Create README.md

README.md

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+---
+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.