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 / docs /experiment_topic.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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782 Bytes

{
  "topic_words": 48,
  "results": [
    {
      "lambda": 0.0,
      "tau": 0.72,
      "min_support": 0,
      "precision": 0.9029,
      "coverage": 0.3866,
      "keystrokes_saved": 0.2296,
      "shown": 16034
    },
    {
      "lambda": 0.25,
      "tau": 0.72,
      "min_support": 0,
      "precision": 0.9032,
      "coverage": 0.3864,
      "keystrokes_saved": 0.2297,
      "shown": 16026
    },
    {
      "lambda": 0.5,
      "tau": 0.72,
      "min_support": 0,
      "precision": 0.9031,
      "coverage": 0.386,
      "keystrokes_saved": 0.2293,
      "shown": 16007
    },
    {
      "lambda": 1.0,
      "tau": 0.73,
      "min_support": 0,
      "precision": 0.904,
      "coverage": 0.3811,
      "keystrokes_saved": 0.2281,
      "shown": 15804
    }
  ]
}

	{
	"topic_words": 48,
	"results": [
	{
	"lambda": 0.0,
	"tau": 0.72,
	"min_support": 0,
	"precision": 0.9029,
	"coverage": 0.3866,
	"keystrokes_saved": 0.2296,
	"shown": 16034
	},
	{
	"lambda": 0.25,
	"tau": 0.72,
	"min_support": 0,
	"precision": 0.9032,
	"coverage": 0.3864,
	"keystrokes_saved": 0.2297,
	"shown": 16026
	},
	{
	"lambda": 0.5,
	"tau": 0.72,
	"min_support": 0,
	"precision": 0.9031,
	"coverage": 0.386,
	"keystrokes_saved": 0.2293,
	"shown": 16007
	},
	{
	"lambda": 1.0,
	"tau": 0.73,
	"min_support": 0,
	"precision": 0.904,
	"coverage": 0.3811,
	"keystrokes_saved": 0.2281,
	"shown": 15804
	}
	]
	}