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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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- llama-cpp |
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- gguf-my-repo |
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inference: false |
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base_model: AstroMLab/AstroSage-8B |
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--- |
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# Triangle104/AstroSage-8B-Q6_K-GGUF |
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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. |
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Refer to the [original model card](https://huggingface.co/AstroMLab/AstroSage-8B) for more details on the model. |
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--- |
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Model details: |
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- |
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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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Continued Pre-training (CPT) on astronomical literature |
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Supervised Fine-tuning (SFT) on QA pairs and instruction sets |
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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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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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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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AstroSage-Llama-3.1-8B 80.9 |
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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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- |
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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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- |
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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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- |
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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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- |
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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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@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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## Use with llama.cpp |
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Install llama.cpp through brew (works on Mac and Linux) |
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```bash |
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brew install llama.cpp |
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``` |
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Invoke the llama.cpp server or the CLI. |
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### CLI: |
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```bash |
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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" |
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``` |
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### Server: |
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```bash |
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llama-server --hf-repo Triangle104/AstroSage-8B-Q6_K-GGUF --hf-file astrosage-8b-q6_k.gguf -c 2048 |
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``` |
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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. |
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Step 1: Clone llama.cpp from GitHub. |
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``` |
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git clone https://github.com/ggerganov/llama.cpp |
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``` |
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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). |
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``` |
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cd llama.cpp && LLAMA_CURL=1 make |
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``` |
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Step 3: Run inference through the main binary. |
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``` |
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./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" |
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``` |
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or |
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``` |
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./llama-server --hf-repo Triangle104/AstroSage-8B-Q6_K-GGUF --hf-file astrosage-8b-q6_k.gguf -c 2048 |
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``` |
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