Model Card for tibetan-to-english-translation-4bit
Model Details
This model is a quantized version of billingsmoore/tibetan-to-english-translation. The model is double quanitized to 4bits. The process was performed (and can be replicated) with the following code:
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM, BitsAndBytesConfig
import torch
model_id = "billingsmoore/tibetan-to-english-translation"
tokenizer = AutoTokenizer.from_pretrained(model_id)
quantization_config = BitsAndBytesConfig(
load_in_4bit=True,
bnb_4bit_quant_type="nf4",
bnb_4bit_use_double_quant=True,
bnb_4bit_compute_dtype=torch.bfloat16
)
model = AutoModelForSeq2SeqLM.from_pretrained(model_id, device_map="auto", quantization_config=quantization_config)
- Developed by: billingsmoore
- Model type: Seq2Seq
- Language(s) (NLP): Tibetan, English
- License: Apache license 2.0
Model Sources
- Repository: GitHub
Uses
The intended usage of this quantized model is for in-browser usage on edge devices.
Direct Use
This model can be used as part of a web app using Transformers.js as below.
import { pipeline } from '@huggingface/transformers';
// Allocate a pipeline for sentiment-analysis
const pipe = await pipeline('translation', 'billingsmoore/tibetan-to-english-translation-4bit');
const out = await pipe('ན་མོ་མཉྫ་ཤཱི་ཡེ།');
Out-of-Scope Use
This model can also be used in the usual way using the Python transformers library as below.
from transformers import pipeline
# Allocate a pipeline for sentiment-analysis
pipe = pipeline('translation', 'billingsmoore/tibetan-to-english-translation-4bit')
out = pipe('ན་མོ་མཉྫ་ཤཱི་ཡེ།')
More Information
For additional information on training, data, etc. please see the model card for billingsmoore/tibetan-to-english-translation.
Model Card Author
billingsmoore
Model Card Contact
billingsmoore [at] gmail [dot] com
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