Instructions to use sriramahesh2000/sample2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use sriramahesh2000/sample2 with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("TheBloke/zephyr-7B-beta-GPTQ") model = PeftModel.from_pretrained(base_model, "sriramahesh2000/sample2") - Notebooks
- Google Colab
- Kaggle
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Download README.md from sriramahesh2000/sample2: direct link, hf CLI and curl.
- Browser
- Download file 1.13 kB
-
https://huggingface.co/sriramahesh2000/sample2/resolve/main/README.md
- Command line
-
hf download hf://sriramahesh2000/sample2/README.md
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curl -L -o README.md https://huggingface.co/sriramahesh2000/sample2/resolve/main/README.md
1.13 kB
metadata
license: mit
base_model: TheBloke/zephyr-7B-beta-GPTQ
tags:
- generated_from_trainer
model-index:
- name: sample2
results: []
library_name: peft
sample2
This model is a fine-tuned version of TheBloke/zephyr-7B-beta-GPTQ 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: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- training_steps: 1000
- mixed_precision_training: Native AMP
Training results
Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0