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FiShota
/
hinomoto-100m-v7

Text Generation
Transformers
PyTorch
Japanese
japanese
llama
from-scratch
solo-developer
keigo
family-conversation
Model card Files Files and versions
xet
Community

Instructions to use FiShota/hinomoto-100m-v7 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • Transformers

    How to use FiShota/hinomoto-100m-v7 with Transformers:

    # Use a pipeline as a high-level helper
    from transformers import pipeline
    
    pipe = pipeline("text-generation", model="FiShota/hinomoto-100m-v7")
    # Load model directly
    from transformers import AutoModel
    model = AutoModel.from_pretrained("FiShota/hinomoto-100m-v7", dtype="auto")
  • Notebooks
  • Google Colab
  • Kaggle
  • Local Apps
  • vLLM

    How to use FiShota/hinomoto-100m-v7 with vLLM:

    Install from pip and serve model
    # Install vLLM from pip:
    pip install vllm
    # Start the vLLM server:
    vllm serve "FiShota/hinomoto-100m-v7"
    # Call the server using curl (OpenAI-compatible API):
    curl -X POST "http://localhost:8000/v1/completions" \
    	-H "Content-Type: application/json" \
    	--data '{
    		"model": "FiShota/hinomoto-100m-v7",
    		"prompt": "Once upon a time,",
    		"max_tokens": 512,
    		"temperature": 0.5
    	}'
    Use Docker
    docker model run hf.co/FiShota/hinomoto-100m-v7
  • SGLang

    How to use FiShota/hinomoto-100m-v7 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 "FiShota/hinomoto-100m-v7" \
        --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": "FiShota/hinomoto-100m-v7",
    		"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 "FiShota/hinomoto-100m-v7" \
            --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": "FiShota/hinomoto-100m-v7",
    		"prompt": "Once upon a time,",
    		"max_tokens": 512,
    		"temperature": 0.5
    	}'
  • Docker Model Runner

    How to use FiShota/hinomoto-100m-v7 with Docker Model Runner:

    docker model run hf.co/FiShota/hinomoto-100m-v7
hinomoto-100m-v7
174 MB
Ctrl+K
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  • 1 contributor
History: 3 commits
FiShota's picture
FiShota
Update README to v2 (YAML metadata + example code + security audit summary)
5910231 verified about 1 month ago
  • .gitattributes
    1.52 kB
    initial commit about 1 month ago
  • README.md
    6.78 kB
    Update README to v2 (YAML metadata + example code + security audit summary) about 1 month ago
  • config.json
    221 Bytes
    Initial upload of HinoMoto-100M-v4 about 1 month ago
  • generation_config.json
    85 Bytes
    Initial upload of HinoMoto-100M-v4 about 1 month ago
  • manifest.json
    294 Bytes
    Initial upload of HinoMoto-100M-v4 about 1 month ago
  • pytorch_model.bin

    Detected Pickle imports (3)

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

    What is a pickle import?

    174 MB
    xet
    Initial upload of HinoMoto-100M-v4 about 1 month ago
  • tokenizer.json
    544 kB
    Initial upload of HinoMoto-100M-v4 about 1 month ago