Instructions to use projecte-aina/Plume256k with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use projecte-aina/Plume256k with Transformers:
# Use a pipeline as a high-level helper # Warning: Pipeline type "translation" is no longer supported in transformers v5. # You must load the model directly (see below) or downgrade to v4.x with: # 'pip install "transformers<5.0.0' from transformers import pipeline pipe = pipeline("translation", model="projecte-aina/Plume256k")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("projecte-aina/Plume256k") model = AutoModelForCausalLM.from_pretrained("projecte-aina/Plume256k") - Notebooks
- Google Colab
- Kaggle
File size: 791 Bytes
f775eb8 | 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": "/gpfs/projects/bsc88/mt_translation/Parallel_LLM/training/saved_checkpoints/gemma256_distributed/checkpoint-540000",
"architectures": [
"GemmaForCausalLM"
],
"attention_bias": false,
"attention_dropout": 0.0,
"bos_token_id": 0,
"eos_token_id": 1,
"head_dim": 256,
"hidden_act": "gelu_pytorch_tanh",
"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": 3,
"rms_norm_eps": 1e-06,
"rope_theta": 10000.0,
"torch_dtype": "float32",
"transformers_version": "4.41.2",
"use_cache": true,
"vocab_size": 256000
}
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