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aloobun/
minini-140m-base

Name: minini-140m-base
Availability: InStock

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50c51e02a205b44c3449fc128400ff20-2052545627

In this experiment, i distilled a 1B Llama model, with training samples drawn from the wikimedia, minipile and fineweb-edu dataset. I've used the SM3 optimizer w/ cosine scheduler.

I've release this initial experimental checkpoint as a foundation for further exploration and I plan to conduct more experiments with different optimization strategies(https://github.com/HomebrewML/HeavyBall) and will update the model weights accordingly.

from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

model_name = "aloobun/minini-140m-base"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

prompt = "Once upon a time"

inputs = tokenizer(prompt, return_tensors="pt")

with torch.no_grad():
    output_ids = model.generate(
        **inputs,
        max_length=100,
        do_sample=True,
        temperature=0.8,
        top_p=0.95,
        pad_token_id=tokenizer.eos_token_id,
    )

output_text = tokenizer.decode(output_ids[0], skip_special_tokens=True)
print(output_text)