from huggingface_hub import notebook_login
from datasets import load_dataset, Dataset
from transformers import AutoModelForCausalLM, AutoTokenizer, TextStreamer

model = "ArtifactAI/phi-biology"

model = AutoModelForCausalLM.from_pretrained(base_model, trust_remote_code= True)
tokenizer = AutoTokenizer.from_pretrained(base_model, trust_remote_code=True)

def generate(prompt):
  inputs = tokenizer(f'''Below is an instruction that describes a task. Write a response that appropriately completes the request If you are adding additional white spaces, stop writing".\n\n### Instruction:\n{prompt}.\n\n### Response:\n ''', return_tensors="pt", return_attention_mask=False)
  streamer = TextStreamer(tokenizer, skip_prompt= True)
  _ = model.generate(**inputs, streamer=streamer, max_new_tokens=500)
  
generate("What are the common techniques used in identifying a new species, and how can scientists accurately categorize it within the existing taxonomy system?")

Training Data

The model was trained on camel-ai/biology, a dataset of question/answer pairs. Questions are generated using the t5-base model, while the answers are generated using the GPT-3.5-turbo model.

Citation

@misc{phi-math,
    title={phi-biology},
    author={Matthew Kenney},
    year={2023}
}

AlgorithmicResearchGroup/phi-biology

Table of Contents

TL;DR

Model Description

Usage

Using the Pytorch model

Training Data

Citation

AlgorithmicResearchGroup/
phi-biology