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 / data /SOURCES.md

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

preview code

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2.08 kB

Data sources

prompts.txt and replies.txt are produced by scripts/fetch_coding_prompts.py. Every line is human-written English prose taken from the datasets below. LLM-generated messages are excluded (OASST synthetic flag). The script keeps only prose: it removes code blocks, code-looking lines and markdown, keeps up to 2 sentences per prompt and 5 per reply, and keeps sentences of 3–24 words.

File	Dataset	What is used	License	Attribution
`prompts.txt`	OpenAssistant/oasst2	English user messages (all turns, reviewed, not deleted, not synthetic) from conversations whose opening prompt is about code	Apache-2.0	Köpf et al., OpenAssistant Conversations: Democratizing Large Language Model Alignment, 2023
`prompts.txt`	google-research-datasets/mbpp	task descriptions (`text`), all splits	CC-BY-4.0	Austin et al., Program Synthesis with Large Language Models, 2021
`prompts.txt`	neulab/conala	curated intents (`intent`), paired train + test	MIT	Yin et al., Learning to Mine Aligned Code and Natural Language Pairs from Stack Overflow, MSR 2018
`replies.txt`	OpenAssistant/oasst2	English assistant messages, written by human volunteers, from the same conversations	Apache-2.0	as above

Counts from the last fetch:

Source	Sentences
OASST user turns	4,169
MBPP	938
CoNaLa	2,820
OASST assistant turns	13,183

After removing duplicates: 7,133 lines in prompts.txt and 12,841 in replies.txt.

The weights contain only n-gram statistics derived from these lines, plus the hand-written phrases in ../phrases/. No dataset was used to train anything. 10% of prompts.txt (chosen by content hash) is held out when measuring precision and keystrokes saved (scripts/sweep.py, scripts/benchmark.py).