README.md

metadata

title: SLM 264M Data Transformer
emoji: 🔄
colorFrom: blue
colorTo: indigo
sdk: gradio
sdk_version: 5.9.1
app_file: app.py
pinned: false

SLM-264M Data Transformer — demo Space

A Gradio front-end for the slm-264m data-transformer fine-tune: convert data between CSV, JSON, and XML with a 264M-parameter model trained from scratch, running on CPU.

How it's wired

• Code lives here (app.py, model.py).
• Weights live in the model repo and are downloaded at startup with huggingface_hub.hf_hub_download. Set the repo in app.py:

  MODEL_REPO = "uday210/slm-recipe-264m"   # your fine-tune
  CKPT_FILENAME = "slm-recipe-final.pt"
  TOKENIZER_FILENAME = "tokenizer.json"
  

• If the model repo is private, add an HF_TOKEN secret under Settings → Variables and secrets.

Files you must add

1. model.py — copy it from your training repo. This Space cannot load the checkpoint without your exact GPT/GPTConfig classes (custom GQA + RoPE architecture). It must be import-safe (no training at file top level).
2. Make sure the model repo actually contains the checkpoint and tokenizer.json.

Notes / limits

• CPU inference: a few tok/s. Fine for a demo. For speed, upgrade the Space hardware or wire in the KV-cache generation path from your deployment notes.
• The prompt template in app.py must match how the fine-tune was trained.
• Numeric transcription is the model's known weak spot; structure and text are reliable.