Instructions to use tmickleydoyle/tiny-autocomplete with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

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
```

tiny-autocomplete / compile_settings.json

tmickleydoyle

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 about 11 hours ago

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674 Bytes

{
  "_comment": "Chosen by scripts/sweep.py (docs/sweep.json): best keystrokes saved at >= 90% precision on held-out prompts. hf = dense transformers model (vocab capped for width); web = sparse browser export.",
  "hf": {
    "n_prev": 3,
    "smoothing": "kn",
    "phrase_weight": 1.0,
    "prompt_weight": 1.0,
    "reply_weight": 1.0,
    "max_vocab": 3000,
    "min_count": null,
    "param_target": 400000000
  },
  "web": {
    "n_prev": 3,
    "smoothing": "kn",
    "phrase_weight": 1.0,
    "prompt_weight": 1.0,
    "reply_weight": 1.0,
    "max_vocab": 8000,
    "min_count": 2
  },
  "policy": {
    "tau": 0.72,
    "min_support": 0,
    "max_words": 8
  }
}

	{
	"_comment": "Chosen by scripts/sweep.py (docs/sweep.json): best keystrokes saved at >= 90% precision on held-out prompts. hf = dense transformers model (vocab capped for width); web = sparse browser export.",
	"hf": {
	"n_prev": 3,
	"smoothing": "kn",
	"phrase_weight": 1.0,
	"prompt_weight": 1.0,
	"reply_weight": 1.0,
	"max_vocab": 3000,
	"min_count": null,
	"param_target": 400000000
	},
	"web": {
	"n_prev": 3,
	"smoothing": "kn",
	"phrase_weight": 1.0,
	"prompt_weight": 1.0,
	"reply_weight": 1.0,
	"max_vocab": 8000,
	"min_count": 2
	},
	"policy": {
	"tau": 0.72,
	"min_support": 0,
	"max_words": 8
	}
	}