Instructions to use dingli/xlnet_nlp_smartdispatch with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use dingli/xlnet_nlp_smartdispatch with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="dingli/xlnet_nlp_smartdispatch")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("dingli/xlnet_nlp_smartdispatch") model = AutoModelForCausalLM.from_pretrained("dingli/xlnet_nlp_smartdispatch", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use dingli/xlnet_nlp_smartdispatch with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "dingli/xlnet_nlp_smartdispatch" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "dingli/xlnet_nlp_smartdispatch", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/dingli/xlnet_nlp_smartdispatch
- SGLang
How to use dingli/xlnet_nlp_smartdispatch 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 "dingli/xlnet_nlp_smartdispatch" \ --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": "dingli/xlnet_nlp_smartdispatch", "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 "dingli/xlnet_nlp_smartdispatch" \ --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": "dingli/xlnet_nlp_smartdispatch", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use dingli/xlnet_nlp_smartdispatch with Docker Model Runner:
docker model run hf.co/dingli/xlnet_nlp_smartdispatch
Download config.json from dingli/xlnet_nlp_smartdispatch: direct link, hf CLI and curl.
- Browser
- Download file 927 Bytes
-
https://huggingface.co/dingli/xlnet_nlp_smartdispatch/resolve/main/config.json
- Command line
-
hf download hf://dingli/xlnet_nlp_smartdispatch/config.json
-
curl -L -o config.json https://huggingface.co/dingli/xlnet_nlp_smartdispatch/resolve/main/config.json
927 Bytes
| { | |
| "architectures": [ | |
| "XLNetLMHeadModel" | |
| ], | |
| "attn_type": "bi", | |
| "bi_data": false, | |
| "clamp_len": -1, | |
| "d_head": 64, | |
| "d_inner": 3072, | |
| "d_model": 768, | |
| "dropout": 0.1, | |
| "end_n_top": 5, | |
| "ff_activation": "relu", | |
| "finetuning_task": null, | |
| "id2label": { | |
| "0": "LABEL_0", | |
| "1": "LABEL_1" | |
| }, | |
| "initializer_range": 0.02, | |
| "is_decoder": false, | |
| "label2id": { | |
| "LABEL_0": 0, | |
| "LABEL_1": 1 | |
| }, | |
| "layer_norm_eps": 1e-12, | |
| "mem_len": null, | |
| "model_type": "xlnet", | |
| "n_head": 12, | |
| "n_layer": 12, | |
| "num_labels": 2, | |
| "output_attentions": false, | |
| "output_hidden_states": false, | |
| "output_past": true, | |
| "pruned_heads": {}, | |
| "reuse_len": null, | |
| "same_length": false, | |
| "start_n_top": 5, | |
| "summary_activation": "tanh", | |
| "summary_last_dropout": 0.1, | |
| "summary_type": "last", | |
| "summary_use_proj": true, | |
| "torchscript": false, | |
| "untie_r": true, | |
| "use_bfloat16": false, | |
| "vocab_size": 32000 | |
| } | |