Instructions to use skrishna/ToxicityModel with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use skrishna/ToxicityModel with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="skrishna/ToxicityModel")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("skrishna/ToxicityModel") model = AutoModelForCausalLM.from_pretrained("skrishna/ToxicityModel", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use skrishna/ToxicityModel with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "skrishna/ToxicityModel" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "skrishna/ToxicityModel", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/skrishna/ToxicityModel
- SGLang
How to use skrishna/ToxicityModel 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 "skrishna/ToxicityModel" \ --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": "skrishna/ToxicityModel", "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 "skrishna/ToxicityModel" \ --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": "skrishna/ToxicityModel", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use skrishna/ToxicityModel with Docker Model Runner:
docker model run hf.co/skrishna/ToxicityModel
File size: 956 Bytes
90288e2 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 | {
"_name_or_path": "nicholasKluge/ToxicityModel",
"architectures": [
"GPTNeoXForCausalLM"
],
"attention_dropout": 0.0,
"attention_probs_dropout_prob": 0.1,
"bos_token_id": 0,
"classifier_dropout": null,
"eos_token_id": 2,
"hidden_act": "gelu",
"hidden_dropout": 0.0,
"hidden_dropout_prob": 0.1,
"hidden_size": 768,
"id2label": {
"0": "REWARD"
},
"initializer_range": 0.02,
"intermediate_size": 3072,
"label2id": {
"REWARD": 0
},
"layer_norm_eps": 1e-05,
"max_position_embeddings": 514,
"model_type": "gpt_neox",
"num_attention_heads": 12,
"num_hidden_layers": 12,
"pad_token_id": 1,
"position_embedding_type": "absolute",
"rope_scaling": null,
"rotary_emb_base": 10000,
"rotary_pct": 0.25,
"tie_word_embeddings": false,
"torch_dtype": "float32",
"transformers_version": "4.32.1",
"type_vocab_size": 1,
"use_cache": true,
"use_parallel_residual": true,
"vocab_size": 50265
}
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