Text Classification
Transformers
Safetensors
English
llama
moderation
toxicity
content-moderation
safety
quark
multi-label-classification
jigsaw
hate-speech
italian-ai
text-embeddings-inference
Instructions to use ThingAI/Quark-Mod with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ThingAI/Quark-Mod with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="ThingAI/Quark-Mod")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("ThingAI/Quark-Mod") model = AutoModelForSequenceClassification.from_pretrained("ThingAI/Quark-Mod") - Notebooks
- Google Colab
- Kaggle
File size: 1,594 Bytes
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"architectures": [
"LlamaForSequenceClassification"
],
"attention_bias": true,
"attention_dropout": 0.0,
"bos_token_id": 0,
"chat_template": "{% for message in messages %}{% if message['role'] == 'system' %}<|system|>\n{{ message['content'] }}\n{% elif message['role'] == 'user' %}<|user|>\n{{ message['content'] }}\n{% elif message['role'] == 'assistant' %}<|assistant|>\n{{ message['content'] }}{% if not loop.last %}\n{% endif %}{% endif %}{% endfor %}{% if messages[-1]['role'] != 'assistant' %}<|assistant|>\n{% endif %}",
"dtype": "bfloat16",
"eos_token_id": 0,
"head_dim": 64,
"hidden_act": "silu",
"hidden_dropout": 0.0,
"hidden_size": 576,
"id2label": {
"0": "LABEL_0",
"1": "LABEL_1",
"2": "LABEL_2",
"3": "LABEL_3",
"4": "LABEL_4",
"5": "LABEL_5",
"6": "LABEL_6",
"7": "LABEL_7",
"8": "LABEL_8"
},
"initializer_range": 0.02,
"intermediate_size": 1536,
"label2id": {
"LABEL_0": 0,
"LABEL_1": 1,
"LABEL_2": 2,
"LABEL_3": 3,
"LABEL_4": 4,
"LABEL_5": 5,
"LABEL_6": 6,
"LABEL_7": 7,
"LABEL_8": 8
},
"max_position_embeddings": 2048,
"mlp_bias": false,
"model_type": "llama",
"num_attention_heads": 9,
"num_hidden_layers": 30,
"num_key_value_heads": 3,
"pad_token_id": 0,
"pretraining_tp": 1,
"problem_type": "multi_label_classification",
"rms_norm_eps": 1e-05,
"rope_parameters": {
"rope_theta": 10000.0,
"rope_type": "default"
},
"tie_word_embeddings": true,
"transformers_version": "5.6.0",
"use_cache": false,
"vocab_size": 49152
}
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