"
    			}
    		]
    	}'
  • Docker Model Runner

    How to use KATHIR2006/Zenthi-AI with Docker Model Runner:

    docker model run hf.co/KATHIR2006/Zenthi-AI

You need to agree to share your contact information to access this model

This repository is publicly accessible, but you have to accept the conditions to access its files and content.

Log in or Sign Up to review the conditions and access this model content.

Gated model
You can list files but not access them

Preview of files found in this repository
  • README.md
    5.21 kB
    Upload folder using huggingface_hub 10 days ago
  • adapter_config.json
    1.16 kB
    Upload folder using huggingface_hub 10 days ago
  • adapter_model.safetensors
    84.4 MB
    xet
    Upload folder using huggingface_hub 10 days ago
  • chat_template.jinja
    404 Bytes
    Upload folder using huggingface_hub 10 days ago
  • processor_config.json
    617 Bytes
    Upload folder using huggingface_hub 10 days ago
  • tokenizer.json
    3.52 MB
    xet
    Upload folder using huggingface_hub 10 days ago
  • tokenizer_config.json
    611 Bytes
    Upload folder using huggingface_hub 10 days ago