README.md

metadata

base_model:
  - mistralai/Mistral-Nemo-Instruct-2407

Ctranslate2 conversion of the model located at mistralai/Mistral-Nemo-Instruct-2407

Conversion script with graphical user interface can be downloaded HERE

Tested with Ctranslate 4.4.0 and Torch 2.2.2

• NOTE: Ctranslate2 will soon release version 4.5.0, which will require greater than Torch 2.2.2.

Example Usage:

import os
import sys
import ctranslate2
import gc
import torch
from transformers import AutoTokenizer

system_message = "You are a helpful person who answers questions."
user_message = "Hello, how are you today? I'd like you to write me a funny poem that is a parody of Milton's Paradise Lost if you are familiar with that famous epic poem?"

model_dir = r"D:\Scripts\bench_chat\models\mistralai--Mistral-Nemo-Instruct-2407-ct2-int8"


def build_prompt_mistral_nemo():
    prompt = f"""<s>
[INST]{system_message}

{user_message}[/INST]"""
    
    return prompt


def main():
    model_name = os.path.basename(model_dir)

    print(f"\033[32mLoading the model: {model_name}...\033[0m")
    
    intra_threads = max(os.cpu_count() - 4, 4)

    generator = ctranslate2.Generator(
        model_dir,
        device="cuda",
        compute_type="int8",
        intra_threads=intra_threads
    )
    
    tokenizer = AutoTokenizer.from_pretrained(model_dir, add_prefix_space=None)
    
    prompt = build_prompt_mistral_nemo()
    
    tokens = tokenizer.convert_ids_to_tokens(tokenizer.encode(prompt))
    
    results_batch = generator.generate_batch(
        [tokens],
        include_prompt_in_result=False,
        max_batch_size=4096,
        batch_type="tokens",
        beam_size=1,
        num_hypotheses=1,
        max_length=512,
        sampling_temperature=0.0,
    )

    output = tokenizer.decode(results_batch[0].sequences_ids[0])
    
    print("\nGenerated response:")
    print(output)
    
    del generator
    del tokenizer
    torch.cuda.empty_cache()
    gc.collect()


if __name__ == "__main__":
    main()