Instructions to use ShacklesLay/CPT4task2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ShacklesLay/CPT4task2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="ShacklesLay/CPT4task2")# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("ShacklesLay/CPT4task2") model = AutoModelForSeq2SeqLM.from_pretrained("ShacklesLay/CPT4task2") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use ShacklesLay/CPT4task2 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "ShacklesLay/CPT4task2" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "ShacklesLay/CPT4task2", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/ShacklesLay/CPT4task2
- SGLang
How to use ShacklesLay/CPT4task2 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 "ShacklesLay/CPT4task2" \ --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": "ShacklesLay/CPT4task2", "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 "ShacklesLay/CPT4task2" \ --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": "ShacklesLay/CPT4task2", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use ShacklesLay/CPT4task2 with Docker Model Runner:
docker model run hf.co/ShacklesLay/CPT4task2
| { | |
| "_name_or_path": "./experiments/fnlp/cpt-large_lr_2.1e-05_bsz_8_sd_44", | |
| "activation_dropout": 0.1, | |
| "activation_function": "gelu", | |
| "add_bias_logits": false, | |
| "add_final_layer_norm": false, | |
| "architectures": [ | |
| "CPTForConditionalGeneration" | |
| ], | |
| "attention_dropout": 0.1, | |
| "bos_token_id": 101, | |
| "classif_dropout": 0.1, | |
| "classifier_dropout": 0.0, | |
| "d_model": 1024, | |
| "decoder_attention_heads": 16, | |
| "decoder_ffn_dim": 4096, | |
| "decoder_layerdrop": 0.0, | |
| "decoder_layers": 4, | |
| "decoder_start_token_id": 102, | |
| "dropout": 0.1, | |
| "early_stopping": true, | |
| "encoder_attention_heads": 16, | |
| "encoder_ffn_dim": 4096, | |
| "encoder_layerdrop": 0.0, | |
| "encoder_layers": 24, | |
| "eos_token_id": 102, | |
| "forced_eos_token_id": 102, | |
| "gradient_checkpointing": false, | |
| "id2label": { | |
| "0": "LABEL_0", | |
| "1": "LABEL_1", | |
| "2": "LABEL_2" | |
| }, | |
| "init_std": 0.02, | |
| "is_encoder_decoder": true, | |
| "label2id": { | |
| "LABEL_0": 0, | |
| "LABEL_1": 1, | |
| "LABEL_2": 2 | |
| }, | |
| "max_position_embeddings": 1024, | |
| "model_type": "bart", | |
| "no_repeat_ngram_size": 3, | |
| "normalize_before": false, | |
| "normalize_embedding": true, | |
| "num_beams": 4, | |
| "num_hidden_layers": 24, | |
| "pad_token_id": 0, | |
| "scale_embedding": false, | |
| "task_specific_params": { | |
| "summarization": { | |
| "length_penalty": 1.0, | |
| "max_length": 128, | |
| "min_length": 12, | |
| "num_beams": 4 | |
| }, | |
| "summarization_cnn": { | |
| "length_penalty": 2.0, | |
| "max_length": 142, | |
| "min_length": 56, | |
| "num_beams": 4 | |
| }, | |
| "summarization_xsum": { | |
| "length_penalty": 1.0, | |
| "max_length": 62, | |
| "min_length": 11, | |
| "num_beams": 6 | |
| } | |
| }, | |
| "tokenizer_class": "BertTokenizer", | |
| "torch_dtype": "float32", | |
| "transformers_version": "4.30.0.dev0", | |
| "use_cache": true, | |
| "vocab_size": 51288 | |
| } | |