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---
pipeline_tag: text-generation
---
# Oolel: A High-Performing Open LLM for Wolof
<video controls autoplay src="https://cdn-uploads.huggingface.co/production/uploads/62e335bbf15e7fce909fe5d4/liiZ1rAkiIgGpgN_jqwq6.mp4"></video>
Despite numerous open-source innovations in large language models, African languages have remained underrepresented.
**Soynade Research** is transforming this landscape with Oolel, the first open-source language model for Wolof.
Built on the **Qwen 2.5** architecture, Oolel combines state-of-the-art AI technology with deep Wolof linguistic expertise. With careful high-quality curated data, we trained and optimized Oolel for the following tasks:
- **RAG** supporting Wolof queries with English, French, or Wolof context.
- **Bidirectional translation between English and Wolof**
- **Natural text generation in Wolof**
- **Math in Wolof**
- **And many other standard NLP tasks**:
- Summarization
- Text edition
- etc
## 3. Usage
**!!! It's important to add your system prompt !!!**
Here provides a code snippet with apply_chat_template to show you how to load the tokenizer and model and how to generate contents.
```python
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
```
**Some tasks examples:**
1. **Translation Tasks**
```python
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. **Code generation**
```python
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. **Problem Solving**
```python
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. **Text Generation** (e.g. story generation)
```python
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. **Multi-turn conversations**
Oolel is not optimized for multi-turn conversations, but you can try it!
```bash
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))
```
## Authors
- [**Yaya SY**](https://x.com/seygalare): NLP Researcher (Efficient Continued Pretraining)
- [**Dioula DOUCOURE**](https://x.com/DioulaD): Data & NLP Engineer

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"_name_or_path": "Soyno/Oolel-v0.1",
"architectures": [
"Qwen2ForCausalLM"
],
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"hidden_size": 3584,
"initializer_range": 0.02,
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"max_position_embeddings": 32768,
"max_window_layers": 28,
"model_type": "qwen2",
"num_attention_heads": 28,
"num_hidden_layers": 28,
"num_key_value_heads": 4,
"rms_norm_eps": 1e-06,
"rope_scaling": null,
"rope_theta": 1000000.0,
"sliding_window": null,
"tie_word_embeddings": false,
"torch_dtype": "float32",
"transformers_version": "4.47.0.dev0",
"use_cache": true,
"use_sliding_window": false,
"vocab_size": 152064
}

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"repetition_penalty": 1.05,
"temperature": 0.7,
"top_k": 20,
"top_p": 0.8,
"transformers_version": "4.47.0.dev0"
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