Instructions to use MaLA-LM/lucky52-bloom-7b1-no-29 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use MaLA-LM/lucky52-bloom-7b1-no-29 with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="MaLA-LM/lucky52-bloom-7b1-no-29")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("MaLA-LM/lucky52-bloom-7b1-no-29")
model = AutoModelForCausalLM.from_pretrained("MaLA-LM/lucky52-bloom-7b1-no-29")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use MaLA-LM/lucky52-bloom-7b1-no-29 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "MaLA-LM/lucky52-bloom-7b1-no-29"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "MaLA-LM/lucky52-bloom-7b1-no-29",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/MaLA-LM/lucky52-bloom-7b1-no-29

SGLang

How to use MaLA-LM/lucky52-bloom-7b1-no-29 with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "MaLA-LM/lucky52-bloom-7b1-no-29" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "MaLA-LM/lucky52-bloom-7b1-no-29",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "MaLA-LM/lucky52-bloom-7b1-no-29" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "MaLA-LM/lucky52-bloom-7b1-no-29",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use MaLA-LM/lucky52-bloom-7b1-no-29 with Docker Model Runner:
```
docker model run hf.co/MaLA-LM/lucky52-bloom-7b1-no-29
```

lucky52-bloom-7b1-no-29 / README.md

jisx

Update README.md

ceedaeb verified over 1 year ago

preview code

Raw

History Blame Contribute Delete

1.81 kB

metadata

library_name: transformers
pipeline_tag: text-generation
language:
  - multilingual
tags:
  - generation
  - question answering
  - instruction tuning
datasets:
  - MBZUAI/Bactrian-X
license: cc-by-nc-4.0

Model Description

This HF repository hosts instruction fine-tuned multilingual BLOOM model using the parallel instruction dataset called Bactrain-X in 52 languages. We progressively add a language during instruction fine-tuning at each time, and train 52 models in total. Then, we evaluate those models in three multilingual benchmarks.

Please refer to our paper for more details.

Base model: BLOOM 7B1
Instruction languages: English, Chinese, Afrikaans, Arabic, Azerbaijani, Bengali, Czech, German, Spanish, Estonian, Farsi, Finnish, French, Galician, Gujarati, Hebrew, Hindi, Croatian, Indonesian, Italian, Japanese, Georgian, Kazakh, Khmer, Korean, Lithuanian, Latvian, Macedonian, Malayalam
Instruction language codes: en, zh, af, ar, az, bn, cs, de, es, et, fa, fi, fr, gl, gu, he, hi, hr, id, it, ja, ka, kk, km, ko, lt, lv, mk, ml
Training method: full-parameter fine-tuning.

Usage

The model checkpoint should be loaded using transformers library.

from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("MaLA-LM/lucky52-bloom-7b1-no-29")
model = AutoModelForCausalLM.from_pretrained("MaLA-LM/lucky52-bloom-7b1-no-29")

Citation

@inproceedings{ji2025lucky52,
      title={How Many Languages Make Good Multilingual Instruction Tuning? A Case Study on BLOOM}, 
      author={Shaoxiong Ji and Pinzhen Chen},
      year={2025},
      booktitle={Proceedings of COLING},
      url={https://arxiv.org/abs/2404.04850}, 
}