Instructions to use McGill-NLP/gemma-2-9b-it-Injongo-intent with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use McGill-NLP/gemma-2-9b-it-Injongo-intent with Transformers:

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

pipe = pipeline("text-generation", model="McGill-NLP/gemma-2-9b-it-Injongo-intent")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("McGill-NLP/gemma-2-9b-it-Injongo-intent")
model = AutoModelForCausalLM.from_pretrained("McGill-NLP/gemma-2-9b-it-Injongo-intent")
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 McGill-NLP/gemma-2-9b-it-Injongo-intent with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "McGill-NLP/gemma-2-9b-it-Injongo-intent"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "McGill-NLP/gemma-2-9b-it-Injongo-intent",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/McGill-NLP/gemma-2-9b-it-Injongo-intent

SGLang

How to use McGill-NLP/gemma-2-9b-it-Injongo-intent 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 "McGill-NLP/gemma-2-9b-it-Injongo-intent" \
    --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": "McGill-NLP/gemma-2-9b-it-Injongo-intent",
		"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 "McGill-NLP/gemma-2-9b-it-Injongo-intent" \
        --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": "McGill-NLP/gemma-2-9b-it-Injongo-intent",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use McGill-NLP/gemma-2-9b-it-Injongo-intent with Docker Model Runner:
```
docker model run hf.co/McGill-NLP/gemma-2-9b-it-Injongo-intent
```

gemma-2-9b-it-Injongo-intent / README.md

PeterYu

Update README.md

549e07b verified about 1 year ago

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metadata

license: cc-by-4.0
datasets:
  - masakhane/InjongoIntent
language:
  - en
  - am
  - ee
  - ha
  - ig
  - rw
  - ln
  - om
  - sn
  - sot
  - sw
  - tw
  - wo
  - xh
  - yo
  - zu
  - lg
base_model:
  - google/gemma-2-9b-it
library_name: transformers
metrics:
  - f1
tags:
  - llama-factory
  - full
  - generated_from_trainer

INJONGO: A Multicultural Intent Detection and Slot-filling Dataset for 16 African Languages

Evaluation Comparison

Zero-Shot Performance of LLMs on Intent Detection and Slot Filling

Intent Detection

Evaluation based on accuracy. Average computed on five templates, and on only African languages.

Model	eng	amh	ewe	hau	ibo	kin	lin	lug	orm	sna	sot	swa	twi	wol	xho	yor	zul	AVG
Llama 3.1 8B	27.6	1.9	2.1	4.8	5.5	3.3	5.3	2.4	1.6	2.8	2.9	14.1	2.6	4.0	3.2	3.5	2.8	3.9±2.4
Gemma 2 9B	77.6	49.2	6.1	40.8	31.5	23.8	22.2	23.2	7.7	29.7	19.9	70.0	21.0	13.8	40.1	32.2	36.3	29.2±8.7
Aya-101 13B	65.3	62.9	13.4	57.8	56.9	40.4	27.8	33.9	20.8	51.2	43.9	65.9	27.2	19.7	58.1	45.9	53.2	42.4±9.1
Gemma 2 27B	79.5	47.2	6.3	46.5	36.9	26.7	27.5	26.1	5.8	36.7	25.6	75.5	21.2	16.4	50.2	34.8	44.3	33.0±9.6
Llama 3.3 70B	81.1	56.2	9.5	52.3	52.4	35.0	37.5	37.7	12.4	32.3	30.5	80.6	29.3	20.9	43.5	41.4	43.9	38.5±9.5
Gemini 1.5 Pro	81.8	77.9	24.3	74.8	65.4	61.5	54.6	59.3	39.3	68.6	51.6	83.2	47.2	25.6	76.2	66.8	68.7	59.1±9.6
GPT-4o (Aug)	80.9	76.0	15.1	80.7	71.8	64.7	56.4	68.2	59.3	75.5	59.7	84.5	58.6	43.7	79.6	77.0	71.2	65.1±9.3
Gemma 2 9B IT (SFT)	81.2	83.3	77.1	89.8	86.7	78.6	85.8	83.6	84.6	87.7	76.8	88.8	82.6	85.1	89.1	87.9	78.9	84.1

Slot Filling

Evaluation based on F1-score. Average computed on five templates, and on only African languages.

