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HenrySentinel
/
tinyMind-SFT

Text Generation
Transformers
PyTorch
Safetensors
tiny_smart_llm
trl
sft
conversational
Model card Files Files and versions
xet
Community

Instructions to use HenrySentinel/tinyMind-SFT with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • Transformers

    How to use HenrySentinel/tinyMind-SFT with Transformers:

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

    How to use HenrySentinel/tinyMind-SFT with vLLM:

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

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

    How to use HenrySentinel/tinyMind-SFT with Docker Model Runner:

    docker model run hf.co/HenrySentinel/tinyMind-SFT
tinyMind-SFT
145 MB
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  • 1 contributor
History: 8 commits
HenrySentinel's picture
HenrySentinel
SFT epoch 3 CPU
7eee6d2 verified 9 days ago
  • .gitattributes
    1.52 kB
    initial commit 14 days ago
  • README.md
    5.18 kB
    SFT epoch 1 CPU 9 days ago
  • chat_template.jinja
    379 Bytes
    SFT on SmolTalk (20K) - SmolLM2 recipe 14 days ago
  • config.json
    466 Bytes
    SFT epoch 1 CPU 9 days ago
  • configuration_tinymind.py
    874 Bytes
    Add configuration_tinymind.py 14 days ago
  • generation_config.json
    216 Bytes
    SFT epoch 1 CPU 9 days ago
  • model.safetensors
    70.9 MB
    xet
    SFT epoch 3 CPU 9 days ago
  • modeling_tinymind.py
    6.85 kB
    Add modeling_tinymind.py 14 days ago
  • pytorch_model.bin

    Detected Pickle imports (3)

    • "torch._utils._rebuild_tensor_v2",
    • "torch.FloatStorage",
    • "collections.OrderedDict"

    What is a pickle import?

    70.9 MB
    xet
    SFT on SmolTalk (20K) - SmolLM2 recipe 14 days ago
  • tokenizer.json
    3.56 MB
    SFT epoch 1 CPU 9 days ago
  • tokenizer_config.json
    366 Bytes
    SFT on SmolTalk (20K) - SmolLM2 recipe 14 days ago