Text Classification
Transformers
Safetensors
qwen3
content-safety
prompt-injection
multi-label-classification
safety
multilingual
hikmaai
Instructions to use HikmaAI/HikmaGuard with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use HikmaAI/HikmaGuard with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="HikmaAI/HikmaGuard")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("HikmaAI/HikmaGuard") model = AutoModelForSequenceClassification.from_pretrained("HikmaAI/HikmaGuard", device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 2,156 Bytes
6604218 | 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 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 | {
"architectures": [
"Qwen3ForSequenceClassification"
],
"attention_bias": false,
"attention_dropout": 0.0,
"bos_token_id": 151643,
"dtype": "bfloat16",
"eos_token_id": 151645,
"head_dim": 128,
"hidden_act": "silu",
"hidden_size": 2560,
"hikma_artifact_id": "d21042f88a814cc19011421f0cf972be",
"id2label": {
"0": "hate",
"1": "abuse",
"2": "violence",
"3": "self_harm",
"4": "sexual_harm",
"5": "misconduct",
"6": "dangerous_capability",
"7": "prompt_injection",
"8": "indirect_prompt_injection"
},
"initializer_range": 0.02,
"intermediate_size": 9728,
"label2id": {
"abuse": 1,
"dangerous_capability": 6,
"hate": 0,
"indirect_prompt_injection": 8,
"misconduct": 5,
"prompt_injection": 7,
"self_harm": 3,
"sexual_harm": 4,
"violence": 2
},
"layer_types": [
"full_attention",
"full_attention",
"full_attention",
"full_attention",
"full_attention",
"full_attention",
"full_attention",
"full_attention",
"full_attention",
"full_attention",
"full_attention",
"full_attention",
"full_attention",
"full_attention",
"full_attention",
"full_attention",
"full_attention",
"full_attention",
"full_attention",
"full_attention",
"full_attention",
"full_attention",
"full_attention",
"full_attention",
"full_attention",
"full_attention",
"full_attention",
"full_attention",
"full_attention",
"full_attention",
"full_attention",
"full_attention",
"full_attention",
"full_attention",
"full_attention",
"full_attention"
],
"max_position_embeddings": 32768,
"max_window_layers": 36,
"model_type": "qwen3",
"num_attention_heads": 32,
"num_hidden_layers": 36,
"num_key_value_heads": 8,
"pad_token_id": 151643,
"problem_type": "multi_label_classification",
"rms_norm_eps": 1e-06,
"rope_scaling": null,
"rope_theta": 1000000,
"sliding_window": null,
"tie_word_embeddings": true,
"transformers_version": "4.57.6",
"use_cache": false,
"use_sliding_window": false,
"vocab_size": 151936
}
|