# Scripts

Automation scripts for dataset generation, fine-tuning, GGUF conversion, llama.cpp runtime, and trace export.

Implemented initial scripts:

- `check_initial_stage.py`: verifies required files, runtime defaults, sample traces, pipeline, and Gradio build.
- `generate_sample_traces.py`: creates six stable public mock traces under `data/traces/samples/`.
- `generate_dataset.py`: creates deterministic SFT preview JSONL for schema and curation planning.
- `prepare_curated_dataset.py`: creates deterministic synthetic curated SFT rows; v1 defaults to 50 rows, v2 defaults to 200 rows across 40 objects and 5 modes.
- `export_traces.py`: exports validated public sample traces to JSONL for dataset-style publishing.
- `check_space_vlm.py`: validates MiniCPM-V object understanding on the hosted Hugging Face Space with three temporary public test images.
- `check_llama_cpp_smoke.py`: smoke-tests the optional llama.cpp text runtime with an external GGUF model.
- `finetune_lora.py`: validates SFT JSONL locally and defines the Modal LoRA training scaffold with optional eval split, assistant-output-only loss, and tunable LoRA/batch settings.
- `publish_hf_dataset.py`: validates and uploads curated JSONL files to a Hugging Face Dataset repository.
- `publish_hf_adapter.py`: uploads a downloaded LoRA adapter folder to Hugging Face Hub.
- `merge_lora_adapter.py`: merges a local PEFT LoRA adapter into a Hugging Face base model and saves tokenizer files.
- `publish_hf_gguf.py`: validates and uploads a local GGUF file to a Hugging Face model repository.

Expected files during implementation:

- `convert_to_gguf.sh`
- `run_llama_cpp.sh`

Modal LoRA dry-run:

```bash
.venv/bin/python -B scripts/finetune_lora.py \
  --dry-run \
  --dataset data/train/objectverse_sft_curated.jsonl \
  --run-name objectverse-diary-qwen15b-curated-test
```

Modal LoRA v2 dry-run for a larger curated dataset:

```bash
.venv/bin/python -B scripts/prepare_curated_dataset.py \
  --version v2 \
  --count 200 \
  --output data/train/objectverse_sft_curated_v2.jsonl
```

Publish curated v2 dataset:

```bash
.venv/bin/python -B scripts/publish_hf_dataset.py \
  --dataset-file data/train/objectverse_sft_curated_v2.jsonl \
  --repo-id qqyule/objectverse-diary-sft-curated \
  --path-in-repo objectverse_sft_curated_v2.jsonl
```

```bash
.venv/bin/python -B scripts/finetune_lora.py \
  --dry-run \
  --dataset data/train/objectverse_sft_curated_v2.jsonl \
  --run-name objectverse-diary-qwen15b-lora-v2 \
  --max-steps 120 \
  --learning-rate 1e-4 \
  --max-seq-length 1536 \
  --lora-r 32 \
  --lora-alpha 64 \
  --per-device-train-batch-size 2 \
  --gradient-accumulation-steps 4 \
  --eval-ratio 0.1 \
  --eval-steps 20
```

Modal LoRA v2 training:

```bash
modal run --timestamps -n objectverse-diary-qwen15b-lora-v2 scripts/finetune_lora.py \
  --dataset data/train/objectverse_sft_curated_v2.jsonl \
  --run-name objectverse-diary-qwen15b-lora-v2 \
  --max-steps 120 \
  --learning-rate 1e-4 \
  --max-seq-length 1536 \
  --lora-r 32 \
  --lora-alpha 64 \
  --per-device-train-batch-size 2 \
  --gradient-accumulation-steps 4 \
  --eval-ratio 0.1 \
  --eval-steps 20
```

For epoch-based experiments, set `--max-steps 0` and provide `--num-train-epochs`.
Assistant-output-only loss is enabled by default; pass `--no-assistant-only-loss` only for debugging full-text loss behavior.

Training dependencies are intentionally separate from the Space runtime:

```bash
pip install -r requirements-training.txt
```

Do not commit Modal credit codes, tokens, Hugging Face tokens, generated adapters, GGUF files, or private datasets.

If `modal run` reports `Token missing`, authenticate outside the repository first:

```bash
modal token new
```

or configure `MODAL_TOKEN_ID` and `MODAL_TOKEN_SECRET` through your shell/secret manager.

