Update README.md

README.md

@@ -18,6 +18,140 @@ base_model: AstroMLab/AstroSage-8B
 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)