---
model_creator: Nekochu
quantized_by: Nekochu
model_name: Llama-2 13B German ORPO
pretty_name: Llama-2 13B German ORPO
model_type: llama2
prompt_template: >-
  Below is an instruction that describes a task. Write a response that
  appropriately completes the request. ### Instruction: {Instruction} {summary} ### input: {category} ### Response: {prompt}
base_model: meta-llama/Llama-2-13b-chat-hf
library_name: peft
license: apache-2.0
datasets:
- mayflowergmbh/intel_orca_dpo_pairs_de
- LeoLM/OpenSchnabeltier
- LeoLM/German_Songs
- LeoLM/German_Poems
- bjoernp/ultrachat_de
- mayflowergmbh/ultra-chat_de
- mayflowergmbh/airoboros-3.0_de
- mayflowergmbh/booksum_de
- mayflowergmbh/dolphin_de
- mayflowergmbh/evol-instruct_de
- mayflowergmbh/openschnabeltier_de
- mayflowergmbh/alpaca-gpt4_de
- mayflowergmbh/dolly-15k_de
- mayflowergmbh/oasst_de
language:
- de
- en
pipeline_tag: text-generation
task_categories:
- question-answering
- text2text-generation
- conversational
inference: True
tags:
- llama-factory
- lora
- generated_from_trainer
- llama2
- llama
- instruct
- finetune
- llm
- pytorch
- llama
- llama-2
- german
- deutsch
model-index:
- name: Llama-2-13B-German-ORPO
  results: []
---

llama2 Chat LoRa sft train Stage A on German dataset: 
```text
German_Songs,German_Poems,bjoernp_ultrachat_de,OpenSchnabeltier,ultrachat_de,oasst_de,dolly_15k_de,alpaca-gpt4_de,openschnabeltier_de,evol_instruct_de,dolphin_de,booksum_de,airoboros_de & eval VAGOsolutions/MT-Bench-TrueGerman?
```

Stage B: Resume LoRa training using ORPO and dataset `mayflowergmbh/intel_orca_dpo_pairs_de`

Oh and I am not GER speaker ^^

### Training hyperparameters

```cmd
python src/train_bash.py --stage sft ... --finetuning_type lora --quantization_bit 4 --template alpaca --rope_scaling linear --flash_attn True --dataset_dir data --dataset German_Songs,German_Poems,bjoernp_ultrachat_de,OpenSchnabeltier,ultrachat_de,oasst_de,dolly_15k_de,alpaca-gpt4_de,openschnabeltier_de,evol_instruct_de,dolphin_de,booksum_de,airoboros_de --cutoff_len 4096 --learning_rate 5e-05 --num_train_epochs 1.0 --max_samples 100000 --per_device_train_batch_size 1 --gradient_accumulation_steps 1 --lr_scheduler_type cosine --max_grad_norm 1.0 --logging_steps 5 --save_steps 1000 --warmup_steps 0 --neftune_noise_alpha 0.5 --optim adamw_torch --upcast_layernorm True --use_llama_pro True --bf16 True --lora_rank 512 --lora_alpha 1024 --lora_dropout 0.15 --lora_target all --use_rslora True --additional_target all --create_new_adapter True --plot_loss True

python src/train_bash.py --stage orpo ... --finetuning_type lora --quantization_bit 4 --template alpaca --rope_scaling linear --flash_attn True --dataset_dir data --dataset orca_dpo_de --cutoff_len 4096 --learning_rate 1e-05 --num_train_epochs 1.0 --max_samples 100000 --per_device_train_batch_size 1 --gradient_accumulation_steps 1 --lr_scheduler_type cosine --max_grad_norm 0.9 --logging_steps 5 --save_steps 250 --warmup_steps 100 --neftune_noise_alpha 0.5 --optim adamw_torch --upcast_layernorm True --use_llama_pro True --report_to none --bf16 True --lora_rank 512 --lora_alpha 1024 --lora_dropout 0.15 --use_rslora True --lora_target all --additional_target all --orpo_beta 0.1 --plot_loss True
```
The following hyperparameters were used during training:
- learning_rate: 1e-05 # not Defaut LR as for high rank 512, alpha 1024 
- train_batch_size: 1
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 1000
- num_epochs: 1.0
### Framework versions
- PEFT 0.10.0
- Transformers 4.39.1
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2