61 lines
2.5 KiB
Markdown
61 lines
2.5 KiB
Markdown
---
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license: apache-2.0
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base_model: paperbd/smollm_135M_neuraltxt_v1
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datasets:
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- paperbd/paper_preference_150K-v1
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library_name: transformers
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pipeline_tag: text-generation
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tags:
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- dpo
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- preference-optimization
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- trl
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- unsloth
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- smollm
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---
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# SmolLM-135M-neuraltxt-dpo-v1
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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.
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- **Base (SFT):** [`paperbd/smollm_135M_neuraltxt_v1`](https://huggingface.co/paperbd/smollm_135M_neuraltxt_v1)
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- **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.
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- **Method:** DPO (Direct Preference Optimization), β=0.1, LoRA r=32, merged to 16-bit.
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## Training
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| | |
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|---|---|
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| Method | DPO (TRL + Unsloth) |
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| beta | 0.1 |
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| LoRA rank / alpha | 32 / 32 |
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| Effective batch | 128 (8 × grad_accum 16) |
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| Max seq / prompt length | 1024 / 768 |
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| Learning rate | 2e-4, linear decay |
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| Epochs | 3 (2,757 steps) |
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| Hardware | 1× RTX 3090, ~6h17m |
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## Evaluation
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Held-out 2% split + diversity on 100 sampled prompts (4 responses × 4 temperatures).
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| Metric | SFT baseline | This model (DPO) |
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|---|---|---|
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| Eval loss | — | **0.457** |
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| Reward accuracy (held-out) | 0.50 (chance) | **0.72** |
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| Reward margin | — | 1.65 |
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| Diversity — EAD | 0.1173 | 0.1193 |
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| Diversity — SBERT | 0.2263 | 0.2322 |
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| Diversity — Vendi | 2.7327 | 2.7410 |
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**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.
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## Intended use & limitations
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- 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.
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- 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.
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- The reward/eval accuracy measures agreement with the LLM judge that created the preference data, so it is **not a fully independent quality signal**.
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## Reproduce
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See [`dpo/DPO_SmolLM135M`](https://github.com/jaydeepraijada/post-training-experiments/tree/main/dpo/DPO_SmolLM135M) (`run_dpo.sh`, `experiments.md`, `LEARNINGS.md`).
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