Text Classification
Transformers
Safetensors
Japanese
gemma2
text-generation
guardrail
safety
japanese
Instructions to use shibu-phys/arise-japanese-guardrail-gemma2b-lora with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use shibu-phys/arise-japanese-guardrail-gemma2b-lora with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="shibu-phys/arise-japanese-guardrail-gemma2b-lora")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("shibu-phys/arise-japanese-guardrail-gemma2b-lora") model = AutoModelForCausalLM.from_pretrained("shibu-phys/arise-japanese-guardrail-gemma2b-lora") - Notebooks
- Google Colab
- Kaggle
File size: 1,439 Bytes
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"architectures": [
"Gemma2ForCausalLM"
],
"attention_bias": false,
"attention_dropout": 0.0,
"attn_logit_softcapping": 50.0,
"bos_token_id": 2,
"cache_implementation": "hybrid",
"dtype": "bfloat16",
"eos_token_id": 1,
"final_logit_softcapping": 30.0,
"head_dim": 256,
"hidden_activation": "gelu_pytorch_tanh",
"hidden_size": 2304,
"initializer_range": 0.02,
"intermediate_size": 9216,
"layer_types": [
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"full_attention",
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],
"max_position_embeddings": 8192,
"model_type": "gemma2",
"num_attention_heads": 8,
"num_hidden_layers": 26,
"num_key_value_heads": 4,
"pad_token_id": 0,
"query_pre_attn_scalar": 224,
"rms_norm_eps": 1e-06,
"rope_theta": 10000.0,
"sliding_window": 4096,
"torch_dtype": "bfloat16",
"transformers_version": "4.53.0",
"use_cache": true,
"vocab_size": 256000
}
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