Translation
Transformers
Safetensors
Korean
English
Vietnamese
llama
text-generation
text-generation-inference
Instructions to use DMTLabs-AI/DMTLLM-Translation-Research with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use DMTLabs-AI/DMTLLM-Translation-Research 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="DMTLabs-AI/DMTLLM-Translation-Research")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("DMTLabs-AI/DMTLLM-Translation-Research") model = AutoModelForCausalLM.from_pretrained("DMTLabs-AI/DMTLLM-Translation-Research", device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 780 Bytes
2e7d3d7 | 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 | {
"name": "dmt-multilingual-50m-translation-sft",
"base_model_dir": "outputs/dmt-multilingual-50m/final",
"data_dir": "data/sft_tokenized",
"tokenizer_path": "tokenizer/tokenizer.json",
"output_dir": "outputs/dmt-multilingual-50m-translation-sft",
"training": {
"micro_batch_size": 64,
"eval_micro_batch_size": 64,
"gradient_accumulation_steps": 2,
"epochs": 1,
"max_steps": 0,
"learning_rate": 5e-05,
"min_learning_rate": 5e-06,
"weight_decay": 0.01,
"warmup_ratio": 0.03,
"gradient_clip": 1.0,
"num_workers": 8,
"pad_to_multiple_of": 8,
"log_interval": 20,
"eval_interval": 500,
"eval_batches": 100,
"save_interval": 2000,
"seed": 42,
"compile": false,
"gradient_checkpointing": false
}
} |