After a successful Modal run, download the adapter from the output volume into ignored local exports. Modal's directory download behavior can vary; downloading individual adapter files into a directory is the safest path.

```bash
mkdir -p exports/objectverse-diary-qwen15b-lora-v2-adapter-dir
for file in vocab.json tokenizer_config.json tokenizer.json special_tokens_map.json merges.txt chat_template.jinja added_tokens.json adapter_model.safetensors adapter_config.json README.md; do
  modal volume get objectverse-diary-lora-output \
    "objectverse-diary-qwen15b-lora-v2/adapter/$file" \
    "exports/objectverse-diary-qwen15b-lora-v2-adapter-dir/$file"
done
modal volume get objectverse-diary-lora-output \
  objectverse-diary-qwen15b-lora-v2/metrics.json \
  exports/objectverse-diary-qwen15b-lora-v2-adapter-dir/training_metrics.json
modal volume get objectverse-diary-lora-output \
  objectverse-diary-qwen15b-lora-v2/training_config.json \
  exports/objectverse-diary-qwen15b-lora-v2-adapter-dir/training_config.json
```

Then upload the adapter to Hugging Face Hub:

```bash
.venv/bin/python -B scripts/publish_hf_adapter.py \
  --adapter-dir exports/objectverse-diary-qwen15b-lora-v2-adapter-dir \
  --repo-id qqyule/objectverse-diary-qwen15b-lora \
  --commit-message "Upload Objectverse Diary Qwen 1.5B LoRA v2"
```

LoRA v2 GGUF conversion and upload:

```bash
.venv/bin/python -B scripts/merge_lora_adapter.py \
  --base-model Qwen/Qwen2.5-1.5B-Instruct \
  --adapter exports/objectverse-diary-qwen15b-lora-v2-adapter-dir \
  --output exports/objectverse-diary-qwen15b-lora-v2-merged-hf

git clone https://github.com/ggml-org/llama.cpp.git .tmp/llama.cpp
git -C .tmp/llama.cpp checkout 8f83d6c271d194bde2d410145a0ce73bc42e85cd
cmake -S .tmp/llama.cpp -B .tmp/llama.cpp/build -DCMAKE_BUILD_TYPE=Release
cmake --build .tmp/llama.cpp/build --target llama-quantize -j

.venv/bin/python .tmp/llama.cpp/convert_hf_to_gguf.py \
  exports/objectverse-diary-qwen15b-lora-v2-merged-hf \
  --outfile models/objectverse-diary-qwen15b-lora-v2-f16.gguf \
  --outtype f16

.tmp/llama.cpp/build/bin/llama-quantize \
  models/objectverse-diary-qwen15b-lora-v2-f16.gguf \
  models/objectverse-diary-qwen15b-lora-v2-q4_k_m.gguf \
  Q4_K_M

.venv/bin/python -B scripts/publish_hf_gguf.py \
  --gguf-file models/objectverse-diary-qwen15b-lora-v2-q4_k_m.gguf \
  --repo-id qqyule/objectverse-diary-qwen15b-lora \
  --path-in-repo objectverse-diary-qwen15b-lora-v2-q4_k_m.gguf \
  --commit-message "Upload Objectverse Diary Qwen 1.5B LoRA v2 Q4_K_M GGUF"
```

The final Q4_K_M GGUF is ignored under `models/`. After upload, remove only generated intermediates such as the merged HF folder and F16 GGUF.

Space VLM validation:

```bash
.venv/bin/python -B scripts/check_space_vlm.py \
  --space-url https://huggingface.co/spaces/build-small-hackathon/ObjectverseDiary \
  --output docs/SPACE_VLM_REPORT.md \
  --json-output docs/SPACE_VLM_REPORT.json \
  --failure-notes-output docs/FAILURES.md
```

External Space changes are explicit:

```bash
.venv/bin/python -B scripts/check_space_vlm.py --configure-space --rollback-to-mock
```

Local LoRA v2 GGUF smoke test:

```bash
.venv/bin/python -B scripts/check_llama_cpp_smoke.py \
  --model-path models/objectverse-diary-qwen15b-lora-v2-q4_k_m.gguf
```

Published GGUF source: `qqyule/objectverse-diary-qwen15b-lora`, file `objectverse-diary-qwen15b-lora-v2-q4_k_m.gguf`. Do not commit the downloaded file.

Current status: mock trace generation, trace JSONL export, SFT preview generation, synthetic curated v2 dataset publishing, optional MiniCPM-V wiring, optional llama.cpp wiring, hosted Space VLM validation tooling with non-secret probe support, local GGUF smoke helper, Modal LoRA training scaffolding, Modal LoRA v2 training, HF adapter publishing, GGUF conversion, GGUF upload, and local llama.cpp smoke are implemented. Real text-model validation on Space is not completed yet.