Instructions to use astroa7m/Jais-AOU with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use astroa7m/Jais-AOU with Transformers:

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

pipe = pipeline("text-generation", model="astroa7m/Jais-AOU", trust_remote_code=True)
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("astroa7m/Jais-AOU", trust_remote_code=True, dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use astroa7m/Jais-AOU with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "astroa7m/Jais-AOU"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "astroa7m/Jais-AOU",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/astroa7m/Jais-AOU

SGLang

How to use astroa7m/Jais-AOU 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 "astroa7m/Jais-AOU" \
    --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": "astroa7m/Jais-AOU",
		"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 "astroa7m/Jais-AOU" \
        --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": "astroa7m/Jais-AOU",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use astroa7m/Jais-AOU with Docker Model Runner:
```
docker model run hf.co/astroa7m/Jais-AOU
```

Jais-AOU

This model is a fine-tuned version of inceptionai/jais-family-590m-chat on an unknown dataset. It achieves the following results on the evaluation set:

Loss: 0.7540

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 2e-05
train_batch_size: 8
eval_batch_size: 8
seed: 42
gradient_accumulation_steps: 4
total_train_batch_size: 32
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
lr_scheduler_warmup_steps: 50
num_epochs: 12
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss
1.1215	1.7699	50	1.1407
0.5782	3.5398	100	0.8883
0.3542	5.3097	150	0.7643
0.2372	7.0796	200	0.7418
0.203	8.8496	250	0.7372
0.1891	10.6195	300	0.7540

Framework versions

Transformers 4.45.2
Pytorch 2.4.0
Datasets 3.0.1
Tokenizers 0.20.0

Downloads last month: 4

Safetensors

Model size

0.8B params

Tensor type

F32

Model tree for astroa7m/Jais-AOU

Base model

inceptionai/jais-family-590m

Finetuned

inceptionai/jais-family-590m-chat

Finetuned

(3)

this model