Instructions to use SeacowX/Enigma-70B-Entity with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use SeacowX/Enigma-70B-Entity with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("/scratch_tmp/prj/inf_llmcache/hf_cache/models--meta-llama--Llama-3.3-70B-Instruct/snapshots/38ff4e01a70559264c95945aa04b900a11e68422") model = PeftModel.from_pretrained(base_model, "SeacowX/Enigma-70B-Entity") - Notebooks
- Google Colab
- Kaggle
metadata
library_name: peft
license: other
base_model: meta-llama/Llama-3.3-70B-Instruct
tags:
- llama-factory
- lora
- generated_from_trainer
model-index:
- name: openpi_entity
results: []
openpi_entity
This model is a fine-tuned version of /scratch_tmp/prj/inf_llmcache/hf_cache/models--meta-llama--Llama-3.3-70B-Instruct/snapshots/38ff4e01a70559264c95945aa04b900a11e68422 on the openpi_entity_guided 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: 5e-05
- train_batch_size: 32
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 3.0
Training results
Framework versions
- PEFT 0.12.0
- Transformers 4.46.1
- Pytorch 2.5.1+cu124
- Datasets 3.1.0
- Tokenizers 0.20.3