Text Generation
Transformers
Safetensors
English
llama
compiled
no-training
autocomplete
n-gram
code
browser
text-generation-inference
Instructions to use tmickleydoyle/ghost-typer with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use tmickleydoyle/ghost-typer with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="tmickleydoyle/ghost-typer")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("tmickleydoyle/ghost-typer") model = AutoModelForCausalLM.from_pretrained("tmickleydoyle/ghost-typer", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use tmickleydoyle/ghost-typer with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "tmickleydoyle/ghost-typer" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "tmickleydoyle/ghost-typer", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/tmickleydoyle/ghost-typer
- SGLang
How to use tmickleydoyle/ghost-typer 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/ghost-typer" \ --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/ghost-typer", "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/ghost-typer" \ --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/ghost-typer", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use tmickleydoyle/ghost-typer with Docker Model Runner:
docker model run hf.co/tmickleydoyle/ghost-typer
v6: modified Kneser-Ney + per-word extension rule (held-out keystrokes saved 22.2% -> 23.0%, precision 90.2% -> 90.8%)
3c53a76 verified Download docs/experiments_lookback.json from tmickleydoyle/ghost-typer: direct link, hf CLI and curl.
- Browser
- Download file 62.8 kB
-
https://huggingface.co/tmickleydoyle/ghost-typer/resolve/main/docs/experiments_lookback.json
- Command line
-
hf download hf://tmickleydoyle/ghost-typer/docs/experiments_lookback.json
-
curl -L -o experiments_lookback.json https://huggingface.co/tmickleydoyle/ghost-typer/resolve/main/docs/experiments_lookback.json
62.8 kB
{
"what": "Lookback / look-ahead experiments (local harness; compile settings as in each run's 'settings'). Each variant compiled without the held-out prompts, every held-out prompt typed one key at a time; per policy family, the threshold with the most keystrokes saved at >= 90% precision. joint = extend while the product of probabilities >= tau (old rule); step/stepfine = first word >= tau, then each next word >= ext (new rule); min2/min3 = show nothing unless >= 2/3 words clear tau. Runs named final-* use the larger held-out set (711 prompts + 4,000 WildChat); the rest use 711 + 1,000. wc48r2/wc48r4 = with extra WildChat-4.8M text (not adopted; size not verified). export_mb of stage-2 runs was over-estimated by a since-fixed bug.",
"target_precision": 0.9,
"runs": {
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"group": "baseline",
"data": "wc1m",
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"replies": 12833,
"wildchat": 94678
},
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"3": 164583,
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},
"export_mb": 19.9,
"policies": {
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"keystrokes_saved": 0.2219,
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},
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}
},
"min2": {
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"precision": 0.9,
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"len_share": {
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"beam_check": {
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"seconds": 207.4
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"n4-kn": {
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},
"seconds": 206.9
},
"n5-kn-long4": {
"name": "n5-kn-long4",
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},
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},
"final-n3-mkn": {
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}
}
{
"what": "Lookback / look-ahead experiments (local harness; compile settings as in each run's 'settings'). Each variant compiled without the held-out prompts, every held-out prompt typed one key at a time; per policy family, the threshold with the most keystrokes saved at >= 90% precision. joint = extend while the product of probabilities >= tau (old rule); step/stepfine = first word >= tau, then each next word >= ext (new rule); min2/min3 = show nothing unless >= 2/3 words clear tau. Runs named final-* use the larger held-out set (711 prompts + 4,000 WildChat); the rest use 711 + 1,000. wc48r2/wc48r4 = with extra WildChat-4.8M text (not adopted; size not verified). export_mb of stage-2 runs was over-estimated by a since-fixed bug.",
"target_precision": 0.9,
"runs": {
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"name": "n3-kn",
"group": "baseline",
"data": "wc1m",
"settings": {
"n_prev": 3,
"smoothing": "kn",
"min_count": 2,
"order_min_counts": null
},
"lines": {
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"prompts": 6421,
"replies": 12833,
"wildchat": 94678
},
"vocab": 29232,
"min_context_count": 2,
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"contexts_kept": 467863,
"contexts_by_len": {
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"2": 149984,
"3": 164583,
"4": 124429
},
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"precision": 0.9019,
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"shown": 40316,
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},
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