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

KissTheHabit
/
IDA_Edge

Text Generation
Transformers
TensorBoard
Safetensors
ida-family
ida-lattice
causal-lm
edge-body
paired-body
governed-memory
recurrent-state
local-attention
cognitive-routing
Model card Files Files and versions
xet
Metrics Training metrics Community

Instructions to use KissTheHabit/IDA_Edge with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • Transformers

    How to use KissTheHabit/IDA_Edge with Transformers:

    # Use a pipeline as a high-level helper
    from transformers import pipeline
    
    pipe = pipeline("text-generation", model="KissTheHabit/IDA_Edge")
    # pip install -U transformers accelerate
    # Load model directly
    from transformers import AutoModel
    model = AutoModel.from_pretrained("KissTheHabit/IDA_Edge", device_map="auto")
  • Notebooks
  • Google Colab
  • Kaggle
  • Local Apps Settings
  • vLLM

    How to use KissTheHabit/IDA_Edge with vLLM:

    Install from pip and serve model
    # Install vLLM from pip:
    pip install vllm
    # Start the vLLM server:
    vllm serve "KissTheHabit/IDA_Edge"
    # Call the server using curl (OpenAI-compatible API):
    curl -X POST "http://localhost:8000/v1/completions" \
    	-H "Content-Type: application/json" \
    	--data '{
    		"model": "KissTheHabit/IDA_Edge",
    		"prompt": "Once upon a time,",
    		"max_tokens": 512,
    		"temperature": 0.5
    	}'
    Use Docker
    docker model run hf.co/KissTheHabit/IDA_Edge
  • SGLang

    How to use KissTheHabit/IDA_Edge 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 "KissTheHabit/IDA_Edge" \
        --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": "KissTheHabit/IDA_Edge",
    		"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 "KissTheHabit/IDA_Edge" \
            --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": "KissTheHabit/IDA_Edge",
    		"prompt": "Once upon a time,",
    		"max_tokens": 512,
    		"temperature": 0.5
    	}'
  • Docker Model Runner

    How to use KissTheHabit/IDA_Edge with Docker Model Runner:

    docker model run hf.co/KissTheHabit/IDA_Edge

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
  • ATLAS
    Upload ATLAS 0.0.0.1 artifacts about 2 months ago
  • ECHO
    Upload ECHO 0.0.0.1 artifacts about 2 months ago
  • FORGE
    Upload FORGE 0.0.0.1 artifacts about 2 months ago
  • IDA
    Update sanitized training status about 2 months ago
  • JUDGE
    Lambda training sync 0.0.0.3/JUDGE about 2 months ago
  • ORBIT
    Upload ORBIT 0.0.0.1 artifacts about 2 months ago
  • PRISM
    Upload PRISM 0.0.0.1 artifacts about 2 months ago
  • PULSE
    Upload PULSE 0.0.0.1 artifacts about 2 months ago
  • SENTINEL
    Lambda training sync 0.0.0.3/SENTINEL about 2 months ago
  • SHADE
    Upload SHADE 0.0.0.1 artifacts about 2 months ago
  • VECTOR
    Upload VECTOR 0.0.0.1 artifacts about 2 months ago