Add DPO dataset card

README.md

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+---
+license: apache-2.0
+task_categories:
+  - text-generation
+tags:
+  - forge-3b
+  - dpo
+  - preference-pairs
+  - direct-preference-optimization
+---
+
+# FORGE-3B DPO Preference Data
+
+Tokenized (prompt, chosen, rejected) preference triples for DPO post-training
+of FORGE-3B, built per the FORGE paper Section 6.2 / Appendix A.2.
+
+**This is data preparation output only — no model was trained to produce this.**
+
+## Stats
+- **Total pairs**: 0 (paper target: ~200,000)
+- **Domains**: 0/4
+- **Context length**: 4096 tokens (paper Appendix A.2, DPO block)
+- **Format**: unpacked — one (prompt, chosen, rejected) triple per training example
+- **Chat template**: `<|SYS|>...<|/SYS|>` `<|USR|>...<|/USR|>` `<|ASST|>...<|/ASST|>` (identical to SFT)
+- **Tokenizer**: CRAYON (xerv-crayon) ONLY — no fallback tokenizer is used, since
+  DPO requires exact token-id alignment with the frozen SFT reference model
+  (paper Sec 6.2)
+
+## Domain Breakdown
+
+| Domain | Pairs | Sources |
+|:-------|------:|:--------|
+| ultrafeedback | — | ✗ |
+| helpsteer2 | — | ✗ |
+| hh_rlhf_helpful | — | ✗ |
+| hh_rlhf_harmless | — | ✗ |
+
+## Usage
+
+```python
+import numpy as np
+from huggingface_hub import hf_hub_download
+
+path = hf_hub_download(
+    repo_id="Phase-Technologies/forge-3b-dpo-data",
+    filename="ultrafeedback/train_shard_0000.npz",
+    repo_type="dataset",
+)
+data = np.load(path, allow_pickle=True)
+chosen_full_ids = data["chosen_full_ids"]      # object array of int32 arrays
+rejected_full_ids = data["rejected_full_ids"]   # object array of int32 arrays
+chosen_loss_mask = data["chosen_loss_mask"]     # 1 = completion token (compute logprob here)
+rejected_loss_mask = data["rejected_loss_mask"]
+```
+
+## Per FORGE paper Section 6.2 DPO config
+- β = 0.1
+- lr = 5e-7 (constant)
+- batch = 32 preference pairs/step
+- grad_clip = 0.3
+- reference_model = frozen SFT checkpoint