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
File size: 1,915 Bytes
4113449 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 | // Run the browser runtime from the command line (Bun or Node >= 23.6):
// bun web/cli.ts "write a python fun" "how do i "
// bun web/cli.ts --json ... full suggestion chains (used by test_model.py)
// bun web/cli.ts --dist ... top-5 next-token log-probs after each text
// bun web/cli.ts --model path/to/model.bin use another export (default web/model.bin)
import { readFileSync } from "node:fs";
import { join } from "node:path";
import { preTokenize } from "../tokenizer.ts";
import { CompiledAutocomplete } from "./autocomplete.ts";
const dir = import.meta.dirname ?? new URL(".", import.meta.url).pathname;
let args = process.argv.slice(2);
let modelPath = join(dir, "model.bin");
const mi = args.indexOf("--model");
if (mi >= 0) {
modelPath = args[mi + 1];
args = [...args.slice(0, mi), ...args.slice(mi + 2)];
}
const mode = args[0] === "--json" || args[0] === "--dist" ? args.shift() : "";
const buf = readFileSync(modelPath);
const ac = new CompiledAutocomplete(buf.buffer.slice(buf.byteOffset, buf.byteOffset + buf.byteLength));
if (mode === "--dist") {
const out = args.map((t) => {
const ids = [ac.eosId, ...preTokenize(t).map((p) => ac.vocab.indexOf(p)).map((i) => (i < 0 ? ac.unkId : i))];
const { probs } = ac.dist(ids);
const top = [...probs.keys()].sort((a, b) => probs[b] - probs[a]).slice(0, 5);
return { text: t, top: top.map((i) => [i, Math.log(probs[i])]) };
});
console.log(JSON.stringify(out));
} else {
const results = args.map((t) => {
const start = performance.now();
const s = ac.suggest(t);
return { text: t, ...s, ms: performance.now() - start };
});
if (mode === "--json") console.log(JSON.stringify(results));
else for (const r of results) console.log(`${r.text}\x1b[90m${r.ghost}\x1b[0m ${r.ghost ? Math.round(r.prob * 100) + "%" : "(no confident suggestion)"} ${r.ms.toFixed(2)} ms`);
}
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