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/benchmarks.json from tmickleydoyle/tiny-autocomplete: direct link, hf CLI and curl.
- Browser
- Download file 2.85 kB
-
https://huggingface.co/tmickleydoyle/tiny-autocomplete/resolve/main/docs/benchmarks.json
- Command line
-
hf download hf://tmickleydoyle/tiny-autocomplete/docs/benchmarks.json
-
curl -L -o benchmarks.json https://huggingface.co/tmickleydoyle/tiny-autocomplete/resolve/main/docs/benchmarks.json
2.85 kB
| { | |
| "hardware": { | |
| "cpu": "Darwin arm64", | |
| "cores": 10, | |
| "ram_gb": 32, | |
| "gpu": "none used", | |
| "python": "3.13.4", | |
| "torch": "2.15.0" | |
| }, | |
| "compile_dense": { | |
| "seconds": 6.9, | |
| "runs": [ | |
| 6.9, | |
| 7.0 | |
| ], | |
| "byte_identical_rebuild": true, | |
| "sha256": "ae857db3e321c35782192c198a7778987964e9cc147d2c67428fea9bf5777100", | |
| "mb": 1521.16, | |
| "peak_memory_gb": 2.4, | |
| "tokenize_seconds": 0.48, | |
| "count_seconds": 2.37, | |
| "build_tensors_seconds": 1.07, | |
| "source_lines": 21106, | |
| "params": 380290320, | |
| "nonzero_params": 26792230, | |
| "vocab": 3000, | |
| "min_context_count": 8.0, | |
| "contexts_seen": 434284, | |
| "contexts_kept": 24773 | |
| }, | |
| "compile_web": { | |
| "seconds": 6.6, | |
| "runs": [ | |
| 7.2, | |
| 6.6 | |
| ], | |
| "byte_identical_rebuild": true, | |
| "sha256": "d770bf4ec6644ccab5646e2b1244b58b185378b371c52b1c263b5a0ebf3f8499", | |
| "mb": 4.49, | |
| "contexts": 107864, | |
| "vocab": 6612 | |
| }, | |
| "pytorch": { | |
| "load_seconds": 0.1, | |
| "next_word_ms": 57.9, | |
| "generate_tokens_per_second": 18.0, | |
| "threads": 8 | |
| }, | |
| "web_keystroke_latency": { | |
| "runtime": "web/autocomplete.ts on Bun", | |
| "median_ms": 0.5, | |
| "p95_ms": 0.92, | |
| "max_ms": 3.48 | |
| }, | |
| "max_logprob_error_vs_ngram": 1.6793374655321713e-05, | |
| "heldout_typing_web": { | |
| "policy": { | |
| "tau": 0.72, | |
| "min_support": 0, | |
| "max_words": 8 | |
| }, | |
| "heldout_prompts": 711, | |
| "characters": 42185, | |
| "tau=0.72": { | |
| "precision": 0.9029, | |
| "coverage": 0.3866, | |
| "keystrokes_saved": 0.2296, | |
| "shown": 16034 | |
| }, | |
| "tau=0.8": { | |
| "precision": 0.9351, | |
| "coverage": 0.3412, | |
| "keystrokes_saved": 0.2066, | |
| "shown": 14153 | |
| }, | |
| "tau=0.9": { | |
| "precision": 0.9644, | |
| "coverage": 0.2692, | |
| "keystrokes_saved": 0.1642, | |
| "shown": 11165 | |
| }, | |
| "tau=0.95": { | |
| "precision": 0.9773, | |
| "coverage": 0.2049, | |
| "keystrokes_saved": 0.1176, | |
| "shown": 8497 | |
| } | |
| }, | |
| "heldout_typing_dense_tables": { | |
| "policy": { | |
| "tau": 0.72, | |
| "min_support": 0, | |
| "max_words": 8 | |
| }, | |
| "heldout_prompts": 711, | |
| "characters": 42185, | |
| "tau=0.72": { | |
| "precision": 0.9243, | |
| "coverage": 0.3245, | |
| "keystrokes_saved": 0.1902, | |
| "shown": 13458 | |
| }, | |
| "tau=0.8": { | |
| "precision": 0.9516, | |
| "coverage": 0.2698, | |
| "keystrokes_saved": 0.1574, | |
| "shown": 11191 | |
| }, | |
| "tau=0.9": { | |
| "precision": 0.9736, | |
| "coverage": 0.1921, | |
| "keystrokes_saved": 0.1057, | |
| "shown": 7966 | |
| }, | |
| "tau=0.95": { | |
| "precision": 0.9843, | |
| "coverage": 0.1322, | |
| "keystrokes_saved": 0.0698, | |
| "shown": 5481 | |
| } | |
| }, | |
| "heldout_next_token_dense": { | |
| "heldout_lines": 711, | |
| "heldout_tokens": 9466, | |
| "next_token_top1": 0.267, | |
| "next_token_top3": 0.406, | |
| "oov_rate": 0.07 | |
| } | |
| } | |