---
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

online_model = AutoModelForCausalLM.from_pretrained("charlieoneill/falcon-7b-abstracts-2190", 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'])
```

### Model Description

<!-- Provide a longer summary of what this model is. -->



- **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.

[More Information Needed]

## Training Details

### Training Data

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### Training Procedure 

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#### Preprocessing [optional]

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#### Training Hyperparameters

- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->

#### 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).

- **Hardware Type:** [More Information Needed]
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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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