h2o-danube2 with ChatML template

This model was first fine-tuned with BAdam on cognitivecomputations/WizardLM_evol_instruct_V2_196k_unfiltered_merged_split using LLama-Factory.

Quants

Thanks to mradermacher!

• mradermacher/danube2-1.8b-WizardLM-Evol-V2-Unfiltered-GGUF

Template

<|im_start|>system
You are a helpful assistant that gives long and detailed answers.<|im_end|>
<|im_start|>user
{{instruction}}<|im_end|>
<|im_start|>assistant
{{response}}<|im_end|>

BAdam config

### 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: 6
seed: 720

### dataset
dataset: wizardlm_evol_v2_196k_unfiltered
template: ninja_chatml
cutoff_len: 8192
overwrite_cache: false
preprocessing_num_workers: 12

### output
output_dir: wizardlm-evol-v2-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
pure_bf16: true
flash_attn: fa2

### eval
val_size: 0.01
per_device_eval_batch_size: 1
eval_strategy: steps
eval_steps: 1000

BAdam training results