---
library_name: transformers
datasets:
- Svngoku/french-multilingual-reward-bench-dpo
language:
- fr
base_model:
- CohereForAI/aya-expanse-8b
metrics:
- bleu
- accuracy
pipeline_tag: text-generation
---
# Model Card for French Aya Expanse 8B 🇫🇷
**Aya Expanse 8B** is an open-weight research release of a model with highly advanced multilingual capabilities. It focuses on pairing a highly performant pre-trained [Command family](https://huggingface.co/CohereForAI/c4ai-command-r-plus) of models with the result of a year’s dedicated research from [Cohere For AI](https://cohere.for.ai/), including [data arbitrage](https://arxiv.org/abs/2408.14960), [multilingual preference training](https://arxiv.org/abs/2407.02552), [safety tuning](https://arxiv.org/abs/2406.18682), and [model merging](https://arxiv.org/abs/2410.10801). The result is a powerful multilingual large language model.
This model card corresponds to the 8-billion version of the Aya Expanse model. We also released an 32-billion version which you can find [here](https://huggingface.co/CohereForAI/aya-expanse-32B).
- Developed by: [Cohere For AI](https://cohere.for.ai/)
- Point of Contact: Cohere For AI: [cohere.for.ai](https://cohere.for.ai/)
- License: [CC-BY-NC](https://cohere.com/c4ai-cc-by-nc-license), requires also adhering to [C4AI's Acceptable Use Policy](https://docs.cohere.com/docs/c4ai-acceptable-use-policy)
- Model: Aya Expanse 8B
- Model Size: 8 billion parameters
### Supported Languages
The model cover 23 languages: Arabic, Chinese (simplified & traditional), Czech, Dutch, English, French, German, Greek, Hebrew, Hebrew, Hindi, Indonesian, Italian, Japanese, Korean, Persian, Polish, Portuguese, Romanian, Russian, Spanish, Turkish, Ukrainian, and Vietnamese.
But the fine-tuned version is focus on `French`
### How to Use Aya Expanse
Install the transformers library and load Aya Expanse 8B as follows:
```python
from transformers import AutoTokenizer, AutoModelForCausalLM
model_id = "Svngoku/French-Aya-Expanse-8B"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id)
# Format the message with the chat template
messages = [{"role": "user", "content": "Quels est la superficie de Paris"}]
input_ids = tokenizer.apply_chat_template(messages, tokenize=True, add_generation_prompt=True, return_tensors="pt")
## <|START_OF_TURN_TOKEN|><|USER_TOKEN|>Anneme onu ne kadar sevdiÄŸimi anlatan bir mektup yaz<|END_OF_TURN_TOKEN|><|START_OF_TURN_TOKEN|><|CHATBOT_TOKEN|>
gen_tokens = model.generate(
input_ids,
max_new_tokens=100,
do_sample=True,
temperature=0.3,
)
gen_text = tokenizer.decode(gen_tokens[0])
print(gen_text)
```
### Example Notebooks
**Fine-Tuning:**
- [Detailed Fine-Tuning Notebook](https://colab.research.google.com/drive/1ryPYXzqb7oIn2fchMLdCNSIH5KfyEtv4).
**Community-Contributed Use Cases:**:
The following notebooks contributed by *Cohere For AI Community* members show how Aya Expanse can be used for different use cases:
- [Mulitlingual Writing Assistant](https://colab.research.google.com/drive/1SRLWQ0HdYN_NbRMVVUHTDXb-LSMZWF60)
- [AyaMCooking](https://colab.research.google.com/drive/1-cnn4LXYoZ4ARBpnsjQM3sU7egOL_fLB?usp=sharing)
- [Multilingual Question-Answering System](https://colab.research.google.com/drive/1bbB8hzyzCJbfMVjsZPeh4yNEALJFGNQy?usp=sharing)
## Model Details
**Input**: Models input text only.
**Output**: Models generate text only.
**Model Architecture**: Aya Expanse 8B is an auto-regressive language model that uses an optimized transformer architecture. Post-training includes supervised finetuning, preference training, and model merging.
**Languages covered**: The model is particularly optimized for multilinguality and supports the following languages: Arabic, Chinese (simplified & traditional), Czech, Dutch, English, French, German, Greek, Hebrew, Hindi, Indonesian, Italian, Japanese, Korean, Persian, Polish, Portuguese, Romanian, Russian, Spanish, Turkish, Ukrainian, and Vietnamese
**Context length**: 8K
For more details about how the model was trained, check out [our blogpost](https://huggingface.co/blog/aya-expanse).
### Evaluation
They evaluated Aya Expanse 8B against Gemma 2 9B, Llama 3.1 8B, Ministral 8B, and Qwen 2.5 7B using the `dolly_human_edited` subset from the [Aya Evaluation Suite dataset](https://huggingface.co/datasets/CohereForAI/aya_evaluation_suite) and m-ArenaHard, a dataset based on the [Arena-Hard-Auto dataset](https://huggingface.co/datasets/lmarena-ai/arena-hard-auto-v0.1) and translated to the 23 languages we support in Aya Expanse 8B. Win-rates were determined using gpt-4o-2024-08-06 as a judge. For a conservative benchmark, we report results from gpt-4o-2024-08-06, though gpt-4o-mini scores showed even stronger performance.
The m-ArenaHard dataset, used to evaluate Aya Expanse’s capabilities, is publicly available [here](https://huggingface.co/datasets/CohereForAI/m-ArenaHard).
### Model Card Contact
For errors or additional questions about details in this model card, contact info@for.ai.
### Terms of Use
They hope that the release of this model will make community-based research efforts more accessible, by releasing the weights of a highly performant multilingual model to researchers all over the world. This model is governed by a [CC-BY-NC](https://cohere.com/c4ai-cc-by-nc-license) License with an acceptable use addendum, and also requires adhering to [C4AI's Acceptable Use Policy](https://docs.cohere.com/docs/c4ai-acceptable-use-policy).