README.md

---
datasets:
- universeTBD/arxiv-abstracts
---

# Astronomy hypothesis generation with Falcon-7B

<!-- This model generates astronomy abstracts. -->

It was fine-tuned on several thousand astronomy abstracts collected on Arxiv.

## Model Details

```python
from transformers import AutoModelForCausalLM, AutoTokenizer
import transformers
import torch

online_model = AutoModelForCausalLM.from_pretrained("universeTBD/falcon-7b-abstracts-tiny", torch_dtype=torch.bfloat16,
                                                    device_map="auto", trust_remote_code=True)

tokenizer = AutoTokenizer.from_pretrained("tiiuae/falcon-7b")
pipeline = transformers.pipeline(
    "text-generation",
    model=online_model,
    tokenizer=tokenizer,
    torch_dtype=torch.bfloat16,
    trust_remote_code=True,
    device_map="auto",
)

sequences = pipeline(
   "### Instruction: Generate a scientific hypothesis about astronomy in the style of an Arxiv paper.\n ### Hypothesis:",
    max_length=500,
    do_sample=True,
    top_k=10,
    num_return_sequences=1,
    eos_token_id=tokenizer.eos_token_id,
)

def format_output(output):
    output = output.replace("\n", " ")  # Replace newline characters with spaces
    output = output.replace("\\n", " ")
    parts = output.split("###")  # Split string at '###'
    
    # Get and clean instruction part
    instruction = parts[1].strip() 
    
    # Get and clean hypothesis part
    hypothesis = parts[2].strip()  
    
    # Format the output
    formatted_output = f"{instruction}\n\n{hypothesis}"
    
    return formatted_output

format_output(sequences[0]['generated_text'])
```

Example generation:

__Using 3D positions and K magnitudes of stars from the Gaia DR2 for which we have spectroscopic information from the RAVE database, we derive distances to the stellar populations in different parts of the bulge of the Milky Way. We find that the metal-rich (blue) stars in the inner part of the bulge have a disk component, while the metal-poor (red) stars in the inner part of the bulge do not have a discernible disk component and are dominated by halo components. Spectral parameters indicate that the red stars are enhanced in nitrogen and the blue stars are enhanced in iron, suggesting that the red stars may have a faster rotation curve than the blue stars. These morpho-chemical properties are similar to those of the classical thick disk populations. However, the inner part of the bulge stars with metallicity about -1.0 <[Fe/H] < -0.5 do not have a discernible disk component and are also found in the halo component. Stars with metallicity about -2.5 <[Fe/H] < -1.0 in the inner part of the bulge also have a faint halo component and are enhanced in nitrogen. We suggest that the metal-rich blue stars in the inner part of the bulge came from a disk formed in situ and the red stars in the inner part of the bulge came from two different disk-to-halo transition zones which may be associated with the late low-density and late high-density spiral arms, respectively.__


### Model Description

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- **Developed by:** [More Information Needed]
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### Model Sources [optional]

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## Uses

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### Direct Use

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### Downstream Use [optional]

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### Out-of-Scope Use

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## Bias, Risks, and Limitations

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### Recommendations

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Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.

## How to Get Started with the Model

Use the code below to get started with the model.

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## Training Details

### Training Data

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### Training Procedure 

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#### Preprocessing [optional]

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#### Training Hyperparameters

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#### Speeds, Sizes, Times [optional]

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## Evaluation

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### Testing Data, Factors & Metrics

#### Testing Data

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#### Factors

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#### Metrics

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### Results

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#### Summary



## Model Examination [optional]

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## Environmental Impact

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Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).

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## Technical Specifications [optional]

### Model Architecture and Objective

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### Compute Infrastructure

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#### Hardware

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#### Software

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## Citation [optional]

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**BibTeX:**

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**APA:**

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## Glossary [optional]

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## More Information [optional]

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## Model Card Authors [optional]

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## Model Card Contact

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