metadata

license: mit
tags:
  - ctranslate2
  - quantization
  - int8
  - float16
  - text-generation
  - ALMA
  - llama

ALMA-13B model for CTranslate2

The model is quantized version of the haoranxu/ALMA-13B with int8_float16 quantization and can be used in CTranslate2.

ALMA (Advanced Language Model-based trAnslator) is an LLM-based translation model, which adopts a new translation model paradigm: it begins with fine-tuning on monolingual data and is further optimized using high-quality parallel data. This two-step fine-tuning process ensures strong translation performance.

Model creator: Haoran Xu
Original model: ALMA 13B

Conversion details

The original model was converted on 2023-12 with the following command:

ct2-transformers-converter --model haoranxu/ALMA-13B --quantization int8_float16 --output_dir ALMA-13B-ct2-int8_float16 \
    --copy_files generation_config.json special_tokens_map.json tokenizer.model tokenizer_config.json

Prompt template: ALMA

Translate this from English to Chinese:
English: {prompt}
Chinese:

Example

This example code is obtained from CTranslate2_transformers. More detailed information about the generate_batch methon can be found at CTranslate2_Generator.generate_batch.

import ctranslate2
import transformers

generator = ctranslate2.Generator("avans06/ALMA-13B-ct2-int8_float16")
tokenizer = transformers.AutoTokenizer.from_pretrained("haoranxu/ALMA-13B")

text = "Who is Alan Turing?"
prompt = f"Translate this from English to Chinese:\nEnglish: {text}\nChinese:"
tokens = tokenizer.convert_ids_to_tokens(tokenizer.encode(prompt))

results = generator.generate_batch([tokens], max_length=256, sampling_temperature=0.7, sampling_topp=0.9, repetition_penalty=1.1, include_prompt_in_result=False)

output = tokenizer.decode(results[0].sequences_ids[0])

The following explanations are excerpted from the FAQ section of the author's GitHub README.

What language directions do ALMA support?
Currently, ALMA supports 10 directions: English↔German, Englishs↔Czech, Englishs↔Icelandic, Englishs↔Chinese, Englishs↔Russian. However, it may surprise us in other directions :)

More information

For more information about the original model, see its GitHub repository