metadata

license: apache-2.0
datasets:
  - abacusai/SystemChat-1.1
language:
  - en
library_name: transformers
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 SystemChat-1.1 from Abacus.AI.

Quants

Thank you mradermacher!

mradermacher/danube2-1.8b-SystemChat-1.1-GGUF

Template

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

BAdam

### model
model_name_or_path: danube2-base-chatml

### method
stage: sft
do_train: true
finetuning_type: full
use_badam: true
badam_switch_mode: descending
badam_switch_interval: 50
badam_start_block: 22
badam_mask_mode: scatter
badam_verbose: 1
seed: 314

### dataset
dataset: systemchat11
template: hermes_chatml
cutoff_len: 8192
overwrite_cache: false
preprocessing_num_workers: 12

### output
output_dir: systemchat11-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: 0.00002
num_train_epochs: 3
lr_scheduler_type: cosine
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: 1000

BAdam Training results

Training Loss	Epoch	Step	Validation Loss
1.0062	0.8324	1000	0.9837
0.8484	1.6648	2000	0.9388
0.7834	2.4971	3000	0.9309

QLoRA+

### model
model_name_or_path: systemchat11-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: 31415

### dataset
dataset: systemchat11
template: hermes_chatml
cutoff_len: 8192
overwrite_cache: false
preprocessing_num_workers: 12

### output
output_dir: systemchat11-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: 2.0
lr_scheduler_type: cosine
warmup_ratio: 0.01
bf16: true
flash_attn: fa2

### eval
val_size: 0.02
per_device_eval_batch_size: 1
eval_strategy: steps
eval_steps: 500

QLoRA+ Training results

Training Loss	Epoch	Step	Validation Loss
0.8591	0.4204	500	0.8457
0.9098	0.8409	1000	0.8251
0.735	1.2613	1500	0.8304
0.6811	1.6817	2000	0.8252