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OPENGCM/
Hydrion-v1-SFT

Name: Hydrion-v1-SFT
Availability: InStock

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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-SFT) is the instruction-tuned, chat-ready version. The base pretrained checkpoint (no chat formatting) is available at OpenGCM/Hydrion-Base.

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 BPE tokenizer, extended with <|im_start|> / <|im_end|> special tokens for ChatML formatting
License: Apache 2.0

Training

Hydrion was trained in two pretraining stages followed by supervised fine-tuning:

Initial pretraining — ~2B tokens on a FineWeb-Edu / Wikipedia mix, trained on a single RTX 3060 (12GB).
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.
Supervised fine-tuning — full-parameter fine-tune (no LoRA) on databricks/databricks-dolly-15k, formatted as ChatML conversations with loss masked to the assistant's response tokens only.

Total pretraining exposure: roughly 2.5 billion tokens.

Benchmarks

Evaluated with 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-SFT")
model = LlamaForCausalLM.from_pretrained("OPENGCM/Hydrion-SFT", torch_dtype=torch.bfloat16).cuda()
model.eval()

prompt = "<|im_start|>user\nWhat is the capital of France?<|im_end|>\n<|im_start|>assistant\n"
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.

OPENGCM/Hydrion-v1-SFT