README.md

metadata

title: intellite-100m
emoji: 💬
colorFrom: blue
colorTo: purple
sdk: gradio
sdk_version: 5.34.2
app_file: app.py
pinned: false

intellite-100M — RLHF data collector

Serves the SFT-tuned intellite 100M model in a chat UI. Every assistant reply gets 👍 / 👎 buttons; each rating appends one JSONL record to a local folder that a CommitScheduler pushes to a dataset repo on the Hub every 5 minutes.

Setup

1. Upload the SFT checkpoint to the Space root as best.pt (or set INTELLITE_CKPT=/path/to/file.pt in Settings → Variables).
2. Create the dataset repo ProCreations/Intellite-storage (the scheduler will auto-create it on first push too).
3. Set HF_TOKEN in Settings → Secrets — a token with write scope on the dataset repo. Without it, the Space runs but feedback only persists in-memory until the container restarts.
4. (Optional) Override FEEDBACK_REPO in Settings → Variables if you want to use a different dataset repo.

Data format

Each record is a single line of JSONL in data/data_<uuid>.jsonl on the dataset repo (one file per Space replica/restart):

{"ts":"2026-04-20T15:23:45","system":"You are a helpful, honest, and concise assistant.","prompt_messages":[{"role":"user","content":"..."},{"role":"assistant","content":"..."},{"role":"user","content":"..."}],"response":"...","liked":true}

Each record is exactly (prompt, response, reward∈{0,1}) — the shape any preference/RL trainer expects. For DPO, group records by identical prompt_messages and pair a liked=true response (chosen) with a liked=false one (rejected). For REINFORCE/PPO, feed liked as a reward.

Downloading the data

hf download ProCreations/Intellite-storage --repo-type=dataset --local-dir ./rlhf-data
# or in Python:
#   from huggingface_hub import snapshot_download
#   snapshot_download("ProCreations/Intellite-storage", repo_type="dataset")

Notes on the free CPU tier

Generation on CPU is slow (~5–10 tok/s for 100M in fp32). If you move to the paid GPU tier, the app auto-detects cuda and uses bf16 autocast — roughly 10× faster.