README.md

---
license: bigscience-bloom-rail-1.0
---

# Bloom CTranslate2's model

This is a collection of some of the [Bigscience Bloom](https://huggingface.co/bigscience/bloom) exported to
[CTranslate2](https://github.com/OpenNMT/CTranslate2) model format. This allows to load and usage these models
efficently on CPU or GPU.

## Models

The models have been converted to *float16* and can be load in with any other quantification method (e.g. *int 8*).


| Model name | Description |
| --- | --- |
| [bloom-560m](https://huggingface.co/bigscience/bloom-560m) |  560M parameter model pretrained on ROOTS|
| [bloom-3b](https://huggingface.co/bigscience/bloom-3b) | 3B parameter model pretrained on ROOTS 
| [bloomz-7b1](https://huggingface.co/bigscience/bloomz-7b1) |  7.1B parameter model finetuned on xP3|
| [bloomz-7b1-mt](https://huggingface.co/bigscience/bloomz-7b1-mt) |  7.1B parameter model finetuned on xP3mt |
| [mt0-xxl-mt](https://huggingface.co/bigscience/mt0-xxl-mt) |  13B parameter model finetuned on xP3| 

## Simple code to use them

Install dependencies:

```shell
pip install huggingface_hub ctranslate2 transformers torch
```

Usage:

```python
model_name = "bloomz-7b1"
prompt = "Hello, I am Joan and I am from Barcelona and"

repo_id = "jordimas/bloom-ctranslate2"
output_dir = "output/"

kwargs = {
    "local_dir" : output_dir,
    "local_dir_use_symlinks" : False,
}
huggingface_hub.snapshot_download(repo_id = repo_id, allow_patterns=f"*{model_name}*", **kwargs)

model = f"{output_dir}{model_name}"
print(f"model: {model}")
generator = ctranslate2.Generator(model, compute_type="int8")
tokenizer = transformers.AutoTokenizer.from_pretrained(model)

start_tokens = tokenizer.convert_ids_to_tokens(tokenizer.encode(prompt))
results = generator.generate_batch([start_tokens], max_length=90)
result = tokenizer.decode(results[0].sequences_ids[0])
print(f"Result: {result}")
```