Instructions to use aubmindlab/aragpt2-mega with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use aubmindlab/aragpt2-mega with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="aubmindlab/aragpt2-mega", trust_remote_code=True)# Load model directly from transformers import AutoModelForCausalLM model = AutoModelForCausalLM.from_pretrained("aubmindlab/aragpt2-mega", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use aubmindlab/aragpt2-mega with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "aubmindlab/aragpt2-mega" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "aubmindlab/aragpt2-mega", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/aubmindlab/aragpt2-mega
- SGLang
How to use aubmindlab/aragpt2-mega 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 "aubmindlab/aragpt2-mega" \ --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": "aubmindlab/aragpt2-mega", "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 "aubmindlab/aragpt2-mega" \ --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": "aubmindlab/aragpt2-mega", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use aubmindlab/aragpt2-mega with Docker Model Runner:
docker model run hf.co/aubmindlab/aragpt2-mega
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"activation_function": "gelu_new",
"architectures": [
"AraGPT2LMHeadModel"
],
"auto_map": {
"AutoConfig": "configuration_aragpt2.AraGPT2Config",
"AutoModelForCausalLM": "modeling_aragpt2.AraGPT2LMHeadModel",
"AutoModel": "modeling_aragpt2.AraGPT2Model"
},
"attention_probs_dropout_prob": 0.1,
"attn_pdrop": 0.1,
"bos_token_id": 0,
"embd_pdrop": 0.1,
"eos_token_id": 0,
"hidden_act": "gelu",
"hidden_dropout_prob": 0.1,
"initializer_range": 0.014142135623731,
"intermediate_size": 6144,
"layer_norm_epsilon": 1e-05,
"model_type": "aragpt2",
"n_ctx": 1024,
"n_embd": 1536,
"n_head": 24,
"n_inner": null,
"n_layer": 48,
"n_positions": 1024,
"resid_pdrop": 0.1,
"summary_activation": null,
"summary_first_dropout": 0.1,
"summary_proj_to_labels": true,
"summary_type": "cls_index",
"summary_use_proj": true,
"task_specific_params": {
"text-generation": {
"do_sample": true,
"max_length": 50,
"num_beams": 5,
"top_p": 0.95,
"repetition_penalty": 3.0,
"no_repeat_ngram_size": 3
}
},
"vocab_size": 64000,
"tokenizer_class": "GPT2Tokenizer"
} |