Dolphin / Hermes — Uncensored Lineages as Engineering Case Studies
The uncensored model lineages — Eric Hartford's Dolphin series and Nous Research's Hermes 3 — studied as engineering case studies, not advocacy. They are production examples of the FT16–FT18 alignment-control techniques: dataset curation for compliance-over-judgment, full-param SFT+DPO on Llama 3.1, and reasoning-trace training on DeepSeek-R1. The lesson is the recipe and the trade-offs, not the ideology.
These are engineering case studies, not advocacy. Dolphin and Hermes are studied for their recipes (dataset curation, full-param SFT+DPO, reasoning-trace RL) and their trade-offs (capability cost of uncensoring). The course's stance — the model steers, the harness bounds (FT00) — is unchanged: an uncensored model is only responsible inside an eval'd harness.
Hermes 3 (Nous Research, arXiv:2408.11857) is the canonical full-param alignment-control recipe. SFT then DPO on Llama 3.1 at 8B/70B/405B, on primarily synthetic data, producing a 'neutrally-aligned, highly steerable' model. It is a production example of the FT12 (SFT) + FT13 (DPO) + FT16–18 (alignment control) stack at scale.
Dolphin (Eric Hartford, Cognitive Computations) is the compliance-over-judgment philosophy, operationalized. Dolphin3.0-R1-Mistral-24B is the only uncensored model trained on DeepSeek-R1 reasoning traces — it pairs Hartford's refusal-removal philosophy with R1-grade reasoning via FT14-style verifiable-reward RL on reasoning traces.
OpenHermes 2.5 (Teknium, ~1M examples) is the shared dataset backbone. It trained both the OpenHermes 2.5 and Nous Hermes 2 model families and is the textbook example of the course's thesis: the dataset is the steering wheel (FT00). Study the lineage to see how a single high-quality dataset propagates through a family of steered models.