OpenGCM
AI & ML interests
Training SLM
Recent Activity
About
OpenGCM (Open Generative Coding Models) is an independent, open-source AI research organization.
Our main focus is Hydrion — a series of language models we pretrain from scratch, exploring what small models can do when you build them yourself instead of fine-tuning someone else's.
We also maintain GCM, a line of code-focused chat models built by fine-tuning existing open-weight models.
Everything we release — weights, training data, and methods — is public.
Hydrion
Our pretrained model series, trained from the ground up rather than fine-tuned.
| Model | Parameters | Type | Status | Link |
|---|---|---|---|---|
| Hydrion v1 | 114M | Base | Released | Hugging Face → |
| Hydrion v1-SFT | 114M | Instruction-tuned | Released | Hugging Face → |
| Hydrion v3 | ~144M | Base | In training | Coming soon |
Hydrion v3 is a from-scratch pretrain built on a curated, leaderboard-informed data mix (FineWeb-Edu, DCLM-Baseline, FineMath, Cosmopedia v2, and more), with a progressive curriculum that ramps math and reasoning data over the course of training.
GCM
Code-focused chat models, fine-tuned on top of existing open-weight base models.
| Model | Parameters | Link |
|---|---|---|
| GCM Mark II | 9B | Hugging Face → |
GCM Mark II scores 74.4% pass@1 on HumanEval and 62.2% pass@1 on MBPP.
Vision
Small models don't have to mean small ambitions. We're building on a few principles:
- Open — every weight, dataset, and training method we use is public, not just the final model
- Efficient — Hydrion is trained on powerful GPUs, but can be ran on consumer GPUs
- Customizable — models built to be forked, fine-tuned, and taken apart
- Accessible — no gatekeeping behind closed weights or paywalled APIs
Research
We're currently focused on:
- Pretraining from scratch — Hydrion, our from-scratch language model series, and the data/architecture decisions that go into it
- Fine-tuning — GCM, our code-focused chat models built on existing open-weight bases
- Evaluation — benchmarking against public leaderboards and tracking what actually moves the needle
- Local deployment — making sure what we build can run on real, non-datacenter hardware
Follow along as it happens in our Research space.
Hardware
Our team has:
| GPU | VRAM | Amount |
|---|---|---|
| RTX 3060 | 12GB | 1* |
| NVIDIA RTX PRO 6000 Blackwell | 96GB | 2 |
* Subject to change
Team
Built by MrGuineaBird, solhost, and contributors.
Sponsored by