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Model: Praneshrajan15/DataForge-0.5B-SFT
Source: Original Platform

README.md

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+---
+license: apache-2.0
+base_model: Qwen/Qwen2.5-0.5B-Instruct
+library_name: transformers
+tags:
+- dataforge
+- data-quality
+- supervised-fine-tuning
+- qlora
+- kaggle
+datasets:
+- Praneshrajan15/dataforge-sft-trajectories
+metrics:
+- f1
+model-index:
+- name: DataForge-0.5B-SFT
+  results:
+  - task:
+      type: text-generation
+      name: DataForge repair planning
+    dataset:
+      name: DataForge SFT trajectories
+      type: Praneshrajan15/dataforge-sft-trajectories
+    metrics:
+    - type: macro_f1
+      name: Held-out macro F1
+      value: null
+---
+
+# DataForge-0.5B-SFT
+
+DataForge-0.5B-SFT is a supervised-fine-tuned warmup checkpoint for tabular
+data-quality repair experiments. The current training path uses chunk-level
+DataForge expert trajectories whose exact repairs are derived from audited
+dirty/clean CSV diffs. The earlier `v0-smoke` release only proved the
+Kaggle-to-Hugging-Face pipeline and should not be read as a performance claim.
+
+## Intended Use
+
+- Research on tabular data-quality agents and repair planning.
+- Offline evaluation on DataForge-Bench-style Hospital, Flights, and Beers
+  tasks.
+- Warm-starting later DataForge RL experiments.
+
+This checkpoint is not intended for autonomous production data modification,
+medical decision support, regulated data governance, or unsupervised repair of
+private datasets.
+
+## Training Data
+
+- Dataset repo: `Praneshrajan15/dataforge-sft-trajectories`.
+- Dataset repo SHA used for this run: `94e2dd556d4f1260c5123d93ca6bf4f9da9b160a`.
+- Training examples: `1958` chunk-level `expert_v4.jsonl`
+  records.
+- Data sources: Raha benchmark Hospital, Flights, and Beers datasets via the
+  BigDaMa/raha repository.
+- Primary label source: `oracle_from_clean_diff` dirty/clean CSV diffs.
+- Legacy teacher lineage: Groq-hosted `clean-diff-v1` ReAct smoke records may
+  remain for auditability, but exact repairs are not teacher-discovered labels.
+- Flights schedule and actual-time repairs are supervised from dirty/clean
+  labels; they are not inferred from incomplete prompt context.
+- Split safety: held-out rows are reserved before chunking and excluded from SFT
+  target rows, context rows, normalization candidates, fixes, and messages.
+- Hard negatives: clean train chunks are retained as `finish` examples with
+  empty repairs so the model is penalized for unnecessary edits.
+
+The trajectory JSONL includes state, tool calls, diagnosis text, proposed fixes,
+teacher/oracle metadata, benchmark metrics, split metadata, and source
+provenance for auditability.
+
+## Training Procedure
+
+- Base model: `Qwen/Qwen2.5-0.5B-Instruct`.
+- Method: 4-bit QLoRA warmup, then LoRA merge into fp16 merged weights.
+- Compute target: Kaggle or Hugging Face remote GPU only; no laptop model
+  training or full evaluation.
+- Kaggle hours used: `1.147`.
+- Epochs: 2.
+- Batch size: 1 per device with gradient accumulation of 16.
+- Learning rate: 2e-5.
+
+## Evaluation
+
+Evaluation is reported on held-out DataForge-Bench-style tasks sampled after the
+training trajectory seeds. The release status generated by the notebook is
+`quality_improved_verified`. Only `quality_improved_verified` should be treated as a
+quality milestone. `diagnostic_complete_no_gain` means the run is authentic and
+published, but not promoted.
+
+| Model | Held-out macro F1 |
+| --- | ---: |
+| `Qwen/Qwen2.5-0.5B-Instruct` | `0.0` |
+| `DataForge-0.5B-SFT` | `0.0077` |
+
+Release gates:
+
+- Parse success: `1.0`.
+- Schema-case errors: `0`.
+- Quality milestone: `True`.
+
+These numbers are produced by the publishing notebook and should not be edited
+manually. Re-run the notebook to regenerate them. Detailed per-dataset metrics
+are stored in `training_metrics.json` under `base_eval` and `sft_eval`.
+Bounded per-task failure evidence is stored in `eval_diagnostics.json`.
+
+## Limitations
+
+- The checkpoint is a Week 9 warmup model, not the final DataForge model family.
+- It has only seen small chunk-level ReAct traces and may fail on larger schemas,
+  unseen domains, adversarial dirty values, or tasks requiring multi-step
+  database access.
+- Legacy teacher traces can contain teacher errors; the primary current labels
+  come from exact dirty/clean diffs.
+- The model should be used behind DataForge's safety, verifier, and transaction
+  layers before any real data changes.
+
+## License
+
+Weights are published as `apache-2.0` after verifying the base model
+metadata for `Qwen/Qwen2.5-0.5B-Instruct`. Users must also comply with the source dataset
+licenses/terms and the teacher model terms that governed trajectory generation.