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README.md

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+---
+title: Opus Research
+---
+
+# Opus Research
+
+Independent research on small language models — pretraining from scratch,
+post-training, and the practical engineering around getting models to run on
+constrained hardware.
+
+We publish weights, datasets, and methodology, including the parts that did not
+work.
+
+## Models
+
+**[opus-1.5](https://huggingface.co/opus-research/opus-1.5)** — 0.88B parameter
+language model pretrained from scratch on 2× RTX 4090. Trained in 42 hours;
+energy use documented in the model card.
+
+**[opus-2.0](https://huggingface.co/opus-research/opus-2.0)** — successor model,
+targeting a larger parameter count. **Training is currently paused: we do not
+have the compute budget to continue.** The architecture and data pipeline are
+ready; the run is blocked on GPU access rather than on research.
+
+**[gemma-2-2b-thinking](https://huggingface.co/opus-research/gemma-2-2b-thinking)**
+— experiment in adding explicit chain-of-thought to a base model that lacks it.
+LoRA on Gemma 2 2B, trained on our own reasoning dataset. Training loss 3.56 →
+0.69 over 566 steps. Exploratory; not benchmarked.
+
+**[bernard-gpt-oss-20b-lora](https://huggingface.co/opus-research/bernard-gpt-oss-20b-lora)**
+— style-transfer LoRA for gpt-oss-20b, a 21B mixture-of-experts model. Notable
+mainly for the training notes: a four-run ablation showing that the checkpoint
+with the *worst* validation loss was the correct one to ship, plus a set of
+gpt-oss-specific engineering gotchas we could not find documented elsewhere.
+
+## Datasets
+
+**[opus-thinking-10k](https://huggingface.co/datasets/opus-research/opus-thinking-10k)**
+— ~9,000 chain-of-thought examples, used to train `gemma-2-2b-thinking`.
+
+**[opus-thinking](https://huggingface.co/datasets/opus-research/opus-thinking)**
+— earlier iteration of the same dataset.
+
+## What we work on
+
+- Pretraining small models end-to-end on consumer hardware
+- Post-training: LoRA, style transfer, capability injection
+- Dataset construction and filtering methodology
+- Quantization and CPU inference for models that would otherwise need a
+  datacenter
+
+Our constraint is compute, not ideas. Everything above was produced on consumer
+GPUs, and the ceiling that imposes is the main thing limiting what we publish
+next.
+
+## Contact
+
+Open to collaboration, compute partnerships, and grant programs. Reach us
+through the organization page.