Instructions to use Weni/WeniGPT-2.1.1-zephyr-7b-beta-BitsandBytes-LLM-Base-1.0.1-6k_evol_complexity with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use Weni/WeniGPT-2.1.1-zephyr-7b-beta-BitsandBytes-LLM-Base-1.0.1-6k_evol_complexity with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("HuggingFaceH4/zephyr-7b-beta") model = PeftModel.from_pretrained(base_model, "Weni/WeniGPT-2.1.1-zephyr-7b-beta-BitsandBytes-LLM-Base-1.0.1-6k_evol_complexity") - Notebooks
- Google Colab
- Kaggle
WeniGPT-2.1.1-zephyr-7b-beta-BitsandBytes-LLM-Base-1.0.1-6k_evol_complexity
This model is a fine-tuned version of HuggingFaceH4/zephyr-7b-beta on the generator dataset. It achieves the following results on the evaluation set:
- Loss: 0.4654
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: 16
- eval_batch_size: 1
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.03
- training_steps: 42
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| No log | 0.99 | 34 | 0.4671 |
| No log | 1.22 | 42 | 0.4654 |
Framework versions
- PEFT 0.7.1
- Transformers 4.37.0.dev0
- Pytorch 2.1.0+cu118
- Datasets 2.16.1
- Tokenizers 0.15.0
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Model tree for Weni/WeniGPT-2.1.1-zephyr-7b-beta-BitsandBytes-LLM-Base-1.0.1-6k_evol_complexity
Base model
mistralai/Mistral-7B-v0.1 Finetuned
HuggingFaceH4/zephyr-7b-beta