Instructions to use blue-prince/aicrowd-big-baby-robo-baby-8-cps with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use blue-prince/aicrowd-big-baby-robo-baby-8-cps with Transformers:

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

pipe = pipeline("text-generation", model="blue-prince/aicrowd-big-baby-robo-baby-8-cps")

# Load model directly
from transformers import AutoTokenizer, AutoModelForMultimodalLM

tokenizer = AutoTokenizer.from_pretrained("blue-prince/aicrowd-big-baby-robo-baby-8-cps")
model = AutoModelForMultimodalLM.from_pretrained("blue-prince/aicrowd-big-baby-robo-baby-8-cps")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use blue-prince/aicrowd-big-baby-robo-baby-8-cps with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "blue-prince/aicrowd-big-baby-robo-baby-8-cps"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "blue-prince/aicrowd-big-baby-robo-baby-8-cps",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/blue-prince/aicrowd-big-baby-robo-baby-8-cps

SGLang

How to use blue-prince/aicrowd-big-baby-robo-baby-8-cps 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 "blue-prince/aicrowd-big-baby-robo-baby-8-cps" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "blue-prince/aicrowd-big-baby-robo-baby-8-cps",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

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 "blue-prince/aicrowd-big-baby-robo-baby-8-cps" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "blue-prince/aicrowd-big-baby-robo-baby-8-cps",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use blue-prince/aicrowd-big-baby-robo-baby-8-cps with Docker Model Runner:
```
docker model run hf.co/blue-prince/aicrowd-big-baby-robo-baby-8-cps
```

aicrowd-big-baby-robo-baby-8-cps

This model is a fine-tuned version of on an unknown dataset.

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: 0.001
train_batch_size: 2048
eval_batch_size: 8
seed: 42
distributed_type: multi-GPU
num_devices: 8
total_train_batch_size: 16384
total_eval_batch_size: 64
optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
lr_scheduler_type: cosine
lr_scheduler_warmup_steps: 1000
training_steps: 91552

Training results

Framework versions

Transformers 4.57.2
Pytorch 2.10.0+cu130
Datasets 4.5.0
Tokenizers 0.22.2

Downloads last month: 1

Safetensors

Model size

55.6M params

Tensor type

F32