NeuralAI Model Family
Collection
DPO LoRA, NeuralAI-Air 135M base, and 135M-SFT models by De'Andrew Preston Harris. • 3 items • Updated
How to use Subject-Emu-5259/NeuralAI-Air-135M with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("text-generation", model="Subject-Emu-5259/NeuralAI-Air-135M") # Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("Subject-Emu-5259/NeuralAI-Air-135M", device_map="auto")How to use Subject-Emu-5259/NeuralAI-Air-135M with vLLM:
# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Subject-Emu-5259/NeuralAI-Air-135M"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
-H "Content-Type: application/json" \
--data '{
"model": "Subject-Emu-5259/NeuralAI-Air-135M",
"prompt": "Once upon a time,",
"max_tokens": 512,
"temperature": 0.5
}'docker model run hf.co/Subject-Emu-5259/NeuralAI-Air-135M
How to use Subject-Emu-5259/NeuralAI-Air-135M with SGLang:
# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
--model-path "Subject-Emu-5259/NeuralAI-Air-135M" \
--host 0.0.0.0 \
--port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
-H "Content-Type: application/json" \
--data '{
"model": "Subject-Emu-5259/NeuralAI-Air-135M",
"prompt": "Once upon a time,",
"max_tokens": 512,
"temperature": 0.5
}'docker run --gpus all \
--shm-size 32g \
-p 30000:30000 \
-v ~/.cache/huggingface:/root/.cache/huggingface \
--env "HF_TOKEN=<secret>" \
--ipc=host \
lmsysorg/sglang:latest \
python3 -m sglang.launch_server \
--model-path "Subject-Emu-5259/NeuralAI-Air-135M" \
--host 0.0.0.0 \
--port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
-H "Content-Type: application/json" \
--data '{
"model": "Subject-Emu-5259/NeuralAI-Air-135M",
"prompt": "Once upon a time,",
"max_tokens": 512,
"temperature": 0.5
}'How to use Subject-Emu-5259/NeuralAI-Air-135M with Docker Model Runner:
docker model run hf.co/Subject-Emu-5259/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.
| 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 |
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>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")
| 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 |