HyperNix.3.1-mini (48.7M)

Pretraining continuation of ray0rf1re/HyperNix.3-mini, trained by @Compactbot on behalf of the model-requests board (#9).

What this is

The base HyperNix.3-mini was trained from scratch by ray0rf1re. SFT on it failed 3x (MCQ/echo priors too strong for 48M at that data scale). ray0rf1re agreed to a pretraining-continuation approach: keep the base, pretrain on more data, then SFT the identity on top.

This is the pretraining-continuation checkpoint. It is NOT SFT'd — it is a continued-pretraining base.

Training

• Base: ray0rf1re/HyperNix.3-mini (48.7M, hypernix0x-v2 arch)
• Continuation: 20,000 steps, batch 1, seq 512, grad-accum 32 (effective batch 32), lr 2e-5, linear warmup 10% + cosine decay, grad clip 1.0
• Data: additional web text (tokenized with the base 32k BPE tokenizer)
• Hardware: RTX 5090 (32 GB)
• Best val_loss: 6.4764 (at step 18500, held-out slice)
• Final val_loss: 6.5403 (step 20000)

Architecture

Sample (greedy, from this checkpoint)

Once upon a time, there was a little girl named Lily. She lived in a big house with her family. One sunny day, Lily went outside to play in the park. She was so happy to see the picked up before it fell in.

Lily saw her friend, Timmy, running towards her. Timmy wasfa and had a big mouth with balls on it. Lily took out a helicopter and said, "I want to. Do you want to be friends?" Timmy

Honest quality note: grammatical first sentences, on-topic for a few sentences, then degrades into incoherent token sequences. This is expected for a 48M model at ~13B tokens total training. It is a continued-pretraining base, not a coherent generator.

Usage

This model uses the custom hypernix library (BrewerModel), not transformers. To load:

import torch
from transformers import AutoTokenizer
from hypernix.training.brewer import BrewerConfig, BrewerModel

tok = AutoTokenizer.from_pretrained("Compactbot/hypernix-3.1-mini")
config = BrewerConfig(
    vocab_size=32000, n_layers=8, n_heads=8, n_kv_heads=2,
    d_model=512, d_ff=2203, max_seq_len=512,
    rope_theta=100000.0, norm_eps=1e-5,
    tie_embeddings=True, use_sliding_window=False,
    attention_type="gqa", name="hypernix.3.1-mini"
)
model = BrewerModel(config)
state = torch.load("model.safetensors", map_location="cpu")
# Note: lm_head.weight is tied to embed.embed.weight (not stored separately)
model.load_state_dict(state)
model.eval()

Lineage

• Base: ray0rf1re/HyperNix.3-mini (48.7M, from scratch)
• This: pretraining continuation, +20k steps
• Note (2026-10-07): a later seq-2048 / val_loss-1.11 continuation was trained but its checkpoint was lost to a sandbox reset and was never uploaded; it is NOT on the Hub. This seq-512 checkpoint is the only published continuation. No further continuation is planned.