Crosslingual Generalization through Multitask Finetuning
Paper • 2211.01786 • Published • 2
# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("rustformers/bloomz-ggml", dtype="auto")Quick Links
GGML converted versions of BigScience's BloomZ models
Description
We present BLOOMZ & mT0, a family of models capable of following human instructions in dozens of languages zero-shot. We finetune BLOOM & mT5 pretrained multilingual language models on our crosslingual task mixture (xP3) and find the resulting models capable of crosslingual generalization to unseen tasks & languages.
- Repository: bigscience-workshop/xmtf
- Paper: Crosslingual Generalization through Multitask Finetuning
- Point of Contact: Niklas Muennighoff
- Languages: Refer to bloom for pretraining & xP3 for finetuning language proportions. It understands both pretraining & finetuning languages.
Intended use
We recommend using the model to perform tasks expressed in natural language. For example, given the prompt "Translate to English: Je t’aime.", the model will most likely answer "I love you.". Some prompt ideas from our paper:
- 一个传奇的开端，一个不灭的神话，这不仅仅是一部电影，而是作为一个走进新时代的标签，永远彪炳史册。你认为这句话的立场是赞扬、中立还是批评?
- Suggest at least five related search terms to "Mạng neural nhân tạo".
- Write a fairy tale about a troll saving a princess from a dangerous dragon. The fairy tale is a masterpiece that has achieved praise worldwide and its moral is "Heroes Come in All Shapes and Sizes". Story (in Spanish):
- Explain in a sentence in Telugu what is backpropagation in neural networks.
Converted Models
Usage
Python via llm-rs:
Installation
Via pip: pip install llm-rs
Run inference
from llm_rs import AutoModel
#Load the model, define any model you like from the list above as the `model_file`
model = AutoModel.from_pretrained("rustformers/bloomz-ggml",model_file="bloomz-3b-q4_0-ggjt.bin")
#Generate
print(model.generate("The meaning of life is"))
Rust via Rustformers/llm:
Installation
git clone --recurse-submodules https://github.com/rustformers/llm.git
cd llm
cargo build --release
Run inference
cargo run --release -- bloom infer -m path/to/model.bin -p "Tell me how cool the Rust programming language is:"
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# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="rustformers/bloomz-ggml")