HTLM (HyperText Language Model) is a fine-tuned LFM2.5-350M that predicts web UI actions — click, type, select — on an indexed element list, entirely in-browser via wllama WebAssembly.

Benchmark Results

Metric	Value
Strict action accuracy	91.2%
Action type accuracy	92.2%
Element index accuracy	99.8%
Parse failure rate	0.0%
p95 latency (browser WASM)	1245 ms
Model size (Q8 GGUF)	362 MB
Base model (no fine-tune)	0.2%

Evaluated on 408 held-out tasks from Mind2Web. Full evaluation details: docs/go-no-go-checklist.md.

How It Works

HTLM takes a structured page representation (element list with role/tag/text) and an instruction, and predicts {type, index, [value]}. The element index refers to the candidate list derived from the page HTML.

HTML → element list → HTLM → {type, index, value?}

Usage

Browser (wllama)

javascript

import { Wllama } from '@wllama/wllama';

const wllama = new Wllama({ default: './wllama.wasm' });
await wllama.loadModelFromHF({
  repo: 'espetro/htlm-lfm2.5-350m',
  file: 'htlm-350m-q8.gguf',
});

const result = await wllama.createCompletion({
  prompt: JSON.stringify({
    instruction: "Click the submit button",
    page: { elements: [{role:"button",tag:"button",text:"Submit"}] },
  }),
  max_tokens: 128,
});

llama.cpp CLI

llama-cli -m htlm-350m-q8.gguf -p "[INPUT JSON]" -n 128 --temp 0

mlx-lm (Apple Silicon)

python

from mlx_lm import load, generate
model, tokenizer = load('espetro/htlm-lfm2.5-350m')
# LoRA merge required first — see GitHub repo

Training

Fine-tuned via LoRA (rank 16) on Mind2Web using the mlx-lm / Unsloth-compatible API on Apple Silicon. Full pipeline, hyperparameters, and reproducibility steps: docs/pipeline.md.

Repository

https://github.com/espetro/HTLM