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danube2-1.8b-openhermes

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metadata
license: apache-2.0
datasets:
  - Crystalcareai/openhermes_200k_unfiltered
language:
  - en
library_name: transformers
base_model: h2oai/h2o-danube2-1.8b-base
tags:
  - llama-factory
  - unsloth

h2o-danube2 with ChatML template

This is a BAdam and LoRA+ fine-tuned danube2 base model. It uses the ChatML template and was trained on the openhermes-unfiltered.

Quants

Thank you mradermacher!

Template 

<|im_start|>user
{{instruction}}<|im_end|>
<|im_start|>assistant
{{response}}<|im_end|>

BAdam

System: You are a helpful assistant.

### model
model_name_or_path: danube2-base-chatml

### method
stage: sft
do_train: true
finetuning_type: full
use_badam: true
badam_switch_mode: ascending
badam_switch_interval: 50
badam_verbose: 1
badam_start_block: 10
seed: 720

### dataset
dataset: openhermes_unfiltered
template: ninja_chatml
cutoff_len: 8192
overwrite_cache: false
preprocessing_num_workers: 12

### output
output_dir: openhermes-chatml-badam
logging_steps: 5
save_steps: 1
save_strategy: epoch
plot_loss: true
overwrite_output_dir: false

### train
per_device_train_batch_size: 2
gradient_accumulation_steps: 8
learning_rate: 0.00001
num_train_epochs: 1
lr_scheduler_type: constant_with_warmup
warmup_ratio: 0.01
bf16: true
flash_attn: fa2

### eval
val_size: 0.01
per_device_eval_batch_size: 1
eval_strategy: steps
eval_steps: 2000

BAdam Training results

Training Loss Epoch Step Validation Loss
0.7971 0.1748 2000 0.7418
0.6815 0.3496 4000 0.7178
0.6593 0.5245 6000 0.7055
0.6923 0.6993 8000 0.6960
0.6942 0.8741 10000 0.6877

QLoRA+ 

### model
model_name_or_path: openhermes-chatml-badam

### method
stage: sft
do_train: true
finetuning_type: lora
lora_target: all
loraplus_lr_ratio: 16.0
lora_rank: 8
lora_alpha: 16
use_unsloth: true
quantization_bit: 4
upcast_layernorm: true
seed: 3141

### dataset
dataset: openhermes_unfiltered
template: hermes_chatml
cutoff_len: 8192
overwrite_cache: false
preprocessing_num_workers: 12

### output
output_dir: openhermes-chatml-badam/loraplus
logging_steps: 1
save_steps: 1
save_strategy: epoch
plot_loss: true
overwrite_output_dir: false

### train
per_device_train_batch_size: 4
gradient_accumulation_steps: 4
learning_rate: 0.0001
num_train_epochs: 1.0
lr_scheduler_type: cosine
warmup_ratio: 0.01
bf16: true
flash_attn: fa2
#neftune_noise_alpha: 5

### eval
val_size: 0.02
per_device_eval_batch_size: 1
eval_strategy: steps
eval_steps: 1000

QLoRA+ Training results

Training Loss Epoch Step Validation Loss
0.6523 0.0883 1000 0.7126
0.6398 0.1766 2000 0.7086
0.6865 0.2649 3000 0.7001
0.6714 0.3532 4000 0.6917
0.7213 0.4415 5000 0.6819
0.7764 0.5298 6000 0.6721
0.6931 0.6181 7000 0.6638
0.6632 0.7064 8000 0.6560
0.5966 0.7947 9000 0.6514
0.6339 0.8830 10000 0.6482
0.4987 0.9713 11000 0.6472