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: 7,409 Bytes
ab4f92b b46c110 ab4f92b | 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 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 | <!doctype html>
<html lang="en">
<head>
<meta charset="utf-8">
<meta name="viewport" content="width=device-width, initial-scale=1">
<title>tiny-autocomplete</title>
<style>
:root { --bg: #fafaf9; --fg: #1c1917; --muted: #78716c; --line: #e7e5e4; --accent: #2563eb; --ghost: #a8a29e; --card: #fff; }
@media (prefers-color-scheme: dark) {
:root { --bg: #0c0a09; --fg: #f5f5f4; --muted: #a8a29e; --line: #292524; --accent: #60a5fa; --ghost: #57534e; --card: #1c1917; }
}
* { box-sizing: border-box; }
body { margin: 0; font: 16px/1.5 system-ui, -apple-system, sans-serif; background: var(--bg); color: var(--fg); }
main { max-width: 720px; margin: 0 auto; padding: 48px 20px; }
h1 { font-size: 24px; margin: 0 0 4px; }
.sub { color: var(--muted); margin: 0 0 28px; font-size: 14px; }
.editor { position: relative; background: var(--card); border: 1px solid var(--line); border-radius: 12px; }
.editor:focus-within { border-color: var(--accent); }
.layer { font: 20px/1.5 system-ui, -apple-system, sans-serif; padding: 16px 18px; margin: 0; white-space: pre-wrap; word-wrap: break-word; }
#ghost { position: absolute; inset: 0; pointer-events: none; color: transparent; }
#ghost span { color: var(--ghost); }
#prompt { position: relative; width: 100%; min-height: 64px; border: 0; outline: 0; resize: none; background: transparent; color: var(--fg); overflow: hidden; }
.hint { display: flex; justify-content: space-between; color: var(--muted); font-size: 13px; margin: 8px 2px 24px; }
kbd { border: 1px solid var(--line); border-radius: 4px; padding: 0 5px; font-size: 12px; background: var(--card); }
h2 { font-size: 13px; text-transform: uppercase; letter-spacing: .06em; color: var(--muted); margin: 0 0 10px; font-weight: 600; }
#next { display: grid; gap: 6px; margin-bottom: 28px; min-height: 40px; }
.cand { display: grid; grid-template-columns: 140px 1fr 56px; align-items: center; gap: 12px; padding: 6px 10px; border: 1px solid var(--line); border-radius: 8px; background: var(--card); cursor: pointer; text-align: left; font: inherit; color: inherit; }
.cand:hover { border-color: var(--accent); }
.bar { height: 6px; border-radius: 3px; background: var(--line); overflow: hidden; }
.bar i { display: block; height: 100%; background: var(--accent); }
.pct { text-align: right; color: var(--muted); font-variant-numeric: tabular-nums; font-size: 14px; }
.tok { font-family: ui-monospace, SFMono-Regular, Menlo, monospace; font-size: 14px; white-space: pre; overflow: hidden; text-overflow: ellipsis; }
.examples { display: flex; flex-wrap: wrap; gap: 8px; }
.ex { border: 1px solid var(--line); background: var(--card); color: inherit; border-radius: 999px; padding: 4px 12px; font: inherit; font-size: 14px; cursor: pointer; }
.ex:hover { border-color: var(--accent); }
.empty { color: var(--muted); font-size: 14px; }
</style>
</head>
<body>
<main>
<h1>tiny-autocomplete</h1>
<p class="sub" id="info">A compiled (not trained) Llama-format model, running in plain TypeScript.</p>
<div class="editor">
<pre class="layer" id="ghost" aria-hidden="true"></pre>
<textarea class="layer" id="prompt" rows="1" placeholder="Start typing, e.g. Once upon a" autofocus spellcheck="false"></textarea>
</div>
<div class="hint">
<span><kbd>Tab</kbd> accept completion <kbd>Enter</kbd> accept next word</span>
<span id="status"></span>
</div>
<h2>Next word</h2>
<div id="next"><div class="empty">Type a prompt to see predictions.</div></div>
