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PicoKittens/
AbstractsLlama-8M

Name: AbstractsLlama-8M
Rating: 2.5 (13 reviews)

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AbstractsLlama-8M

AbstractsLlama-8M is an ultra-compact, "pico-sized" language model trained from scratch by Pico-Kittens. It utilizes the Llama 2 architecture and is specifically optimized for generating scientific and academic text.

Model Details

Developed by: Pico-Kittens
Model type: Llama 2-based Causal Language Model
Training Status: Trained from scratch (Not a fine-tune)
Parameters: ~8 Million
Language(s): English
License: apache-2.0

Training Data

The model was trained on a large-scale collection of ArXiv abstracts. The training objective was to compress the structural patterns, technical nomenclature, and "academic tone" of scientific research into a minimal parameter budget.

Capabilities & Limitations

AbstractsLlama-8M is an experimental model. While it effectively mimics the syntax of research papers, users should be aware of the following:

Scientific Syntax: Highly competent; it excels at producing the "feel" of a formal research proposal or abstract.
Architecture: Implements the Llama 2 transformer block structure at a micro scale.
Hallucinations: Extremely high. The model will invent methodologies, chemical structures, and mathematical frameworks that do not exist.
Context: Limited. It is best suited for short-form generation (under 128 tokens).

Generation Sample

User: We propose

AbstractsLlama-8M:

We propose a unified framework for modeling large-scale non-linearity of Cancer (NCI) problems with a variable-scale dataset for the linearized dynamics of polynomial conjugal structure. Our key idea of a multi-objective-centile-based model with a fixed, non-preferred variational autoencoder (NMAE) for feature extraction, which includes ax-aware, non-convex optimization formulation for both a single

How to Get Started

python

import torch
from transformers import pipeline

device = 0 if torch.cuda.is_available() else -1
pipe = pipeline("text-generation", model="PicoKittens/AbstractsLlama-8M", device=device)

output = pipe("We propose", max_new_tokens=100, do_sample=True)
print(output[0]['generated_text'])