ArgonneAI
Collection
Pretrained LLMs from scratch.
β’
3 items
β’
Updated
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:
π https://github.com/PursuitOfDataScience/ArgonneAI/tree/Argonne-1.5
Compared to Argonne-1.0, this model features significant enhancements, including:
torch.compile()
for improved pretraining speed.AutoModel
integration for convenient use.This model was trained on the same dataset used for Argonne-1.0 pretraining.
Attribute | Value |
---|---|
Parameters | 356,516,640 |
Block Size | 2048 |
Layers | 16 |
Attention Heads | 16 |
Embedding Size | 1296 |
Batch Size | 756 |
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))
The meaning of life is tamed in many ways. It is a state of mental and physical development. It is a state of deep emotional strength and confidence, and it is a state of physical and mental balance. In this article, we will explore the meaning of life, the different ways life is defined, and how we can apply this concept to our own lives.