<h2>Try</h2>
<div class="examples" id="examples"></div>
</main>
<script>
var EXAMPLES = ["Once upon a", "The quick brown", "Thank you for", "I hope this email", "Can I have the",
"Actions speak", "How are", "Looking forward to", "Where is the", "Don't worry",
"In Python,", "How to convert a", "You can use the", "Write a python function to", "Here is an example of"];
var promptEl = document.getElementById("prompt");
var ghostEl = document.getElementById("ghost");
var nextEl = document.getElementById("next");
var statusEl = document.getElementById("status");
var latest = { completion: "", top: [] };
var seq = 0, timer = null;
function esc(s) { return s.replace(/[&<>]/g, function (c) { return { "&": "&", "<": "<", ">": ">" }[c]; }); }
function render() {
var text = promptEl.value;
ghostEl.innerHTML = esc(text) + "<span>" + esc(text ? latest.completion : "") + "</span>";
promptEl.style.height = "auto";
promptEl.style.height = Math.max(promptEl.scrollHeight, ghostEl.scrollHeight) + "px";
if (!text.trim() || !latest.top.length) {
nextEl.innerHTML = '<div class="empty">Type a prompt to see predictions.</div>';
return;
}
nextEl.innerHTML = "";
latest.top.forEach(function (t) {
var p = Math.exp(t.logprob) * 100;
var b = document.createElement("button");
b.className = "cand";
b.innerHTML = '<span class="tok"></span><span class="bar"><i style="width:' + p.toFixed(1) + '%"></i></span><span class="pct">' + p.toFixed(1) + "%</span>";
b.querySelector(".tok").textContent = t.id === 0 ? "<end>" : JSON.stringify(t.token);
b.onclick = function () { if (t.id !== 0) append(t.token); promptEl.focus(); };
nextEl.appendChild(b);
});
}
function append(s) { promptEl.value += s; latest = { completion: "", top: latest.top }; render(); schedule(0); }
function schedule(delay) {
clearTimeout(timer);
timer = setTimeout(run, delay);
}
function run() {
var id = ++seq, text = promptEl.value;
if (!text.trim()) { latest = { completion: "", top: [] }; statusEl.textContent = ""; render(); return; }
statusEl.textContent = "thinking...";
fetch("/api/complete", { method: "POST", headers: { "content-type": "application/json" }, body: JSON.stringify({ prompt: text, topK: 5 }) })
.then(function (r) { return r.json(); })
.then(function (r) {
if (id !== seq || promptEl.value !== text) return; // stale
if (r.error) { statusEl.textContent = r.error; return; }
latest = r;
statusEl.textContent = r.ms + " ms";
render();
})
.catch(function (e) { statusEl.textContent = String(e); });
}
promptEl.addEventListener("input", function () { latest = { completion: "", top: [] }; render(); schedule(200); });
promptEl.addEventListener("keydown", function (e) {
if (e.key === "Tab" && latest.completion) { e.preventDefault(); append(latest.completion); }
else if (e.key === "Enter") {
e.preventDefault();
var t = latest.top[0];
if (t && t.id !== 0) append(t.token);
}
});
EXAMPLES.forEach(function (ex) {
var b = document.createElement("button");
b.className = "ex"; b.textContent = ex;
b.onclick = function () { promptEl.value = ex; latest = { completion: "", top: [] }; render(); schedule(0); promptEl.focus(); };
document.getElementById("examples").appendChild(b);
});
fetch("/api/info").then(function (r) { return r.json(); }).then(function (i) {
document.getElementById("info").textContent = "A compiled (not trained) Llama-format model: " + i.params.toLocaleString() +
" parameters, " + i.vocab.toLocaleString() + " words, " + i.neurons.toLocaleString() + " n-gram neurons. Running in plain TypeScript.";
});
render();
</script>
</body>
</html>
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