datasets:
- universeTBD/arxiv-abstracts
Astronomy hypothesis generation with Falcon-7B
It was fine-tuned on several thousand astronomy abstracts collected on Arxiv.
Model Details
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch
online_model = AutoModelForCausalLM.from_pretrained("charlieoneill/falcon-7b-abstracts-2190", 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'])
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