Instructions to use Raneechu/mininglarge2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use Raneechu/mininglarge2 with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("meta-llama/Llama-2-7b-hf") model = PeftModel.from_pretrained(base_model, "Raneechu/mininglarge2") - Notebooks
- Google Colab
- Kaggle
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Download README.md from Raneechu/mininglarge2: direct link, hf CLI and curl.
- Browser
- Download file 1.16 kB
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https://huggingface.co/Raneechu/mininglarge2/resolve/main/README.md
- Command line
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hf download hf://Raneechu/mininglarge2/README.md
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curl -L -o README.md https://huggingface.co/Raneechu/mininglarge2/resolve/main/README.md
1.16 kB
metadata
license: llama2
library_name: peft
tags:
- trl
- sft
- generated_from_trainer
base_model: meta-llama/Llama-2-7b-hf
model-index:
- name: mininglarge2
results: []
mininglarge2
This model is a fine-tuned version of meta-llama/Llama-2-7b-hf on an unknown 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: 2e-05
- train_batch_size: 4
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- training_steps: 1
Training results
Framework versions
- Transformers 4.40.1
- Pytorch 2.1.1+cu121
- Datasets 2.14.5
- Tokenizers 0.19.1
Training procedure
Framework versions
- PEFT 0.6.2