ct2fast-GPT-JT-6B-v0

# Fast-Inference with Ctranslate2

Speedup inference while reducing memory by 2x-4x using int8 inference in C++ on CPU or GPU.

quantized version of togethercomputer/GPT-JT-6B-v0

pip install hf-hub-ctranslate2>=2.0.6 

Converted on 2023-05-19 using

ct2-transformers-converter --model togethercomputer/GPT-JT-6B-v0 --output_dir /home/michael/tmp-ct2fast-GPT-JT-6B-v0 --force --copy_files merges.txt tokenizer.json README.md tokenizer_config.json vocab.json special_tokens_map.json added_tokens.json .gitattributes --quantization float16

Checkpoint compatible to ctranslate2>=3.13.0 and hf-hub-ctranslate2>=2.0.6

• compute_type=int8_float16 for device="cuda"
• compute_type=int8 for device="cpu"

from hf_hub_ctranslate2 import TranslatorCT2fromHfHub, GeneratorCT2fromHfHub
from transformers import AutoTokenizer

model_name = "michaelfeil/ct2fast-GPT-JT-6B-v0"
# use either TranslatorCT2fromHfHub or GeneratorCT2fromHfHub here, depending on model.
model = GeneratorCT2fromHfHub(
        # load in int8 on CUDA
        model_name_or_path=model_name, 
        device="cuda",
        compute_type="int8_float16",
        tokenizer=AutoTokenizer.from_pretrained("togethercomputer/GPT-JT-6B-v0")
)
outputs = model.generate(
    text=["How do you call a fast Flan-ingo?", "User: How are you doing? Bot:"],
)
print(outputs)

Licence and other remarks:

This is just a quantized version. Licence conditions are intended to be idential to original huggingface repo.

Original description

Quick Start

from transformers import pipeline

pipe = pipeline(model='togethercomputer/GPT-JT-6B-v0')

pipe("Where is Zurich? Ans:")