import { AutoTokenizer, AutoModelForCausalLM } from "@huggingface/transformers";

const tokenizer = await AutoTokenizer.from_pretrained("tomvaillant/qwen3-4b-journalist-ONNX");
const model = await AutoModelForCausalLM.from_pretrained("tomvaillant/qwen3-4b-journalist-ONNX", {
  dtype: "q4f16",
  device: "webgpu",
});

Training

Method: QLoRA with Unsloth / TRL SFT
Data: 687 instruction/response pairs across investigative journalism and OSINT categories
Task: browser-sized assistant for OSINT tool selection, investigation planning, verification, and source handling

ONNX Conversion

Quantization: q4f16
Runtime: Transformers.js via WebGPU
Files: onnx/model_q4f16.onnx, external data files, tokenizer, and chat template

Sources And Attribution

Training data: tomvaillant/investigative-journalism-training — 687 instruction/response pairs synthesized by Claude Opus 4.6 (Anthropic) from the Buried Signals OSINT and investigative-journalism corpus: OSINT Navigator tool data, Indicator Media briefings, Buried Signals investigative skills, GIJN, Bellingcat, Verification Handbook 3, SPJ Code of Ethics, RCFP, and public manuals from UNESCO, Al Jazeera Media Institute, CiFAR, CIPE, and EJF/TEMPO Institute.

See the dataset card for the full source list, licenses, and per-partner attribution.

Intended Use

Use for local/browser investigative assistance. Generated findings, URLs, and legal conclusions require independent verification before reporting or operational use.