README.md

---
license: mit
language:
- en
pipeline_tag: text-generation
tags:
- llama-2
- astronomy
- astrophysics
- arxiv
inference: false
base_model:
- meta-llama/Llama-2-70b-hf
---

# AstroLLaMA-2-70B-Base_AIC

AstroLLaMA-2-70B-Base_AIC is a specialized base language model for astronomy, developed by fine-tuning Meta's LLaMA-2-70b architecture on astronomical literature. This model was developed by the AstroMLab team and is, to our best knowledge, the first specialized 70B parameter-level LLM in astronomy. It is designed for next token prediction tasks and is not an instruct/chat model.

## Model Details

- **Base Architecture**: LLaMA-2-70b
- **Training Data**: Abstract, Introduction, and Conclusion (AIC) sections from arXiv's astro-ph category papers (from arXiv's inception up to July 2023)
- **Data Processing**: The training data was derived from LaTeX source files using regex-based extraction methods to identify and extract the relevant sections (Abstract, Introduction, and Conclusion).
- **Fine-tuning Method**: Continual Pre-Training (CPT) using the LMFlow framework
- **Training Details**:
  - Learning rate: 2 × 10⁻⁵
  - Total batch size: 160
  - Maximum token length: 2048
  - Warmup ratio: 0.03
  - Cosine decay schedule for learning rate reduction
  - Training duration: 1 epoch (approximately 2,000 A100 GPU hours)
- **Primary Use**: Next token prediction for astronomy-related text generation and analysis
- **Reference**: [Pan et al. 2024](https://arxiv.org/abs/2409.19750)

## Generating text from a prompt

```python
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

# Load the model and tokenizer
tokenizer = AutoTokenizer.from_pretrained("AstroMLab/astrollama-2-70b-base_aic")
model = AutoModelForCausalLM.from_pretrained("AstroMLab/astrollama-2-70b-base_aic", device_map="auto")

# Create the pipeline with explicit truncation
from transformers import pipeline
generator = pipeline(
    "text-generation",
    model=model,
    tokenizer=tokenizer,
    device_map="auto",
    truncation=True,
    max_length=512
)

# Example prompt from an astronomy paper
prompt = "In this letter, we report the discovery of the highest redshift, " \
    "heavily obscured, radio-loud QSO candidate selected using JWST NIRCam/MIRI, " \
    "mid-IR, sub-mm, and radio imaging in the COSMOS-Web field. "

# Set seed for reproducibility
torch.manual_seed(42)

# Generate text
generated_text = generator(prompt, do_sample=True)
print(generated_text[0]['generated_text'])
```

## Model Performance and Significance

AstroLLaMA-2-70B-Base_AIC demonstrates notable improvements over its baseline LLaMA-2-70B model, marking a crucial step in specialized astronomical LLMs. Here's a performance comparison chart based upon the astronomical benchmarking Q&A as described in [Ting et al. 2024](https://arxiv.org/abs/2407.11194):

| Model | Score (%) |
|-------|-----------|
| **AstroSage-LLaMA-3.1-8B (AstroMLab)** | **80.9** |
| **<span style="color:green">AstroLLaMA-2-70B-Base (AstroMLab)</span>** | **<span style="color:green">76.0</span>** |
| LLaMA-3.1-8B | 73.7 |
| LLaMA-2-70B | 70.7 |
| Gemma-2-9B | 71.5 |
| Qwen-2.5-7B | 70.4 |
| Yi-1.5-9B | 68.4 |
| InternLM-2.5-7B | 64.5 |
| Mistral-7B-v0.3 | 63.9 |
| ChatGLM3-6B | 50.4 |

It demonstrates that training specialized LLMs can be effective, especially at larger model scales.


## Ethical Considerations

While this model is designed for scientific use, users should be mindful of potential misuse, such as generating misleading scientific content. Always verify model outputs against peer-reviewed sources for critical applications.

## Citation

If you use this model in your research, please cite:

```
@ARTICLE{2024arXiv240919750P,
       author = {{Pan}, Rui and {Dung Nguyen}, Tuan and {Arora}, Hardik and {Accomazzi}, Alberto and {Ghosal}, Tirthankar and {Ting}, Yuan-Sen},
        title = "{AstroMLab 2: AstroLLaMA-2-70B Model and Benchmarking Specialised LLMs for Astronomy}",
      journal = {arXiv e-prints},
     keywords = {Astrophysics - Instrumentation and Methods for Astrophysics, Computer Science - Computation and Language},
         year = 2024,
        month = sep,
          eid = {arXiv:2409.19750},
        pages = {arXiv:2409.19750},
          doi = {10.48550/arXiv.2409.19750},
archivePrefix = {arXiv},
       eprint = {2409.19750},
 primaryClass = {astro-ph.IM},
       adsurl = {https://ui.adsabs.harvard.edu/abs/2024arXiv240919750P},
      adsnote = {Provided by the SAO/NASA Astrophysics Data System}
}
```