🚀 Oolel：面向沃洛夫语的高性能开源大语言模型

尽管大语言模型领域有众多开源创新成果，但非洲语言在其中的代表性仍然不足。索亚纳德研究公司（Soynade Research） 正通过Oolel改变这一局面，Oolel是首个面向沃洛夫语的开源语言模型。

Oolel基于通义千问2.5（Qwen 2.5） 架构构建，将最先进的人工智能技术与深厚的沃洛夫语语言学专业知识相结合。通过精心策划的高质量数据，我们对Oolel进行了训练和优化，使其能够处理以下任务：

• 检索增强生成（RAG）：支持使用英语、法语或沃洛夫语上下文回答沃洛夫语查询。
• 英语与沃洛夫语的双向翻译
• 沃洛夫语自然文本生成
• 沃洛夫语数学运算
• 以及许多其他标准自然语言处理（NLP）任务：
  • 文本摘要
  • 文本编辑 等

🚀 快速开始

💻 使用示例

基础用法

from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

device = "cuda" 

model = AutoModelForCausalLM.from_pretrained(
    "soynade-research/Oolel-v0.1",
    torch_dtype = torch.bfloat16,
    device_map="auto")

tokenizer = AutoTokenizer.from_pretrained("soynade-research/Oolel-v0.1")

def generate_response(messages, max_new_tokens=1024, temperature=0.1):
    text = tokenizer.apply_chat_template(
          messages,
          tokenize=False,
          add_generation_prompt=True
)
    model_inputs = tokenizer([text], return_tensors="pt").to(device)
    generated_ids = model.generate(model_inputs.input_ids, max_new_tokens=max_new_tokens, temperature=temperature)
    
    generated_ids = [
    output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)]
    response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
    return response

高级用法

以下是一些不同任务的使用示例：

1. 翻译任务

system_prompt = "You're a Wolof AI assistant. Please always provide detailed and useful answers to the user queries."
messages = [
    {"role": "system", "content": system_prompt},
    {"role": "user", "content": "Translate to Wolof: Bassirou Diomaye Faye is the new Senegalese president. He is 44 years old"}
]
print(generate_response(messages))

2. 代码生成

system_prompt = "You're a Wolof AI assistant. Please always provide detailed and useful answers to the user queries"
messages = [
    {"role": "system", "content": system_prompt},
    {"role": "user", "content": "Bindal ab klaas Python buy wone ni ñuy jëfandikoo dataframe yi ci Pandas"}
]
print(generate_response(messages))

3. 问题解决

system_prompt = "You're a Wolof AI assistant. Please always provide detailed and useful answers to the user queries."
messages = [
    {"role": "system", "content": system_prompt},
    {"role": "user", "content": "Ndax nga mën ma won ni ñuy resolver problème bii: Fatou dafa jënd 3 kilo ceeb, 2 kilo diw ak 5 kilo sukër. Ceeb gi wenn kilo 500 CFA la, diw gi 1200 CFA kilo bi, sukër gi 750 CFA kilo bi. Ñaata la wara fay?"}
]
from pprint import pprint
pprint(generate_response(messages))

4. 文本生成（例如故事生成）

system_prompt = "You are a skilled Wolof storyteller (Gewël) with deep knowledge of African folktales and traditions. Write engaging stories in Wolof that reflect African cultural values and wisdom."
messages = [
    {"role": "system", "content": system_prompt},
    {"role": "user", "content": "Bindal ab léeb ci gaynde gi lekk muus mi"}
]
print(generate_response(messages, temperature=0.9))

5. 多轮对话 Oolel未针对多轮对话进行优化，但你可以尝试一下！

messages = [
   {"role": "user", "content": "Wax ma clan mooy CEDEAO ? Ci lan la liggeey?"},
   {"role": "assistant", "content": "CEDEAO mooy 'organisation' gu boole reew yi nekk ci pennc Afrika bi. Mu ngi sukkandiku ci wàll économie, politig, ak déggoo diggante reew yi"},
   {"role": "user", "content": "ñaata reew ñoo ci bokk?"}
]
print(generate_response(messages))

⚠️ 重要提示

务必添加系统提示！

👨‍💻 作者

• 亚亚·西（Yaya SY）：自然语言处理研究员（高效持续预训练方向）
• 迪乌拉·杜库雷（Dioula DOUCOURE）：数据与自然语言处理工程师

📄 许可证

本项目采用Apache-2.0许可证。