Create README.md

README.md

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+---
+library_name: transformers
+language:
+- wo
+- en
+license: apache-2.0
+pipeline_tag: text2text-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-Small-v0.1",
+    torch_dtype = torch.bfloat16,
+    device_map="auto")
+
+tokenizer = AutoTokenizer.from_pretrained("soynade-research/Oolel-Small-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