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

Securelayer7
/
Ling-3.0-tiny-Uncensored-Abliterated

Text Generation
Transformers
Safetensors
English
ling
bailing-moe
uncensored
abliterated
uncensored-llm
no-refusal
Mixture of Experts
mixture-of-experts
linear-attention
apple-silicon
mps
reasoning
cybersecurity
red-teaming
conversational
custom_code
Model card Files Files and versions
xet
Community

Instructions to use Securelayer7/Ling-3.0-tiny-Uncensored-Abliterated with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • Transformers

    How to use Securelayer7/Ling-3.0-tiny-Uncensored-Abliterated with Transformers:

    # Use a pipeline as a high-level helper
    from transformers import pipeline
    
    pipe = pipeline("text-generation", model="Securelayer7/Ling-3.0-tiny-Uncensored-Abliterated", trust_remote_code=True)
    messages = [
        {"role": "user", "content": "Who are you?"},
    ]
    pipe(messages)
    # Load model directly
    from transformers import AutoModelForCausalLM
    model = AutoModelForCausalLM.from_pretrained("Securelayer7/Ling-3.0-tiny-Uncensored-Abliterated", trust_remote_code=True, device_map="auto")
  • Notebooks
  • Google Colab
  • Kaggle
  • Local Apps Settings
  • vLLM

    How to use Securelayer7/Ling-3.0-tiny-Uncensored-Abliterated with vLLM:

    Install from pip and serve model
    # Install vLLM from pip:
    pip install vllm
    # Start the vLLM server:
    vllm serve "Securelayer7/Ling-3.0-tiny-Uncensored-Abliterated"
    # Call the server using curl (OpenAI-compatible API):
    curl -X POST "http://localhost:8000/v1/chat/completions" \
    	-H "Content-Type: application/json" \
    	--data '{
    		"model": "Securelayer7/Ling-3.0-tiny-Uncensored-Abliterated",
    		"messages": [
    			{
    				"role": "user",
    				"content": "What is the capital of France?"
    			}
    		]
    	}'
    Use Docker
    docker model run hf.co/Securelayer7/Ling-3.0-tiny-Uncensored-Abliterated
  • SGLang

    How to use Securelayer7/Ling-3.0-tiny-Uncensored-Abliterated 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 "Securelayer7/Ling-3.0-tiny-Uncensored-Abliterated" \
        --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": "Securelayer7/Ling-3.0-tiny-Uncensored-Abliterated",
    		"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 "Securelayer7/Ling-3.0-tiny-Uncensored-Abliterated" \
            --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": "Securelayer7/Ling-3.0-tiny-Uncensored-Abliterated",
    		"messages": [
    			{
    				"role": "user",
    				"content": "What is the capital of France?"
    			}
    		]
    	}'
  • Docker Model Runner

    How to use Securelayer7/Ling-3.0-tiny-Uncensored-Abliterated with Docker Model Runner:

    docker model run hf.co/Securelayer7/Ling-3.0-tiny-Uncensored-Abliterated
Ling-3.0-tiny-Uncensored-Abliterated
15.8 GB
Ctrl+K
Ctrl+K
  • 1 contributor
History: 3 commits
sandeep1337's picture
sandeep1337
Remove emojis (match house style)
f3814e8 verified 1 day ago
  • .gitattributes
    1.57 kB
    Add files using upload-large-folder tool 1 day ago
  • LICENSE
    1.3 kB
    Add files using upload-large-folder tool 1 day ago
  • NOTICE
    1.69 kB
    Add files using upload-large-folder tool 1 day ago
  • README.md
    3.85 kB
    Remove emojis (match house style) 1 day ago
  • chat_template.jinja
    6.04 kB
    Add files using upload-large-folder tool 1 day ago
  • config.json
    2.36 kB
    Add files using upload-large-folder tool 1 day ago
  • configuration_bailing_moe_v3.py
    4.73 kB
    Add files using upload-large-folder tool 1 day ago
  • generation_config.json
    114 Bytes
    Add files using upload-large-folder tool 1 day ago
  • model-00001-of-00004.safetensors
    5 GB
    xet
    Add files using upload-large-folder tool 1 day ago
  • model-00002-of-00004.safetensors
    5 GB
    xet
    Add files using upload-large-folder tool 1 day ago
  • model-00003-of-00004.safetensors
    5 GB
    xet
    Add files using upload-large-folder tool 1 day ago
  • model-00004-of-00004.safetensors
    790 MB
    xet
    Add files using upload-large-folder tool 1 day ago
  • model.safetensors.index.json
    834 kB
    Add files using upload-large-folder tool 1 day ago
  • modeling_bailing_moe_v3.py
    80.2 kB
    Add files using upload-large-folder tool 1 day ago
  • tokenizer.json
    12.2 MB
    xet
    Add files using upload-large-folder tool 1 day ago
  • tokenizer_config.json
    486 Bytes
    Add files using upload-large-folder tool 1 day ago