Instructions to use PierreMaxime/llama3fine with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use PierreMaxime/llama3fine with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("unsloth/llama-3-8b-bnb-4bit") model = PeftModel.from_pretrained(base_model, "PierreMaxime/llama3fine") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Unsloth Desktop
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Download README.md from PierreMaxime/llama3fine: direct link, hf CLI and curl.
- Browser
- Download file 1.26 kB
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https://huggingface.co/PierreMaxime/llama3fine/resolve/main/README.md
- Command line
-
hf download hf://PierreMaxime/llama3fine/README.md
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curl -L -o README.md https://huggingface.co/PierreMaxime/llama3fine/resolve/main/README.md
1.26 kB
metadata
license: llama2
library_name: peft
tags:
- trl
- sft
- unsloth
- generated_from_trainer
base_model: unsloth/llama-3-8b-bnb-4bit
model-index:
- name: llama3fine
results: []
llama3fine
This model is a fine-tuned version of unsloth/llama-3-8b-bnb-4bit on the None 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: 0.0002
- train_batch_size: 2
- eval_batch_size: 8
- seed: 3407
- gradient_accumulation_steps: 4
- total_train_batch_size: 8
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 5
- training_steps: 60
- mixed_precision_training: Native AMP
Training results
Framework versions
- PEFT 0.11.1
- Transformers 4.41.1
- Pytorch 2.2.2+cu121
- Datasets 2.16.0
- Tokenizers 0.19.1