Instructions to use kmeanskaran/gemma-code-instruct-finetune-test with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use kmeanskaran/gemma-code-instruct-finetune-test with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("question-answering", model="kmeanskaran/gemma-code-instruct-finetune-test")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("kmeanskaran/gemma-code-instruct-finetune-test") model = AutoModelForCausalLM.from_pretrained("kmeanskaran/gemma-code-instruct-finetune-test") - Notebooks
- Google Colab
- Kaggle
File size: 662 Bytes
2758e65 | 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 | {
"_name_or_path": "google/gemma-2b-it",
"architectures": [
"GemmaForCausalLM"
],
"attention_bias": false,
"attention_dropout": 0.0,
"bos_token_id": 2,
"eos_token_id": 1,
"head_dim": 256,
"hidden_act": "gelu",
"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_scaling": null,
"rope_theta": 10000.0,
"torch_dtype": "float16",
"transformers_version": "4.38.0",
"use_cache": true,
"vocab_size": 256000
}
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