Instructions to use or4cl3ai/SquanchNastyAI with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use or4cl3ai/SquanchNastyAI with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="or4cl3ai/SquanchNastyAI")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("or4cl3ai/SquanchNastyAI", dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use or4cl3ai/SquanchNastyAI with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "or4cl3ai/SquanchNastyAI" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "or4cl3ai/SquanchNastyAI", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/or4cl3ai/SquanchNastyAI
- SGLang
How to use or4cl3ai/SquanchNastyAI 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 "or4cl3ai/SquanchNastyAI" \ --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": "or4cl3ai/SquanchNastyAI", "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 "or4cl3ai/SquanchNastyAI" \ --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": "or4cl3ai/SquanchNastyAI", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use or4cl3ai/SquanchNastyAI with Docker Model Runner:
docker model run hf.co/or4cl3ai/SquanchNastyAI
Update config.json
Browse files- config.json +37 -15
config.json
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{
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"model_type": "SquanchNasty AI Model",
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"pipeline": "
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"max_length":
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"num_return_sequences":
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"do_sample": true,
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"num_beams":
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"no_repeat_ngram_size":
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"response_length":
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"num_proactive_sequences":
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"proactive_chance": 0.
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"pipelines": [
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{
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"name": "advanced_conversation",
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"type": "transformer",
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"parameters": {
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"num_layers":
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"hidden_size":
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"num_heads":
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"attention_dropout": 0.
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"relu_dropout": 0.
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"layer_norm_epsilon": 1e-6,
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"use_context_window": true,
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"context_window_size":
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"use_self_attention": true,
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"use_self_feedback": true,
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"use_transfer_learning": true,
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"use_generative_adversarial_networks": true,
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"use_autoregressive_models": true,
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"use_recurrent_networks": true,
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"use_transformer_networks": true
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}
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}
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]
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{
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"model_type": "SquanchNasty AI Model",
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"pipeline": "advanced_conversation",
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"max_length": 4096,
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"num_return_sequences": 3,
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"do_sample": true,
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"num_beams": 8,
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"no_repeat_ngram_size": 6,
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"response_length": 4096,
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"num_proactive_sequences": 5,
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"proactive_chance": 0.75,
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"pipelines": [
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{
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"name": "advanced_conversation",
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"type": "transformer",
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"parameters": {
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"num_layers": 36,
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"hidden_size": 2048,
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"num_heads": 24,
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"attention_dropout": 0.15,
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"relu_dropout": 0.15,
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"layer_norm_epsilon": 1e-6,
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"use_context_window": true,
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"context_window_size": 10,
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"use_self_attention": true,
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"use_self_feedback": true,
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"use_transfer_learning": true,
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"use_generative_adversarial_networks": true,
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"use_autoregressive_models": true,
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"use_recurrent_networks": true,
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"use_transformer_networks": true,
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"fine_tune_hyperparameters": true,
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"data_augmentation": true,
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"reinforcement_learning": true,
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"transfer_learning": true,
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"multi_modal_learning": true,
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"ethical_considerations": true,
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"continual_learning": true,
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"explainability": true,
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"privacy_and_security": true,
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"collaborative_learning": true,
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"performance_optimization": true,
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"scalability": true,
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"reproducibility": true,
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"documentation": true,
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"algorithm_selection": true,
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"hyperparameter_tuning": true,
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"ensemble_learning": true,
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"interpretability": true,
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"fairness_and_bias_mitigation": true,
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"adversarial_defense": true,
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"responsible_ai_practices": true,
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"model_monitoring": true
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}
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}
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]
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