初始化项目，由ModelHub XC社区提供模型
Model: AstroMLab/AstroSage-8B Source: Original Platform
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ModelHub XC
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README.md
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README.md
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---
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language:
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- en
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pipeline_tag: text-generation
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tags:
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- llama-3.1
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- astronomy
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- astrophysics
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- cosmology
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- arxiv
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inference: false
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base_model:
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- meta-llama/Meta-Llama-3.1-8B
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---
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# AstroSage-Llama-3.1-8B
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https://arxiv.org/abs/2411.09012
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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.
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## Model Details
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- **Base Architecture**: Meta-Llama-3.1-8B
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- **Base Model**: Meta-Llama-3.1-8B
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- **Parameters**: 8 billion
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- **Training Focus**: Astronomy, Astrophysics, Cosmology, and Astronomical Instrumentation
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- **License**: Llama 3.1 Community License
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- **Development Process**:
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1. Continued Pre-training (CPT) on astronomical literature
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2. Supervised Fine-tuning (SFT) on QA pairs and instruction sets
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3. Model merging with Meta-Llama-3.1-8B-Instruct (75% CPT+SFT / 25% Meta-Instruct)
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## Using the model
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```python
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import torch
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from transformers import AutoModelForCausalLM, AutoTokenizer
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# Load the model and tokenizer
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model = AutoModelForCausalLM.from_pretrained("AstroMLab/AstroSage-8b", device_map="auto")
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tokenizer = AutoTokenizer.from_pretrained("AstroMLab/AstroSage-8b")
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# Function to generate a response
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def generate_response(prompt):
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inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
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outputs = model.generate(
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**inputs,
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max_new_tokens=128,
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do_sample=True,
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pad_token_id=tokenizer.eos_token_id,
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)
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response = outputs[0][inputs['input_ids'].shape[-1]:]
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decoded = tokenizer.decode(response, skip_special_tokens=True)
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return decoded
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# Example usage
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prompt = """
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You are an expert in general astrophysics. Your task is to answer the following question:
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What are the main components of a galaxy?
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"""
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response = generate_response(prompt)
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print(response)
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```
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## Model Improvements and Performance
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AstroSage-Llama-3.1-8B shows remarkable performance improvements:
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| Model | Score (%) |
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|-------|-----------|
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| **<span style="color:green">AstroSage-Llama-3.1-8B</span>** | **<span style="color:green">80.9</span>** |
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| GPT-4o | 80.4 |
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| LLaMA-3.1-8B | 73.7 |
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| Gemma-2-9B | 71.5 |
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| Qwen-2.5-7B | 70.4 |
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| Yi-1.5-9B | 68.4 |
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| InternLM-2.5-7B | 64.5 |
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| Mistral-7B-v0.3 | 63.9 |
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| ChatGLM3-6B | 50.4 |
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The model demonstrates:
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- Outperformance of all 8B parameter models
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- Comparable performance to GPT-4o (80.4%)
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- ~1000x more cost-effective than proprietary models
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- 7 percentage-point improvement over base Llama-3.1-8b model
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## Training Data
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- **Continued Pre-training**:
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- ~250,000 arXiv preprints (2007-2024) from astro-ph and gr-qc
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- Astronomy-related Wikipedia articles
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- Selected astronomy textbooks
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- Total: 3.3 billion tokens, 19.9 GB plaintext
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- **Supervised Fine-tuning**:
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- 8.8 million curated QA pairs
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- Filtered Infinity-Instruct-7M dataset
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- Paper summaries and metadata
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- Total: 2.0 billion tokens, 9.8 GB plaintext
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## Intended Use
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- Curiosity-driven question answering
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- Brainstorming new ideas
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- Astronomical research assistance
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- Educational support in astronomy
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- Literature review and summarization
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- Scientific explanation of concepts
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## Limitations
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- Training data cutoff: January 2024
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- As with all LLMs, hallucinations are possible
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- Limited by 8B parameter size for complex reasoning
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- Paper metadata not perfectly memorized
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- Performance primarily validated on multiple-choice questions
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- Primarily trained for use in English
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## Technical Specifications
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- Architecture: Based on Meta-Llama 3.1
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- Training Infrastructure: ORNL OLCF Frontier
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- Hosting: Hugging Face Hub (AstroMLab/AstroSage-8B)
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## Ethical Considerations
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While this model is designed for scientific use:
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- Should not be used as sole source for critical research decisions
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- Output should be verified against primary sources
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- May reflect biases present in astronomical literature
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## Citation and Contact
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- Corresponding author: Tijmen de Haan (tijmen dot dehaan at gmail dot com)
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- AstroMLab: astromachinelearninglab at gmail dot com
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- Please cite the AstroMLab 3 paper when referencing this model:
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```
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@preprint{dehaan2024astromlab3,
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title={AstroMLab 3: Achieving GPT-4o Level Performance in Astronomy with a Specialized 8B-Parameter Large Language Model},
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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},
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year={2024},
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eprint={2411.09012},
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archivePrefix={arXiv},
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primaryClass={astro-ph.IM},
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url={https://arxiv.org/abs/2411.09012},
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}
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```
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