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from transformers import AutoTokenizer
import gradio as gr
def tokenize(input_text):
llama_tokens = len(
llama_tokenizer(input_text, add_special_tokens=True)["input_ids"]
)
llama3_tokens = len(
llama3_tokenizer(input_text, add_special_tokens=True)["input_ids"]
)
mistral_tokens = len(
mistral_tokenizer(input_text, add_special_tokens=True)["input_ids"]
)
gpt2_tokens = len(gpt2_tokenizer(input_text, add_special_tokens=True)["input_ids"])
gpt_neox_tokens = len(
gpt_neox_tokenizer(input_text, add_special_tokens=True)["input_ids"]
)
falcon_tokens = len(
falcon_tokenizer(input_text, add_special_tokens=True)["input_ids"]
)
phi2_tokens = len(phi2_tokenizer(input_text, add_special_tokens=True)["input_ids"])
t5_tokens = len(t5_tokenizer(input_text, add_special_tokens=True)["input_ids"])
gemma_tokens = len(gemma_tokenizer(input_text, add_special_tokens=True)["input_ids"])
command_r_tokens = len(command_r_tokenizer(input_text, add_special_tokens=True)["input_ids"])
results = {
"LLaMa-1/LLaMa-2": llama_tokens,
"LLaMa-3": llama3_tokens,
"Mistral": mistral_tokens,
"GPT-2/GPT-J": gpt2_tokens,
"GPT-NeoX": gpt_neox_tokens,
"Falcon": falcon_tokens,
"Phi": phi2_tokens,
"T5": t5_tokens,
"Gemma": gemma_tokens,
"Command-R": command_r_tokens
}
# Sort the results in descending order based on token length
sorted_results = sorted(results.items(), key=lambda x: x[1], reverse=True)
return "\n".join([f"{model}: {tokens}" for model, tokens in sorted_results])
if __name__ == "__main__":
llama_tokenizer = AutoTokenizer.from_pretrained("TheBloke/Llama-2-7B-fp16")
llama3_tokenizer = AutoTokenizer.from_pretrained("unsloth/llama-3-8b")
mistral_tokenizer = AutoTokenizer.from_pretrained("unsloth/Mistral-7B-v0.2")
gpt2_tokenizer = AutoTokenizer.from_pretrained("gpt2")
gpt_neox_tokenizer = AutoTokenizer.from_pretrained("EleutherAI/gpt-neox-20b")
falcon_tokenizer = AutoTokenizer.from_pretrained("tiiuae/falcon-7b")
phi2_tokenizer = AutoTokenizer.from_pretrained("microsoft/phi-2")
t5_tokenizer = AutoTokenizer.from_pretrained("google/flan-t5-xxl")
gemma_tokenizer = AutoTokenizer.from_pretrained("alpindale/gemma-2b")
command_r_tokenizer = AutoTokenizer.from_pretrained("CohereForAI/c4ai-command-r-plus")
iface = gr.Interface(fn=tokenize, inputs=gr.Textbox(label="Input Text", lines=10), outputs="text")
iface.launch()
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