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Model: narinzar/dpo-finetune-demo
Source: Original Platform

README.md

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+---
+license: mit
+base_model: gpt2
+library_name: transformers
+pipeline_tag: text-generation
+tags:
+- dpo
+- preference-tuning
+- gpt2
+- text-generation
+---
+
+# dpo-finetune-demo
+
+A small `gpt2` policy fine-tuned with **Direct Preference Optimization (DPO)**
+against a frozen `gpt2` reference. It is a compact, self-contained demo of the
+full DPO pipeline: build preference pairs from a base model's own samples,
+train a from-scratch DPO loss against a frozen reference, and measure win-rate
+before vs after.
+
+Code: https://github.com/narinzar/dpo-finetune-demo
+
+## What this is
+
+- **Base model:** `gpt2` (124M).
+- **Method:** DPO loss implemented from scratch (`src/dpo.py`), policy trained
+  against a frozen reference copy of `gpt2`.
+- **Preference data:** 358 pairs, **auto-labeled by a transparent reward
+  heuristic** (keyword presence + politeness + conciseness in `src/reward.py`),
+  not by humans. Candidates are sampled from the base `gpt2` itself.
+- **Target property:** polite, concise answers that contain the keyword
+  `please`.
+
+This upload is the **full fine-tuned model** (safetensors + tokenizer), loadable
+directly with `transformers`.
+
+## Results (real, small-scale)
+
+Measured on a single RTX 5090 (24 GB). 286 train / 72 eval pairs, `beta=0.1`,
+`lr=1e-5`, batch size 8, 3 epochs.
+
+| Metric                              | Value  |
+| ----------------------------------- | ------ |
+| Win-rate before DPO (policy vs ref) | 0.500  |
+| Win-rate after DPO (policy vs ref)  | 0.812  |
+| Final DPO loss                      | 0.199  |
+
+Win-rate is judged by the same reward heuristic that labeled the pairs, so the
+dataset and the metric are aligned. Before training the policy is a copy of the
+reference, so win-rate sits at 0.500; after DPO it rises to 0.812. This is a
+small-scale demo, so the effect size is bounded by the tiny model and dataset;
+the point is the mechanism and the before/after direction, not a headline score.
+
+## Usage
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+tok = AutoTokenizer.from_pretrained("narinzar/dpo-finetune-demo")
+model = AutoModelForCausalLM.from_pretrained("narinzar/dpo-finetune-demo")
+
+prompt = "How do I reset my password?"
+ids = tok(prompt, return_tensors="pt")
+out = model.generate(**ids, max_new_tokens=48, pad_token_id=tok.eos_token_id)
+print(tok.decode(out[0], skip_special_tokens=True))
+```
+
+## Limitations
+
+- Inherits all of `gpt2`'s limitations and biases.
+- Preferences are defined by a heuristic reward proxy, not human judgment, so the
+  model optimizes for that proxy (keyword + politeness + conciseness), which is a
+  narrow and gameable target.
+- Small scale: outputs are still short and repetitive; treat this as an
+  educational artifact, not a production assistant.
+
+## License
+
+MIT.