Instructions to use DeepDream2045/d536d2f9-7fe1-46fb-b209-eee34febd19d with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use DeepDream2045/d536d2f9-7fe1-46fb-b209-eee34febd19d with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("unsloth/codegemma-2b") model = PeftModel.from_pretrained(base_model, "DeepDream2045/d536d2f9-7fe1-46fb-b209-eee34febd19d") - Notebooks
- Google Colab
- Kaggle
File size: 757 Bytes
24bcfc7 | 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 | {
"_attn_implementation_autoset": true,
"_name_or_path": "unsloth/codegemma-2b",
"architectures": [
"GemmaForCausalLM"
],
"attention_bias": false,
"attention_dropout": 0.0,
"bos_token_id": 2,
"eos_token_id": 1,
"head_dim": 256,
"hidden_act": "gelu",
"hidden_activation": "gelu_pytorch_tanh",
"hidden_size": 2048,
"initializer_range": 0.02,
"intermediate_size": 16384,
"max_position_embeddings": 8192,
"model_type": "gemma",
"num_attention_heads": 8,
"num_hidden_layers": 18,
"num_key_value_heads": 1,
"pad_token_id": 0,
"rms_norm_eps": 1e-06,
"rope_theta": 10000.0,
"torch_dtype": "bfloat16",
"transformers_version": "4.46.3",
"unsloth_version": "2024.9",
"use_cache": false,
"vocab_size": 256000
}
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