Instructions to use ShushantLLM/LLama_missing_music_generator with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ShushantLLM/LLama_missing_music_generator with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("ShushantLLM/LLama_missing_music_generator", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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Download README.md from ShushantLLM/LLama_missing_music_generator: direct link, hf CLI and curl.
- Browser
- Download file 1.34 kB
-
https://huggingface.co/ShushantLLM/LLama_missing_music_generator/resolve/main/README.md
- Command line
-
hf download hf://ShushantLLM/LLama_missing_music_generator/README.md
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curl -L -o README.md https://huggingface.co/ShushantLLM/LLama_missing_music_generator/resolve/main/README.md
1.34 kB
metadata
library_name: transformers
license: llama2
base_model: meta-llama/Llama-2-7b-hf
tags:
- trl
- sft
- LyricGeneration Again
- generated_from_trainer
datasets:
- generator
model-index:
- name: LLama_missing_music_generator
results: []
LLama_missing_music_generator
This model is a fine-tuned version of meta-llama/Llama-2-7b-hf on the generator dataset.
Model description
More information needed
Intended uses & limitations
More information needed
Training and evaluation data
More information needed
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 4
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: constant_with_warmup
- lr_scheduler_warmup_ratio: 0.04
- num_epochs: 2
- mixed_precision_training: Native AMP
Training results
Framework versions
- Transformers 4.44.2
- Pytorch 2.4.0+cu121
- Datasets 3.0.0
- Tokenizers 0.19.1