peft safetensors mistral alignment-handbook generated_from_trainer trl sft dataset:jan-hq/rag_dataset_1200_binarized dataset:jan-hq/rag_dataset_12000_binarized dataset:jan-hq/rag_hallucination_dataset_1000_binarized dataset:jan-hq/rag_full_20000_binarized dataset:jan-hq/bagel_sft_binarized dataset:jan-hq/dolphin_binarized dataset:jan-hq/openhermes_binarized base_model:jan-hq/stealth-v1.3 license:apache-2.0 4-bit bitsandbytes region:us
---
license: apache-2.0
library_name: peft
tags:
- alignment-handbook
- generated_from_trainer
- trl
- sft
- generated_from_trainer
datasets:
- jan-hq/rag_dataset_1200_binarized
- jan-hq/rag_dataset_12000_binarized
- jan-hq/rag_hallucination_dataset_1000_binarized
- jan-hq/rag_full_20000_binarized
- jan-hq/bagel_sft_binarized
- jan-hq/dolphin_binarized
- jan-hq/openhermes_binarized
base_model: jan-hq/stealth-v1.3
model-index:
- name: stealth-rag-v1
  results: []
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# stealth-rag-v1

This model is a fine-tuned version of [jan-hq/stealth-v1.3](https://huggingface.co/jan-hq/stealth-v1.3) on the jan-hq/rag_dataset_1200_binarized, the jan-hq/rag_dataset_12000_binarized, the jan-hq/rag_hallucination_dataset_1000_binarized, the jan-hq/rag_full_20000_binarized, the jan-hq/bagel_sft_binarized, the jan-hq/dolphin_binarized and the jan-hq/openhermes_binarized datasets.
It achieves the following results on the evaluation set:
- Loss: 1.3883

## 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: 2
- eval_batch_size: 4
- seed: 42
- distributed_type: multi-GPU
- num_devices: 2
- gradient_accumulation_steps: 16
- total_train_batch_size: 64
- total_eval_batch_size: 8
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 3

### Training results



### Framework versions

- PEFT 0.7.1
- Transformers 4.36.2
- Pytorch 2.1.2+cu121
- Datasets 2.14.6
- Tokenizers 0.15.0