Instructions to use kineticseas/llama7b-python-finetuned with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use kineticseas/llama7b-python-finetuned with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("TinyPixel/Llama-2-7B-bf16-sharded") model = PeftModel.from_pretrained(base_model, "kineticseas/llama7b-python-finetuned") - Notebooks
- Google Colab
- Kaggle
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Download README.md from kineticseas/llama7b-python-finetuned: direct link, hf CLI and curl.
- Browser
- Download file 1.24 kB
-
https://huggingface.co/kineticseas/llama7b-python-finetuned/resolve/main/README.md
- Command line
-
hf download hf://kineticseas/llama7b-python-finetuned/README.md
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curl -L -o README.md https://huggingface.co/kineticseas/llama7b-python-finetuned/resolve/main/README.md
1.24 kB
metadata
library_name: peft
tags:
- trl
- sft
- generated_from_trainer
datasets:
- generator
base_model: TinyPixel/Llama-2-7B-bf16-sharded
model-index:
- name: train-test
results: []
train-test
This model is a fine-tuned version of TinyPixel/Llama-2-7B-bf16-sharded 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: 0.0003
- train_batch_size: 6
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 24
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 1000
- num_epochs: 3
Training results
Framework versions
- PEFT 0.8.2
- Transformers 4.37.2
- Pytorch 2.2.0+cu121
- Datasets 2.17.0
- Tokenizers 0.15.1