PursuitOfDataScience/Argonne-1.5

Argonne 1.5

Argonne 1.5 is an optimized and significantly improved causal language model compared to its predecessor, Argonne 1.0. It is built on Hugging Face's Transformers library, fully integrated with the AutoModel class for ease of use.

The codebase is in:

👉 https://github.com/PursuitOfDataScience/ArgonneAI/tree/Argonne-1.5

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Model Improvements 🚀

Compared to Argonne-1.0, this model features significant enhancements, including:

• Utilized torch.compile() for improved pretraining speed.
• Integrated Flash Attention for an additional 2.6× memory efficiency improvement, enabling a much larger batch size.
• Increased number of layers and attention heads for richer model capacity.
• Better GPU hardware utilization.
• Hugging Face AutoModel integration for convenient use.
• Enhanced text generation capabilities.

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Training Data 📚

This model was trained on the same dataset used for Argonne-1.0 pretraining.

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Model Details 🧠

Attribute	Value
Parameters	356,516,640
Block Size	2048
Layers	16
Attention Heads	16
Embedding Size	1296
Batch Size	756

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Training Hardware & Duration 🖥️

• Trained on a single DGX node (8× A100 GPUs, 80GB each).
• Total GPU Hours: 1248
• Total Global Steps: 80,000

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How to Use (Inference) ⚙️

Here's how you can quickly start generating text with Argonne-1.5:

from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

model_path = "PursuitOfDataScience/Argonne-1.5"

# 1) Load the Argonne-1.5 model (set trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
    model_path,
    trust_remote_code=True
)

# 2) Load the tokenizer
tokenizer = AutoTokenizer.from_pretrained(model_path)

# 3) Inference example
prompt = "The meaning of life is "
inputs = tokenizer(prompt, return_tensors="pt")

# generate text using common HF parameters
outputs = model.generate(**inputs, max_length=150, do_sample=True, top_k=50, top_p=0.95, temperature=0.7)

# Decode and print the generated text
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

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