Model	eng	amh	ewe	hau	ibo	kin	lin	lug	orm	sna	sot	swa	twi	wol	xho	yor	zul	AVG
Llama 3.1 8B	25.0	3.7	5.6	11.1	12.6	8.5	9.1	10.1	2.8	9.9	11.5	17.3	11.2	9.2	2.6	11.0	9.0	9.1±2.2
Gemma 2 IT 9B	34.1	4.5	0.3	7.4	10.6	5.0	6.0	5.6	0.1	7.3	10.8	21.2	2.4	2.6	2.2	5.2	8.2	6.2±2.9
Aya-101 13B	21.4	8.2	7.9	11.8	14.6	12.2	9.4	15.5	3.6	15.0	17.0	16.2	13.8	14.0	2.8	9.6	10.6	11.4±2.4
Gemma 2 IT 27B	49.8	15.7	9.5	24.1	25.2	21.7	15.2	28.4	2.6	29.8	28.0	40.2	24.3	23.3	4.5	28.1	31.0	22.0±5.8
Llama 3.3 70B Instruct	52.6	26.3	22.0	29.5	35.0	31.4	25.0	30.4	9.3	29.5	36.4	40.7	35.6	36.4	6.9	34.2	31.9	28.8±5.2
Gemini 1.5 Pro	52.8	15.2	18.7	31.9	35.8	34.4	34.9	34.4	12.2	36.8	43.0	37.5	34.5	34.2	6.9	33.2	38.6	30.1±6.1
GPT-4o (Aug)	55.4	22.8	19.4	37.8	38.9	36.4	33.5	35.3	13.0	40.2	40.9	46.5	40.1	37.9	10.0	42.4	37.6	33.3±6.0
Gemma 2 9B IT (SFT)	80.6	80.7	82.0	92.2	81.3	75.5	88.5	85.8	81.1	82.5	77.2	87.7	86.3	82.9	89.6	88.4	68.8	83.1

Bold values indicate the best performance for each language/metric.

Language Codes

eng: English
amh: Amharic
ewe: Ewe
hau: Hausa
ibo: Igbo
kin: Kinyarwanda
lin: Lingala
lug: Luganda
orm: Oromo
sna: Shona
sot: Sesotho
swa: Swahili
twi: Twi
wol: Wolof
xho: Xhosa
yor: Yoruba
zul: Zulu

Citation

@misc{yu2025injongo,
      title={INJONGO: A Multicultural Intent Detection and Slot-filling Dataset for 16 African Languages}, 
      author={Hao Yu and Jesujoba O. Alabi and Andiswa Bukula and Jian Yun Zhuang and En-Shiun Annie Lee and Tadesse Kebede Guge and Israel Abebe Azime and Happy Buzaaba and Blessing Kudzaishe Sibanda and Godson K. Kalipe and Jonathan Mukiibi and Salomon Kabongo Kabenamualu and Mmasibidi Setaka and Lolwethu Ndolela and Nkiruka Odu and Rooweither Mabuya and Shamsuddeen Hassan Muhammad and Salomey Osei and Sokhar Samb and Juliet W. Murage and Dietrich Klakow and David Ifeoluwa Adelani},
      year={2025},
      eprint={2502.09814},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2502.09814}, 
}

@misc{adelani2023sib200,
      title={SIB-200: A Simple, Inclusive, and Big Evaluation Dataset for Topic Classification in 200+ Languages and Dialects}, 
      author={David Ifeoluwa Adelani and Hannah Liu and Xiaoyu Shen and Nikita Vassilyev and Jesujoba O. Alabi and Yanke Mao and Haonan Gao and Annie En-Shiun Lee},
      year={2023},
      eprint={2309.07445},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}