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Model Card for FastChat-T5 3B Q8
The model is quantized version of the lmsys/fastchat-t5-3b-v1.0 with int8 quantization.
Model Details
Model Description
The model being quantized using CTranslate2 with the following command:
ct2-transformers-converter --model lmsys/fastchat-t5-3b --output_dir lmsys/fastchat-t5-3b-ct2 --copy_files generation_config.json added_tokens.json tokenizer_config.json special_tokens_map.json spiece.model --quantization int8 --force --low_cpu_mem_usage
If you want to perform the quantization yourself, you need to install the following dependencies:
pip install -qU ctranslate2 transformers[torch] sentencepiece accelerate
- Shared by: Lim Chee Kin
- License: Apache 2.0
How to Get Started with the Model
Use the code below to get started with the model.
import ctranslate2
import transformers
translator = ctranslate2.Translator("limcheekin/fastchat-t5-3b-ct2")
tokenizer = transformers.AutoTokenizer.from_pretrained("limcheekin/fastchat-t5-3b-ct2")
input_text = "translate English to German: The house is wonderful."
input_tokens = tokenizer.convert_ids_to_tokens(tokenizer.encode(input_text))
results = translator.translate_batch([input_tokens])
output_tokens = results[0].hypotheses[0]
output_text = tokenizer.decode(tokenizer.convert_tokens_to_ids(output_tokens))
print(output_text)
The code is taken from https://opennmt.net/CTranslate2/guides/transformers.html#t5.
The key method of the code above is translate_batch, you can find out its supported parameters here.
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