Text Classification
Transformers
ONNX
Safetensors
llama
text-generation
voice-ai
turn-detection
end-of-utterance
end-of-turn
conversational-ai
livekit
quantized
knowledge-distillation
text-embeddings-inference
Instructions to use livekit/turn-detector with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use livekit/turn-detector with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="livekit/turn-detector")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("livekit/turn-detector") model = AutoModelForCausalLM.from_pretrained("livekit/turn-detector", device_map="auto") - Inference
- Notebooks
- Google Colab
- Kaggle
File size: 793 Bytes
f9967f2 ffac5c6 f9967f2 | 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 | {
"_attn_implementation_autoset": true,
"_name_or_path": "/tmp/tmpdhw4_gdh",
"architectures": [
"LlamaForCausalLM"
],
"attention_bias": false,
"attention_dropout": 0.0,
"bos_token_id": 0,
"eos_token_id": 0,
"head_dim": 64,
"hidden_act": "silu",
"hidden_size": 576,
"initializer_range": 0.041666666666666664,
"intermediate_size": 1536,
"is_llama_config": true,
"max_position_embeddings": 8192,
"mlp_bias": false,
"model_type": "llama",
"num_attention_heads": 9,
"num_hidden_layers": 30,
"num_key_value_heads": 3,
"pretraining_tp": 1,
"rms_norm_eps": 1e-05,
"rope_interleaved": false,
"rope_scaling": null,
"rope_theta": 100000,
"tie_word_embeddings": true,
"transformers_version": "4.46.3",
"use_cache": true,
"vocab_size": 49154
}
|