---
license: apache-2.0
language:
- en
tags:
- text-generation
- causal-lm
- chatml
- from-scratch
- hydrion
- opengcm
pipeline_tag: text-generation
---



![image](https://cdn-uploads.huggingface.co/production/uploads/69c842686cf758859915159c/KRCENFQ0W7H9B2GruxyA9.png)

Hydrion is a 114M-parameter causal language model, pretrained from scratch and fine-tuned for chat, built on a single RTX 3060 plus a handful of rented A100 hours.

This repo (`OpenGCM/Hydrion-Base`) is the base model, non-chat ready version. The instruction model (chat formatting) is available at [`OpenGCM/Hydrion-SFT`](https://huggingface.co/OPENGCM/Hydrion-SFT).
## Model Details

- **Architecture:** Llama-style decoder-only transformer (RMSNorm, rotary position embeddings, SwiGLU MLP, grouped-query attention)
- **Parameters:** 114.1M
- **Layers:** 12
- **Hidden size:** 768
- **Attention heads:** 12 (4 KV heads, GQA)
- **Context length:** 1024 tokens
- **Tokenizer:** [`EleutherAI/gpt-neox-20b`](https://huggingface.co/EleutherAI/gpt-neox-20b)
- **License:** Apache 2.0

## Training

Hydrion was trained in two pretraining stages.

1. **Initial pretraining** — ~2B tokens on a FineWeb-Edu / Wikipedia mix, trained on a single RTX 3060 (12GB).
2. **Continued pretraining** — an additional ~0.5B tokens on a more diverse mix (FineWeb-Edu, Wikipedia, TinyStories, a code subset, and Dolly), run on a rented A100 to broaden register and topic coverage beyond pure web/encyclopedic text.

Total pretraining exposure: roughly **2.5 billion tokens**.

## Benchmarks

Evaluated with [`lm-evaluation-harness`](https://github.com/EleutherAI/lm-evaluation-harness) on the base (pre-SFT) checkpoint:

| Benchmark | Metric | Score |
|---|---|---|
| BLiMP | acc | 80.08% |
| ARC-Easy | acc | 47.26% |
| ARC-Easy | acc_norm | 43.39% |
| WikiText-2 | byte_perplexity | 2.04 |
| WikiText-2 | bits_per_byte | 1.03 |
| WikiText-2 | word_perplexity | 45.02 |

Grammatical judgment (BLiMP) is comparable to models trained on far larger token budgets; factual/reasoning performance (ARC-Easy) is meaningfully weaker, consistent with the relatively small pretraining corpus.

## Usage

```python
import torch
from transformers import AutoTokenizer, LlamaForCausalLM

tokenizer = AutoTokenizer.from_pretrained("OPENGCM/Hydrion-Base")
model = LlamaForCausalLM.from_pretrained("OPENGCM/Hydrion-Base", torch_dtype=torch.bfloat16).cuda()
model.eval()

prompt = "What is the capital of"
inputs = tokenizer(prompt, return_tensors="pt").to("cuda")

with torch.no_grad():
    output = model.generate(
        **inputs,
        max_new_tokens=150,
        do_sample=True,
        temperature=0.7,
        top_p=0.9,
        repetition_penalty=1.3,
        no_repeat_ngram_size=3,
        eos_token_id=tokenizer.convert_tokens_to_ids("<|im_end|>"),
    )

response = tokenizer.decode(output[0][inputs["input_ids"].shape[1]:], skip_special_tokens=True)
print(response)
```

## Limitations

Hydrion is a small model trained on a modest token budget (~2.5B tokens, versus the trillions used by comparable production small models). It should **not** be relied on for factual accuracy. It reliably produces fluent, grammatically well-formed English and responds in a conversational chat format, but frequently states incorrect facts, fabricates names/dates/attributions, and performs poorly at arithmetic and multi-step reasoning. Treat outputs as unreliable by default — this model is best understood as a demonstration of a working from-scratch training pipeline rather than a usable knowledge source or assistant.