NeuralAI Air 135M

A compact, custom-built decoder-only language model created for the NeuralAI ecosystem. Designed to be small, fast, and locally runnable while still useful for inference on commodity hardware.

Model Details

Property Value
Architecture NeuralAI-Air (custom decoder-only Transformer)
Parameters ~135M
Vocabulary size 32,000
Hidden size 768
Layers 15
Attention heads 12
Key/Value heads 2 (GQA)
Intermediate size 2,560
Max position embeddings 2,048
Tie word embeddings true
Torch dtype float32

Tokenizer

The tokenizer files (tokenizer.json, tokenizer_config.json) use a GPT-2/BPE-style tokenizer with a 32,000-token vocabulary.

Special tokens:

  • bos_token: <s>
  • eos_token: </s>
  • pad_token: <pad>

Usage

from transformers import AutoModelForCausalLM, AutoTokenizer
model = AutoModelForCausalLM.from_pretrained("Subject-Emu-5259/NeuralAI-Air-135M")
tokenizer = AutoTokenizer.from_pretrained("Subject-Emu-5259/NeuralAI-Air-135M")

NeuralAI Model Family

Model Role Repo
NeuralAI Main production DPO adapter (360M, default) Subject-Emu-5259/NeuralAI
NeuralAI-Air-135M This repo — compact base model here
NeuralAI-Air-135M-SFT Supervised fine-tune of this base model Subject-Emu-5259/NeuralAI-Air-135M-SFT
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