Instructions to use ciskoM/wolof-qwen-1.5b-gguf with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use ciskoM/wolof-qwen-1.5b-gguf with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="ciskoM/wolof-qwen-1.5b-gguf")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForMultimodalLM

tokenizer = AutoTokenizer.from_pretrained("ciskoM/wolof-qwen-1.5b-gguf")
model = AutoModelForMultimodalLM.from_pretrained("ciskoM/wolof-qwen-1.5b-gguf")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use ciskoM/wolof-qwen-1.5b-gguf with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "ciskoM/wolof-qwen-1.5b-gguf"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ciskoM/wolof-qwen-1.5b-gguf",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/ciskoM/wolof-qwen-1.5b-gguf

SGLang

How to use ciskoM/wolof-qwen-1.5b-gguf with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "ciskoM/wolof-qwen-1.5b-gguf" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ciskoM/wolof-qwen-1.5b-gguf",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "ciskoM/wolof-qwen-1.5b-gguf" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ciskoM/wolof-qwen-1.5b-gguf",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Unsloth Studio

How to use ciskoM/wolof-qwen-1.5b-gguf with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for ciskoM/wolof-qwen-1.5b-gguf to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for ciskoM/wolof-qwen-1.5b-gguf to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for ciskoM/wolof-qwen-1.5b-gguf to start chatting

Load model with FastModel

pip install unsloth
from unsloth import FastModel
model, tokenizer = FastModel.from_pretrained(
    model_name="ciskoM/wolof-qwen-1.5b-gguf",
    max_seq_length=2048,
)

Docker Model Runner
How to use ciskoM/wolof-qwen-1.5b-gguf with Docker Model Runner:
```
docker model run hf.co/ciskoM/wolof-qwen-1.5b-gguf
```

wolof-qwen-1.5b-gguf / README.md

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metadata

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 (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

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:

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.