Text Generation
Transformers
Safetensors
English
llama
hand-crafted
compiled
autocomplete
no-training
n-gram
code
browser
text-generation-inference
Instructions to use tmickleydoyle/tiny-autocomplete with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use tmickleydoyle/tiny-autocomplete with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="tmickleydoyle/tiny-autocomplete")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("tmickleydoyle/tiny-autocomplete") model = AutoModelForCausalLM.from_pretrained("tmickleydoyle/tiny-autocomplete", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use tmickleydoyle/tiny-autocomplete with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "tmickleydoyle/tiny-autocomplete" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "tmickleydoyle/tiny-autocomplete", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/tmickleydoyle/tiny-autocomplete
- SGLang
How to use tmickleydoyle/tiny-autocomplete 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 "tmickleydoyle/tiny-autocomplete" \ --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": "tmickleydoyle/tiny-autocomplete", "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 "tmickleydoyle/tiny-autocomplete" \ --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": "tmickleydoyle/tiny-autocomplete", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use tmickleydoyle/tiny-autocomplete with Docker Model Runner:
docker model run hf.co/tmickleydoyle/tiny-autocomplete
Confidence-gated ghost text for coding prompts: Kneser-Ney compile, 4.5 MB browser export + zero-dep runtime, held-out precision/keystrokes-saved benchmarks
4113449 verified Download docs/sweep.json from tmickleydoyle/tiny-autocomplete: direct link, hf CLI and curl.
- Browser
- Download file 46.7 kB
-
https://huggingface.co/tmickleydoyle/tiny-autocomplete/resolve/main/docs/sweep.json
- Command line
-
hf download hf://tmickleydoyle/tiny-autocomplete/docs/sweep.json
-
curl -L -o sweep.json https://huggingface.co/tmickleydoyle/tiny-autocomplete/resolve/main/docs/sweep.json
46.7 kB
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