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Model: AstroMLab/AstroSage-8B
Source: Original Platform

.gitattributes

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README.md

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+---
+language:
+- en
+pipeline_tag: text-generation
+tags:
+- llama-3.1
+- astronomy
+- astrophysics
+- cosmology
+- arxiv
+inference: false
+base_model:
+- meta-llama/Meta-Llama-3.1-8B
+---
+
+# AstroSage-Llama-3.1-8B
+
+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**:
+  1. Continued Pre-training (CPT) on astronomical literature
+  2. Supervised Fine-tuning (SFT) on QA pairs and instruction sets
+  3. Model merging with Meta-Llama-3.1-8B-Instruct (75% CPT+SFT / 25% Meta-Instruct)
+
+## Using the model
+
+```python
+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 (%) |
+|-------|-----------|
+| **<span style="color:green">AstroSage-Llama-3.1-8B</span>** | **<span style="color:green">80.9</span>** |
+| 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}, 
+}
+```

config.json

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+{
+  "_name_or_path": "AstroMLab/AstroSage-8B",
+  "architectures": [
+    "LlamaForCausalLM"
+  ],
+  "attention_bias": false,
+  "attention_dropout": 0.0,
+  "bos_token_id": 128000,
+  "eos_token_id": [
+    128001,
+    128008,
+    128009
+  ],
+  "hidden_act": "silu",
+  "hidden_size": 4096,
+  "initializer_range": 0.02,
+  "intermediate_size": 14336,
+  "max_position_embeddings": 131072,
+  "mlp_bias": false,
+  "model_type": "llama",
+  "num_attention_heads": 32,
+  "num_hidden_layers": 32,
+  "num_key_value_heads": 8,
+  "pretraining_tp": 1,
+  "rms_norm_eps": 1e-05,
+  "rope_scaling": {
+    "factor": 8.0,
+    "high_freq_factor": 4.0,
+    "low_freq_factor": 1.0,
+    "original_max_position_embeddings": 8192,
+    "rope_type": "llama3"
+  },
+  "rope_theta": 500000.0,
+  "tie_word_embeddings": false,
+  "torch_dtype": "bfloat16",
+  "transformers_version": "4.44.2",
+  "use_cache": true,
+  "vocab_size": 128256
+}

generation_config.json

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+{
+  "_from_model_config": true,
+  "bos_token_id": 128000,
+  "do_sample": true,
+  "eos_token_id": [
+    128001,
+    128008,
+    128009
+  ],
+  "temperature": 0.6,
+  "min_p": 0.1,
+  "transformers_version": "4.44.0"
+}

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special_tokens_map.json

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+{
+  "bos_token": {
+    "content": "<|begin_of_text|>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "eos_token": {
+    "content": "<|end_of_text|>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  }
+}