Instructions to use Finisha-F-scratch/SoraForSLM-1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Finisha-F-scratch/SoraForSLM-1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="Finisha-F-scratch/SoraForSLM-1", trust_remote_code=True)# Load model directly from transformers import AutoModelForCausalLM model = AutoModelForCausalLM.from_pretrained("Finisha-F-scratch/SoraForSLM-1", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use Finisha-F-scratch/SoraForSLM-1 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Finisha-F-scratch/SoraForSLM-1" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Finisha-F-scratch/SoraForSLM-1", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/Finisha-F-scratch/SoraForSLM-1
- SGLang
How to use Finisha-F-scratch/SoraForSLM-1 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 "Finisha-F-scratch/SoraForSLM-1" \ --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": "Finisha-F-scratch/SoraForSLM-1", "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 "Finisha-F-scratch/SoraForSLM-1" \ --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": "Finisha-F-scratch/SoraForSLM-1", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use Finisha-F-scratch/SoraForSLM-1 with Docker Model Runner:
docker model run hf.co/Finisha-F-scratch/SoraForSLM-1
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| "▁hardware.": 1879, | |
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| "▁healthcare,": 1885, | |
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| "▁here\",": 1888, | |
| "▁here.": 1889, | |
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| "▁high-volume,": 1891, | |
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| "▁hours,": 1895, | |
| "▁how.": 1896, | |
| "▁https://github.com/SreeEswaran/Train-your-LLM": 1897, | |
| "▁human-labeled": 1898, | |
| "▁human.": 1899, | |
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| "▁hyperparameters,": 1903, | |
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| "▁i.e.,": 1905, | |
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| "▁important.": 1917, | |
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| "▁improvement.": 1920, | |
| "▁improvements,": 1921, | |
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| "▁include:": 1924, | |
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| "▁industries.": 1935, | |
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| "▁industry,": 1937, | |
| "▁industry:": 1938, | |
| "▁influenced": 1939, | |
| "▁information,": 1940, | |
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| "▁infrastructure.": 1942, | |
| "▁innovation": 1943, | |
| "▁innovation,": 1944, | |
| "▁innovative": 1945, | |
| "▁input.": 1946, | |
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| "▁insightful,": 1949, | |
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| "▁insights!": 1951, | |
| "▁installed.": 1952, | |
| "▁instance,": 1953, | |
| "▁instances.": 1954, | |
| "▁instantiated": 1955, | |
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| "▁is.": 1970, | |
| "▁isn’t": 1971, | |
| "▁issue:": 1972, | |
| "▁it.": 1973, | |
| "▁iteratively,": 1974, | |
| "▁job.": 1975, | |
| "▁joining": 1976, | |
| "▁journey,": 1977, | |
| "▁kept": 1978, | |
| "▁key.": 1979, | |
| "▁kind": 1980, | |
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| "▁knowledge": 1982, | |
| "▁knowledge.": 1983, | |
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| "▁labs,": 1986, | |
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| "▁lemmatization": 2001, | |
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| "▁length.": 2004, | |
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| "▁level,": 2008, | |
| "▁level.": 2009, | |
| "▁leverage": 2010, | |
| "▁leverages": 2011, | |
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| "▁libraries.": 2013, | |
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| "▁llama@gmail.com": 2030, | |
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| "▁logging.": 2032, | |
| "▁logging_dir='./logs',": 2033, | |
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