README.md

---
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-Q8_0-GGUF
This model was converted to GGUF format from [`AstroMLab/AstroSage-8B`](https://huggingface.co/AstroMLab/AstroSage-8B) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space.
Refer to the [original model card](https://huggingface.co/AstroMLab/AstroSage-8B) 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)

```bash
brew install llama.cpp

```
Invoke the llama.cpp server or the CLI.

### CLI:
```bash
llama-cli --hf-repo Triangle104/AstroSage-8B-Q8_0-GGUF --hf-file astrosage-8b-q8_0.gguf -p "The meaning to life and the universe is"
```

### Server:
```bash
llama-server --hf-repo Triangle104/AstroSage-8B-Q8_0-GGUF --hf-file astrosage-8b-q8_0.gguf -c 2048
```

Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) 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-Q8_0-GGUF --hf-file astrosage-8b-q8_0.gguf -p "The meaning to life and the universe is"
```
or 
```
./llama-server --hf-repo Triangle104/AstroSage-8B-Q8_0-GGUF --hf-file astrosage-8b-q8_0.gguf -c 2048
```