Instructions to use SYH99999/output with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use SYH99999/output with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="SYH99999/output")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("SYH99999/output") model = AutoModelForCausalLM.from_pretrained("SYH99999/output", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use SYH99999/output with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "SYH99999/output" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "SYH99999/output", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/SYH99999/output
- SGLang
How to use SYH99999/output 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 "SYH99999/output" \ --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": "SYH99999/output", "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 "SYH99999/output" \ --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": "SYH99999/output", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use SYH99999/output with Docker Model Runner:
docker model run hf.co/SYH99999/output
Upload tokenizer
Browse files- spiece.model +3 -0
- tokenizer_config.json +1 -1
spiece.model
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version https://git-lfs.github.com/spec/v1
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oid sha256:b5cbdfa8aa7c54c8c5af85b78c309c54a5f2749a20468bf6f60eee007fe6fec1
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size 805634
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tokenizer_config.json
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"eos_token": "</s>",
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"extra_ids": 0,
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"mask_token": "[MASK]",
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"name_or_path": "/
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"pad_token": "[PAD]",
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"sep_token": "[SEP]",
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"sp_model_kwargs": {},
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"eos_token": "</s>",
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"extra_ids": 0,
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"mask_token": "[MASK]",
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"name_or_path": "rinna/japanese-gpt2-medium",
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"pad_token": "[PAD]",
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"sep_token": "[SEP]",
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"sp_model_kwargs": {},
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