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Model: bfpill/gemma-3-270m-sft-skywork-chosen
Source: Original Platform

README.md

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+---
+library_name: transformers
+base_model: google/gemma-3-270m
+license: gemma
+datasets:
+  - Skywork/Skywork-Reward-Preference-80K-v0.2
+language:
+  - en
+tags:
+  - gemma3
+  - sft
+  - research
+  - skywork
+pipeline_tag: text-generation
+---
+
+# gemma-3-270m SFT'd on Skywork-Reward-Preference-80K (chosen-only)
+
+A response-only SFT of [`google/gemma-3-270m`](https://huggingface.co/google/gemma-3-270m)
+on the **chosen** responses of
+[`Skywork/Skywork-Reward-Preference-80K-v0.2`](https://huggingface.co/datasets/Skywork/Skywork-Reward-Preference-80K-v0.2).
+
+This is the anchor checkpoint used as the SFT reference policy for a research
+project on **data-space DPO** — applying preference signal by reweighting SFT
+samples through a susceptibility matrix, instead of via DPO's weight-space
+gradient descent. It is **not** intended as a useful chat model — at 270M
+parameters and a single epoch of SFT, generations are at best "coherent text
+on the topic of the prompt". The role of this checkpoint is to be a
+controlled, reproducible anchor for sampling-based experiments.
+
+## Training recipe
+
+| | |
+|---|---|
+| Base | `google/gemma-3-270m` (pretrained, no chat template) |
+| Data | `Skywork/Skywork-Reward-Preference-80K-v0.2`, chosen responses only (~77K examples) |
+| Epochs | 1 |
+| Learning rate | 2e-5, cosine schedule, 3% warmup |
+| Batch size | 16 per device × 4 GPUs = 64 effective |
+| Precision | bf16 + gradient checkpointing |
+| Loss | response-only cross-entropy (prompt tokens masked to -100) |
+| Total steps | 602 |
+| Final train loss | 1.41 |
+| Wall time | ~4 min on 8× H100 |
+
+## Prompt format
+
+The base model has no `chat_template`, so we use a fixed plain-text format
+matching the SFT distribution:
+
+```text
+Human: <user message>
+Assistant: ASSISTANT_RESPONSE
+```
+
+Internally the prompt prefix is `"Human: <user>\n\nAssistant:"` and the loss is
+computed only on the tokens following `Assistant:`.
+
+## Quick start
+
+```python
+import torch
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+mid = "bfpill/gemma-3-270m-sft-skywork-chosen"
+tok = AutoTokenizer.from_pretrained(mid)
+model = AutoModelForCausalLM.from_pretrained(mid, torch_dtype=torch.bfloat16).cuda().eval()
+
+prompt = f"Human: What is the capital of France?\n\nAssistant:"
+ids = tok(prompt, return_tensors="pt").to(model.device)
+out = model.generate(
+    **ids, max_new_tokens=80, do_sample=False,
+    repetition_penalty=1.15, pad_token_id=tok.eos_token_id,
+)
+print(tok.decode(out[0, ids.input_ids.shape[1]:], skip_special_tokens=True))
+```
+
+## Limitations
+
+- 270M params, 1 epoch of SFT on ~77K examples. Generations are short and
+  often factually incorrect.
+- Trained exclusively on the Skywork prompt distribution; performance on
+  other distributions (instruction-following, dialogue, code, math)
+  reflects the base model plus a thin Skywork-flavoured veneer.
+- Not preference-tuned. The whole point of this checkpoint is to be the
+  anchor for a follow-up data-space patterning experiment that is the
+  *replacement* for the usual DPO step.
+- Inherits the base model's biases and license restrictions.
+
+## License
+
+Gemma Terms of Use — see [Google's Gemma license](https://ai.google.dev/gemma/terms).
+This SFT preserves the base model's weights structure; usage is subject to
+the same terms.
+
+## Source code
+
+The trainer that produced this checkpoint:
+`rm-patterning/policy/sft_anchor.py` in
+`timaeus-research/timaeus@max/rm-scaling` (commit `3a3e0755e` and later).
+
+```bash
+torchrun --nproc_per_node=8 rm-patterning/policy/sft_anchor.py \
+    --base-model google/gemma-3-270m \
+    --output-dir ./gemma-3-270m-sft-skywork-chosen \
+    --epochs 1 --learning-rate 2e-5 --batch-size 16 --max-length 1024
+```