---
license: apache-2.0
datasets:
- arxiv_abstracts
language:
- en
pipeline_tag: text-generation
tags:
- tiny
- pico
- scratch
- llama-2
- academic
---

# 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'])