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
      • Learn
      • Discord
      • Forum
      • GitHub
    • Solutions
      • Team & Enterprise
      • Hugging Face PRO
      • Enterprise Support
      • Inference Providers
      • Inference Endpoints
      • Storage Buckets

  • Log In
  • Sign Up

mzen
/
EventModel-1.2B

Text Generation
PEFT
Safetensors
Transformers
lora
sft
trl
conversational
Model card Files Files and versions
xet
Community

Instructions to use mzen/EventModel-1.2B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • PEFT

    How to use mzen/EventModel-1.2B with PEFT:

    from peft import PeftModel
    from transformers import AutoModelForCausalLM
    
    base_model = AutoModelForCausalLM.from_pretrained("liquidai/LFM2-1.2B")
    model = PeftModel.from_pretrained(base_model, "mzen/EventModel-1.2B")
  • Transformers

    How to use mzen/EventModel-1.2B with Transformers:

    # Use a pipeline as a high-level helper
    from transformers import pipeline
    
    pipe = pipeline("text-generation", model="mzen/EventModel-1.2B")
    messages = [
        {"role": "user", "content": "Who are you?"},
    ]
    pipe(messages)
    # Load model directly
    from transformers import AutoModel
    model = AutoModel.from_pretrained("mzen/EventModel-1.2B", dtype="auto")
  • Notebooks
  • Google Colab
  • Kaggle
  • Local Apps
  • vLLM

    How to use mzen/EventModel-1.2B with vLLM:

    Install from pip and serve model
    # Install vLLM from pip:
    pip install vllm
    # Start the vLLM server:
    vllm serve "mzen/EventModel-1.2B"
    # Call the server using curl (OpenAI-compatible API):
    curl -X POST "http://localhost:8000/v1/chat/completions" \
    	-H "Content-Type: application/json" \
    	--data '{
    		"model": "mzen/EventModel-1.2B",
    		"messages": [
    			{
    				"role": "user",
    				"content": "What is the capital of France?"
    			}
    		]
    	}'
    Use Docker
    docker model run hf.co/mzen/EventModel-1.2B
  • SGLang

    How to use mzen/EventModel-1.2B 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 "mzen/EventModel-1.2B" \
        --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": "mzen/EventModel-1.2B",
    		"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 "mzen/EventModel-1.2B" \
            --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": "mzen/EventModel-1.2B",
    		"messages": [
    			{
    				"role": "user",
    				"content": "What is the capital of France?"
    			}
    		]
    	}'
  • Docker Model Runner

    How to use mzen/EventModel-1.2B with Docker Model Runner:

    docker model run hf.co/mzen/EventModel-1.2B
EventModel-1.2B / checkpoint-35
72 MB
Ctrl+K
Ctrl+K
  • 1 contributor
History: 1 commit
mzen's picture
mzen
Upload folder using huggingface_hub
2c6835d verified 3 months ago
  • README.md
    5.2 kB
    Upload folder using huggingface_hub 3 months ago
  • adapter_config.json
    1.05 kB
    Upload folder using huggingface_hub 3 months ago
  • adapter_model.safetensors
    44.5 MB
    xet
    Upload folder using huggingface_hub 3 months ago
  • chat_template.jinja
    1.38 kB
    Upload folder using huggingface_hub 3 months ago
  • optimizer.pt

    Detected Pickle imports (6)

    • "torch.Tensor",
    • "torch._tensor._rebuild_from_type_v2",
    • "collections.OrderedDict",
    • "torch.FloatStorage",
    • "torch.ByteStorage",
    • "torch._utils._rebuild_tensor_v2"

    How to fix it?

    22.8 MB
    xet
    Upload folder using huggingface_hub 3 months ago
  • rng_state.pth
    14.6 kB
    xet
    Upload folder using huggingface_hub 3 months ago
  • scheduler.pt

    Pickle imports

    • No problematic imports detected

    What is a pickle import?

    1.47 kB
    xet
    Upload folder using huggingface_hub 3 months ago
  • tokenizer.json
    4.73 MB
    Upload folder using huggingface_hub 3 months ago
  • tokenizer_config.json
    489 Bytes
    Upload folder using huggingface_hub 3 months ago
  • trainer_state.json
    11.2 kB
    Upload folder using huggingface_hub 3 months ago
  • training_args.bin
    5.59 kB
    xet
    Upload folder using huggingface_hub 3 months ago