Instructions to use nightdessert/WeCheck with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use nightdessert/WeCheck with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="nightdessert/WeCheck")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("nightdessert/WeCheck") model = AutoModelForSequenceClassification.from_pretrained("nightdessert/WeCheck") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use nightdessert/WeCheck with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "nightdessert/WeCheck" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "nightdessert/WeCheck", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/nightdessert/WeCheck
- SGLang
How to use nightdessert/WeCheck 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 "nightdessert/WeCheck" \ --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": "nightdessert/WeCheck", "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 "nightdessert/WeCheck" \ --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": "nightdessert/WeCheck", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use nightdessert/WeCheck with Docker Model Runner:
docker model run hf.co/nightdessert/WeCheck
Commit ·
a6e8bce
1
Parent(s): 5fdaff0
Upload 2 files
Browse files- config.json +45 -0
- pytorch_model.bin +3 -0
config.json
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{
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"_name_or_path": "/mnt/wuwenhao/baidu/personal-code/pretrained_models/anli_check",
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"architectures": [
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"DebertaV2ForSequenceClassification"
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],
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"attention_probs_dropout_prob": 0.1,
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"hidden_act": "gelu",
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"hidden_dropout_prob": 0.1,
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"hidden_size": 1024,
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"id2label": {
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"0": "entailment",
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"1": "neutral",
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"2": "contradiction"
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},
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"initializer_range": 0.02,
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"intermediate_size": 4096,
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"label2id": {
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"contradiction": 2,
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"entailment": 0,
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"neutral": 1
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},
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"layer_norm_eps": 1e-07,
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"max_position_embeddings": 512,
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"max_relative_positions": -1,
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"model_type": "deberta-v2",
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"norm_rel_ebd": "layer_norm",
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"num_attention_heads": 16,
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"num_hidden_layers": 24,
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"pad_token_id": 0,
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"pooler_dropout": 0,
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"pooler_hidden_act": "gelu",
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"pooler_hidden_size": 1024,
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"pos_att_type": [
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"p2c",
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"c2p"
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],
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"position_biased_input": false,
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"position_buckets": 256,
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"relative_attention": true,
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"share_att_key": true,
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"torch_dtype": "float32",
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"transformers_version": "4.25.1",
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"type_vocab_size": 0,
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"vocab_size": 128100
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}
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pytorch_model.bin
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version https://git-lfs.github.com/spec/v1
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oid sha256:2354679a344ffeeacdb6ee38565df84366bc0521d8b74a7c39cce7b2db27c1dd
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size 1740397483
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