---
license: cc-by-nc-4.0
language:
- wo
- fr
- en
base_model: Qwen/Qwen2.5-1.5B-Instruct
pipeline_tag: text-generation
library_name: transformers
tags:
- wolof
- senegal
- translation
- low-resource
- qwen2.5
- unsloth
- lora
---

# Wolof Qwen2.5-1.5B — conversational + translation (wo · fr · en)

A fine-tune of **Qwen2.5-1.5B-Instruct** for the **Wolof** language. It can hold a
chat conversation and translate between **Wolof ↔ French ↔ English**.

Trained with [Unsloth](https://github.com/unslothai/unsloth) (QLoRA, 4-bit) on a
single GPU.

## Intended use

- Wolof ⇄ French ⇄ English translation inside a chat interface
- Experiments and research on low-resource (Wolof) language modelling
- A base to iterate on with more conversational Wolof data

## How to use

**Transformers**
```python
from transformers import AutoModelForCausalLM, AutoTokenizer

tok = AutoTokenizer.from_pretrained("ciskoM/wolof-qwen-1.5b")
model = AutoModelForCausalLM.from_pretrained("ciskoM/wolof-qwen-1.5b", device_map="auto")

msgs = [{"role": "user", "content": "Translate to Wolof: Good morning, how are you?"}]
ids = tok.apply_chat_template(msgs, add_generation_prompt=True, return_tensors="pt").to(model.device)
print(tok.decode(model.generate(ids, max_new_tokens=64)[0], skip_special_tokens=True))
```

**GGUF (Ollama / LM Studio / llama.cpp)** — see the companion repo
`ciskoM/wolof-qwen-1.5b-gguf` (q4_k_m). With Ollama:
```bash
ollama create wolof -f Modelfile   # Modelfile: FROM ./wolof-qwen.Q4_K_M.gguf
ollama run wolof
```

Example prompts: `Translate to Wolof: …`, `Traduis en wolof : …`,
`What does "Jamm rekk" mean in English?`, `Nanga def?`

## Training data

~120k instruction pairs (ShareGPT format), balanced across translation
directions plus monolingual Wolof:

| Source | Content |
|---|---|
| `bilalfaye/english-wolof-french-translation` | aligned en/wo/fr sentences |
| `galsenai/centralized_wolof_french_translation_data` | aligned wo/fr sentences |
| ALMA / DLIR Wolof e-books (13) | OCR'd authentic Wolof prose |
| wolofresources.org PDFs + UDHR Wolof | proverbs, tales, vocabulary |
| Wolof New Testament (bibliamundi) | clean verse-numbered scripture |

The dataset is ~96% translation pairs; bidirectional pairs (en2wo, wo2en,
fr2wo, wo2fr) were generated with varied prompt templates.

## Training procedure

- Base: `Qwen/Qwen2.5-1.5B-Instruct`, loaded 4-bit
- LoRA: r=16, alpha=16, dropout=0, on all attention + MLP projections
- 1 epoch, lr 2e-4, linear schedule, adamw_8bit, seq len 2048
- Trained on assistant responses only (`train_on_responses_only`)
- Tooling: Unsloth + TRL `SFTTrainer`/`SFTConfig`

## Limitations & honest caveats

- **Translation > conversation.** Because the data is overwhelmingly translation
  pairs, the model is strong at translating but **limited at free-flowing Wolof
  conversation**. More conversational Wolof data is the fix, not a bigger model.
- Wolof is low-resource; expect errors, especially on rare topics, long inputs,
  and Wolof orthography variants.
- Some training text came from OCR (scanned PDFs) and subtitle-sourced corpora —
  there may be noise, odd register, or artifacts.
- Not safety-tuned; do not use for high-stakes decisions.

## Data & licensing note

Model weights derive from Qwen2.5 (Apache-2.0). However, the **training data
provenance is mixed**: the translation corpora are subtitle/film-sourced and the
scripture is from a translation that may carry its own copyright. This card is
released **CC-BY-NC-4.0** as a conservative default. **Verify the licenses of the
underlying datasets before any commercial use or redistribution.**

## Acknowledgements

Datasets by bilalfaye and the GalsenAI community; ALMA/DLIR African Language
Materials Archive; wolofresources.org; Biblia Mundi. Built with Unsloth.