Instructions to use inceptionai/Jais-2-70B-Chat with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use inceptionai/Jais-2-70B-Chat with Transformers:

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

pipe = pipeline("text-generation", model="inceptionai/Jais-2-70B-Chat")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("inceptionai/Jais-2-70B-Chat")
model = AutoModelForCausalLM.from_pretrained("inceptionai/Jais-2-70B-Chat")
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

vLLM

How to use inceptionai/Jais-2-70B-Chat with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "inceptionai/Jais-2-70B-Chat"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "inceptionai/Jais-2-70B-Chat",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/inceptionai/Jais-2-70B-Chat

SGLang

How to use inceptionai/Jais-2-70B-Chat 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 "inceptionai/Jais-2-70B-Chat" \
    --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": "inceptionai/Jais-2-70B-Chat",
		"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 "inceptionai/Jais-2-70B-Chat" \
        --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": "inceptionai/Jais-2-70B-Chat",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use inceptionai/Jais-2-70B-Chat with Docker Model Runner:
```
docker model run hf.co/inceptionai/Jais-2-70B-Chat
```

MADAR in training data

by cepin - opened Jan 3

Discussion

cepin

Jan 3

Thanks for open-sourcing this great model! I've been comparing the translation quality of multiple LLMs in English to Arabic dialects and Jais scores very well on MADAR dataset. Was this dataset used as a part of the training data? The BLEU scores would suggest it was, since Jais scores around 40-50 BLEU on some of the test sets, compared to ~15 BLEU for gpt-4.1. Do you have any suggestions for different test sets that could be used for a fair comparison?

SarahAlBarri

Inception org Jan 6

Hi @cepin , Jais-2 was trained on the MADAR training and validation sets only. It was not trained on the MADAR test sets. We also ran our own evaluations on MADAR test data, and Jais-2 performs among the best models of its size on this benchmark. Please ensure that you are using the testing subset to evaluate Jais-2 performance.

SarahAlBarri changed discussion status to closed Jan 7

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