Instructions to use sbintuitions/tiny-lm with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use sbintuitions/tiny-lm with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="sbintuitions/tiny-lm")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("sbintuitions/tiny-lm") model = AutoModelForCausalLM.from_pretrained("sbintuitions/tiny-lm") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use sbintuitions/tiny-lm with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "sbintuitions/tiny-lm" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "sbintuitions/tiny-lm", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/sbintuitions/tiny-lm
- SGLang
How to use sbintuitions/tiny-lm 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 "sbintuitions/tiny-lm" \ --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": "sbintuitions/tiny-lm", "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 "sbintuitions/tiny-lm" \ --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": "sbintuitions/tiny-lm", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use sbintuitions/tiny-lm with Docker Model Runner:
docker model run hf.co/sbintuitions/tiny-lm
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README.md
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from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
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model = AutoModelForCausalLM.from_pretrained("sbintuitions/tiny-lm", torch_dtype="auto")
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tokenizer = AutoTokenizer.from_pretrained("sbintuitions/tiny-lm")
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generator = pipeline("text-generation", model=model, tokenizer=tokenizer)
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print(generator("Hello", max_length=30, do_sample=True, top_k=100))
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```
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from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
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model = AutoModelForCausalLM.from_pretrained("sbintuitions/tiny-lm", torch_dtype="auto")
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tokenizer = AutoTokenizer.from_pretrained("sbintuitions/tiny-lm", use_fast=False)
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generator = pipeline("text-generation", model=model, tokenizer=tokenizer)
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print(generator("Hello", max_length=30, do_sample=True, top_k=100))
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```
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