Translation
Transformers
ONNX
Transformers.js
English
t5
text2text-generation
cron
scheduling
cronlm
Instructions to use pavstev/cronlm with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use pavstev/cronlm 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="pavstev/cronlm")# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("pavstev/cronlm") model = AutoModelForSeq2SeqLM.from_pretrained("pavstev/cronlm") - Transformers.js
How to use pavstev/cronlm with Transformers.js:
// npm i @huggingface/transformers import { pipeline } from '@huggingface/transformers'; // Allocate pipeline const pipe = await pipeline('translation', 'pavstev/cronlm'); - Notebooks
- Google Colab
- Kaggle
File size: 699 Bytes
ace238f | 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 | {
"architectures": [
"T5ForConditionalGeneration"
],
"classifier_dropout": 0.0,
"d_ff": 1024,
"d_kv": 64,
"d_model": 256,
"decoder_start_token_id": 0,
"dense_act_fn": "relu",
"dropout_rate": 0.1,
"dtype": "float32",
"eos_token_id": 1,
"feed_forward_proj": "relu",
"initializer_factor": 1.0,
"is_encoder_decoder": true,
"is_gated_act": false,
"layer_norm_epsilon": 1e-06,
"model_type": "t5",
"n_positions": 512,
"num_decoder_layers": 4,
"num_heads": 4,
"num_layers": 4,
"pad_token_id": 0,
"relative_attention_max_distance": 128,
"relative_attention_num_buckets": 32,
"transformers_version": "4.57.6",
"use_cache": true,
"vocab_size": 32128
}
|