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Model: JaydeepR/SmolLM-135M-neuraltxt-dpo-v1
Source: Original Platform

README.md

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+---
+license: apache-2.0
+base_model: paperbd/smollm_135M_neuraltxt_v1
+datasets:
+  - paperbd/paper_preference_150K-v1
+library_name: transformers
+pipeline_tag: text-generation
+tags:
+  - dpo
+  - preference-optimization
+  - trl
+  - unsloth
+  - smollm
+---
+
+# SmolLM-135M-neuraltxt-dpo-v1
+
+Preference-tuned (DPO) version of the SFT'd SmolLM-135M ML-paper research assistant. This is **stage 3** of a CPT → SFT → DPO pipeline on a 135M-parameter model.
+
+- **Base (SFT):** [`paperbd/smollm_135M_neuraltxt_v1`](https://huggingface.co/paperbd/smollm_135M_neuraltxt_v1)
+- **Preference data:** [`paperbd/paper_preference_150K-v1`](https://huggingface.co/datasets/paperbd/paper_preference_150K-v1) — pairs mined by sampling the SFT model and ranking responses with an LLM judge.
+- **Method:** DPO (Direct Preference Optimization), β=0.1, LoRA r=32, merged to 16-bit.
+
+## Training
+
+| | |
+|---|---|
+| Method | DPO (TRL + Unsloth) |
+| beta | 0.1 |
+| LoRA rank / alpha | 32 / 32 |
+| Effective batch | 128 (8 × grad_accum 16) |
+| Max seq / prompt length | 1024 / 768 |
+| Learning rate | 2e-4, linear decay |
+| Epochs | 3 (2,757 steps) |
+| Hardware | 1× RTX 3090, ~6h17m |
+
+## Evaluation
+
+Held-out 2% split + diversity on 100 sampled prompts (4 responses × 4 temperatures).
+
+| Metric | SFT baseline | This model (DPO) |
+|---|---|---|
+| Eval loss | — | **0.457** |
+| Reward accuracy (held-out) | 0.50 (chance) | **0.72** |
+| Reward margin | — | 1.65 |
+| Diversity — EAD | 0.1173 | 0.1193 |
+| Diversity — SBERT | 0.2263 | 0.2322 |
+| Diversity — Vendi | 2.7327 | 2.7410 |
+
+**Takeaways:** the model learned the preference (reward accuracy 0.50 → 0.72) while **preserving output diversity** (no mode collapse — all diversity metrics flat vs the SFT baseline). Training shows mild overfitting (train reward accuracy ~0.85 vs eval 0.72), so 3 epochs is the right length.
+
+## Intended use & limitations
+
+- Same scope as the SFT base: a **structured ML-paper research assistant**, not a general chatbot. Best used via the `PaperResearcher` task API from the SFT stage.
+- At 135M parameters the model is **capacity-limited** — it learns task *shape* and preference, not deep factual recall. DPO sharpens which response style is preferred; it does not add knowledge.
+- The reward/eval accuracy measures agreement with the LLM judge that created the preference data, so it is **not a fully independent quality signal**.
+
+## Reproduce
+
+See [`dpo/DPO_SmolLM135M`](https://github.com/jaydeepraijada/post-training-experiments/tree/main/dpo/DPO_SmolLM135M) (`run_dpo.sh`, `experiments.md`, `LEARNINGS.md`).