SPACE_RUNBOOK.md

Hugging Face token (never commit real values)

1. On https://huggingface.co/settings/tokens create a token with write access.
2. In the Space: Settings → Repository secrets add:
   • Name: HF_TOKEN (or HUGGINGFACE_HUB_TOKEN)
   • Value: your token
3. Restart the Space so the env is applied.
4. If a token was ever pasted in chat or committed, revoke it and create a new one.

Check disk writes + that HF token is visible to the app

Pings the Space and creates outputs/simpllll.csv (overwrites on each call). JSON includes hf_token_env_set: true/false (never the secret). After redeploy, run:

curl -sS "https://hiitsesh-new-gpu-space.hf.space/outputs/write_test"

Why the CSV does not appear on huggingface.co → Files / outputs/: that view is the git repository (committed files). The probe file is written on the running container only. Use the download_url from the JSON, or:

curl -sS "https://hiitsesh-new-gpu-space.hf.space/outputs/file?path=simpllll.csv"

To list runtime files: GET /outputs/ls (or open it in the browser).

Train (saves best-by-loss to outputs/best_by_loss)

Replace the Space host if yours differs. model_name must be URL-encoded (/ → %2F).

curl -sS -X POST \
  "https://hiitsesh-new-gpu-space.hf.space/train/pilot?max_steps=100&num_generations=8&gradient_accumulation_steps=8&max_completion_length=512&learning_rate=1e-5&logging_steps=5&model_name=Qwen%2FQwen3-1.7B&bf16=true&best_loss_dir=outputs%2Fbest_by_loss"

Optional: use a separate folder per run, e.g. best_loss_dir=outputs%2Fbest_1.7b_2026-04-26.

Automatic upload when training finishes (recommended)

Create an empty model repo on the Hub first (or it will be created on first push). With HF_TOKEN set as a Space secret, training can push after trainer.train() finishes so weights are not lost if the machine restarts.

• hub_model_repo — your repo id, URL-encoded (/ → %2F), e.g. YOUR_USER%2Freleaseops-grpo-1.7b
• hub_upload_include — best (default, outputs/best_by_loss → best_by_loss/ on Hub), final (trainer output only → trainer_final/), or both

Example (append to the same pilot URL as &hub_model_repo=...&hub_upload_include=both):

&hub_model_repo=YOUR_USER%2Freleaseops-grpo-1.7b&hub_upload_include=both

Why not GitHub for the model files? Fine for code; multi‑GB checkpoints hit LFS limits and are painful. Hugging Face Hub (or S3/GCS) is built for model weights. You can still keep your code on GitHub and weights on the Hub.

After training: push finetuned weights to the Hub (manual, same as automatic)

Use a model repo (recommended) so you can from_pretrained later. Replace YOUR_USER and repo name.

curl -sS \
  "https://hiitsesh-new-gpu-space.hf.space/train/push_to_hub?repo_id=YOUR_USER%2Freleaseops-grpo-1.7b-best&repo_type=model&path=best_by_loss"

• path is relative to outputs/, so best_by_loss means outputs/best_by_loss/.
• To push the full trainer run dir: path=releaseops-grpo (if present).

Download without Hub (ephemeral)

curl -sS -o best_by_loss.tar.gz "https://hiitsesh-new-gpu-space.hf.space/outputs/archive?path=best_by_loss"

Use the finetuned model later (Python)

from transformers import AutoModelForCausalLM, AutoTokenizer

path = "YOUR_USER/releaseops-grpo-1.7b-best"  # or local folder
tok = AutoTokenizer.from_pretrained(path)
model = AutoModelForCausalLM.from_pretrained(path, device_map="auto")