Instructions to use AnalyticsIntelligence/PIDGIN_gemma3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use AnalyticsIntelligence/PIDGIN_gemma3 with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("AnalyticsIntelligence/PIDGIN_gemma3", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Unsloth Desktop
Upload config.json
Browse files- config.json +143 -0
config.json
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{
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"architectures": [
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"Gemma3ForConditionalGeneration"
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],
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"boi_token_index": 255999,
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"bos_token_id": 2,
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"eoi_token_index": 256000,
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"eos_token_id": 106,
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"image_token_index": 262144,
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"initializer_range": 0.02,
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"mm_tokens_per_image": 256,
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"model_type": "gemma3",
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"pad_token_id": 0,
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"quantization_config": {
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"_load_in_4bit": true,
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"_load_in_8bit": false,
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"bnb_4bit_compute_dtype": "bfloat16",
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"bnb_4bit_quant_storage": "uint8",
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"bnb_4bit_quant_type": "nf4",
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"bnb_4bit_use_double_quant": true,
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"llm_int8_enable_fp32_cpu_offload": false,
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"llm_int8_has_fp16_weight": false,
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"llm_int8_skip_modules": [
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"lm_head",
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"multi_modal_projector",
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"merger",
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"modality_projection",
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"language_model.model.layers.1.mlp",
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"language_model.model.layers.4.mlp",
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"language_model.model.layers.2.mlp",
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"language_model.model.layers.5.mlp",
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"vision_tower.vision_model.encoder.layers.22.self_attn",
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"vision_tower.vision_model.encoder.layers.26.self_attn",
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"vision_tower.vision_model.encoder.layers.25.mlp",
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"vision_tower.vision_model.encoder.layers.20.self_attn",
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"language_model.model.layers.2.self_attn",
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"vision_tower.vision_model.encoder.layers.19.mlp",
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"vision_tower.vision_model.encoder.layers.20.mlp",
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"vision_tower.vision_model.encoder.layers.21.mlp",
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"vision_tower.vision_model.encoder.layers.15.mlp",
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"vision_tower.vision_model.encoder.layers.23.mlp",
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"vision_tower.vision_model.encoder.layers.17.mlp",
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"vision_tower.vision_model.encoder.layers.18.mlp",
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"vision_tower.vision_model.encoder.layers.24.mlp",
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"vision_tower.vision_model.encoder.layers.16.self_attn",
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"vision_tower.vision_model.encoder.layers.16.mlp",
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"vision_tower.vision_model.encoder.layers.17.self_attn",
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"vision_tower.vision_model.encoder.layers.13.mlp",
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"vision_tower.vision_model.encoder.layers.14.mlp",
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"vision_tower.vision_model.encoder.layers.15.self_attn",
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"vision_tower.vision_model.encoder.layers.12.mlp",
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"vision_tower.vision_model.encoder.layers.14.self_attn",
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"vision_tower.vision_model.encoder.layers.9.mlp",
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"vision_tower.vision_model.encoder.layers.5.mlp",
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"vision_tower.vision_model.encoder.layers.10.self_attn",
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"vision_tower.vision_model.encoder.layers.7.mlp",
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"vision_tower.vision_model.encoder.layers.9.self_attn",
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"vision_tower.vision_model.encoder.layers.8.mlp",
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"vision_tower.vision_model.encoder.layers.10.mlp",
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"vision_tower.vision_model.encoder.layers.13.self_attn",
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"vision_tower.vision_model.encoder.layers.11.mlp",
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"vision_tower.vision_model.encoder.layers.11.self_attn",
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"vision_tower.vision_model.encoder.layers.12.self_attn",
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"vision_tower.vision_model.encoder.layers.3.mlp",
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"vision_tower.vision_model.encoder.layers.8.self_attn",
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"vision_tower.vision_model.encoder.layers.4.self_attn",
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"vision_tower.vision_model.encoder.layers.6.mlp",
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"vision_tower.vision_model.encoder.layers.7.self_attn",
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"vision_tower.vision_model.encoder.layers.1.self_attn",
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"vision_tower.vision_model.encoder.layers.1.mlp",
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"vision_tower.vision_model.encoder.layers.3.self_attn",
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"vision_tower.vision_model.encoder.layers.0.self_attn",
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"vision_tower.vision_model.encoder.layers.0.mlp",
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"vision_tower.vision_model.encoder.layers.2.self_attn",
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"vision_tower.vision_model.encoder.layers.26.mlp"
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],
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"llm_int8_threshold": 6.0,
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"load_in_4bit": true,
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"load_in_8bit": false,
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"quant_method": "bitsandbytes"
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},
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"text_config": {
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"attention_bias": false,
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"attention_dropout": 0.0,
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"attn_logit_softcapping": null,
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"cache_implementation": "hybrid",
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"final_logit_softcapping": null,
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"head_dim": 256,
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"hidden_activation": "gelu_pytorch_tanh",
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"max_position_embeddings": 131072,
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"model_type": "gemma3_text",
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"num_attention_heads": 8,
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"num_hidden_layers": 34,
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"num_key_value_heads": 4,
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"rope_type": "linear"
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},
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},
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"torch_dtype": "bfloat16",
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"transformers_version": "4.51.3",
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"unsloth_fixed": true,
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"vision_config": {
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"attention_dropout": 0.0,
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"hidden_act": "gelu_pytorch_tanh",
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"layer_norm_eps": 1e-06,
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"model_type": "siglip_vision_model",
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"num_hidden_layers": 27,
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"patch_size": 14,
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"torch_dtype": "bfloat16",
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"vision_use_head": false
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}
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}
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