metadata

language:
  - en
pipeline_tag: text-generation
tags:
  - llama-3.1
  - astronomy
  - astrophysics
  - cosmology
  - arxiv
  - llama-cpp
  - gguf-my-repo
inference: false
base_model: AstroMLab/AstroSage-8B

Triangle104/AstroSage-8B-Q6_K-GGUF

This model was converted to GGUF format from AstroMLab/AstroSage-8B using llama.cpp via the ggml.ai's GGUF-my-repo space. Refer to the original model card for more details on the model.

Model details:

https://arxiv.org/abs/2411.09012

AstroSage-Llama-3.1-8B is a domain-specialized natural-language AI assistant tailored for research in astronomy, astrophysics, and cosmology. Trained on the complete collection of astronomy-related arXiv papers from 2007-2024 along with millions of synthetically-generated question-answer pairs and other astronomical literature, AstroSage-Llama-3.1-8B demonstrates excellent proficiency on a wide range of questions. This achievement demonstrates the potential of domain specialization in AI, suggesting that focused training can yield capabilities exceeding those of much larger, general-purpose models. Model Details

Base Architecture: Meta-Llama-3.1-8B
Base Model: Meta-Llama-3.1-8B
Parameters: 8 billion
Training Focus: Astronomy, Astrophysics, Cosmology, and Astronomical Instrumentation
License: Llama 3.1 Community License
Development Process:
    Continued Pre-training (CPT) on astronomical literature
    Supervised Fine-tuning (SFT) on QA pairs and instruction sets
    Model merging with Meta-Llama-3.1-8B-Instruct (75% CPT+SFT / 25% Meta-Instruct)

Using the model

import torch from transformers import AutoModelForCausalLM, AutoTokenizer

Load the model and tokenizer

model = AutoModelForCausalLM.from_pretrained("AstroMLab/AstroSage-8b", device_map="auto") tokenizer = AutoTokenizer.from_pretrained("AstroMLab/AstroSage-8b")

Function to generate a response

def generate_response(prompt): inputs = tokenizer(prompt, return_tensors="pt").to(model.device)

outputs = model.generate(
    **inputs,
    max_new_tokens=128,
    do_sample=True,
    pad_token_id=tokenizer.eos_token_id,
)
response = outputs[0][inputs['input_ids'].shape[-1]:]
decoded = tokenizer.decode(response, skip_special_tokens=True)

return decoded

Example usage

prompt = """ You are an expert in general astrophysics. Your task is to answer the following question: What are the main components of a galaxy? """ response = generate_response(prompt) print(response)

Model Improvements and Performance

AstroSage-Llama-3.1-8B shows remarkable performance improvements: Model Score (%) AstroSage-Llama-3.1-8B 80.9 GPT-4o 80.4 LLaMA-3.1-8B 73.7 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

The model demonstrates:

Outperformance of all 8B parameter models
Comparable performance to GPT-4o (80.4%)
~1000x more cost-effective than proprietary models
7 percentage-point improvement over base Llama-3.1-8b model

Training Data

Continued Pre-training:
    ~250,000 arXiv preprints (2007-2024) from astro-ph and gr-qc
    Astronomy-related Wikipedia articles
    Selected astronomy textbooks
    Total: 3.3 billion tokens, 19.9 GB plaintext

Supervised Fine-tuning:
    8.8 million curated QA pairs
    Filtered Infinity-Instruct-7M dataset
    Paper summaries and metadata
    Total: 2.0 billion tokens, 9.8 GB plaintext

Intended Use

Curiosity-driven question answering
Brainstorming new ideas
Astronomical research assistance
Educational support in astronomy
Literature review and summarization
Scientific explanation of concepts

Limitations

Training data cutoff: January 2024
As with all LLMs, hallucinations are possible
Limited by 8B parameter size for complex reasoning
Paper metadata not perfectly memorized
Performance primarily validated on multiple-choice questions
Primarily trained for use in English

Technical Specifications

Architecture: Based on Meta-Llama 3.1
Training Infrastructure: ORNL OLCF Frontier
Hosting: Hugging Face Hub (AstroMLab/AstroSage-8B)

Ethical Considerations

While this model is designed for scientific use:

Should not be used as sole source for critical research decisions
Output should be verified against primary sources
May reflect biases present in astronomical literature

Citation and Contact

Corresponding author: Tijmen de Haan (tijmen dot dehaan at gmail dot com)
AstroMLab: astromachinelearninglab at gmail dot com
Please cite the AstroMLab 3 paper when referencing this model:

@preprint{dehaan2024astromlab3, title={AstroMLab 3: Achieving GPT-4o Level Performance in Astronomy with a Specialized 8B-Parameter Large Language Model}, author={Tijmen de Haan and Yuan-Sen Ting and Tirthankar Ghosal and Tuan Dung Nguyen and Alberto Accomazzi and Azton Wells and Nesar Ramachandra and Rui Pan and Zechang Sun}, year={2024}, eprint={2411.09012}, archivePrefix={arXiv}, primaryClass={astro-ph.IM}, url={https://arxiv.org/abs/2411.09012}, }

Use with llama.cpp

Install llama.cpp through brew (works on Mac and Linux)

brew install llama.cpp

Invoke the llama.cpp server or the CLI.

CLI:

llama-cli --hf-repo Triangle104/AstroSage-8B-Q6_K-GGUF --hf-file astrosage-8b-q6_k.gguf -p "The meaning to life and the universe is"

Server:

llama-server --hf-repo Triangle104/AstroSage-8B-Q6_K-GGUF --hf-file astrosage-8b-q6_k.gguf -c 2048

Note: You can also use this checkpoint directly through the usage steps listed in the Llama.cpp repo as well.

Step 1: Clone llama.cpp from GitHub.

git clone https://github.com/ggerganov/llama.cpp

Step 2: Move into the llama.cpp folder and build it with LLAMA_CURL=1 flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux).

cd llama.cpp && LLAMA_CURL=1 make

Step 3: Run inference through the main binary.

./llama-cli --hf-repo Triangle104/AstroSage-8B-Q6_K-GGUF --hf-file astrosage-8b-q6_k.gguf -p "The meaning to life and the universe is"

./llama-server --hf-repo Triangle104/AstroSage-8B-Q6_K-GGUF --hf-file astrosage-8b-q6_k.gguf -c 2048