danube2-1.8b-glaive-function-calling-v2

h2o-danube2 with ChatML template

This is a BAdam fine-tuned danube2 base model. It uses the ChatML template and was trained on the glaive-function-calling-v2 dataset from GlaiveAI that has been converted to ShareGPT by hiyouga of LLama-Factory fame.

Template

ChatML

<|im_start|>system
{{system}}
<tools>
{{json_format_tools}}
</tools><|im_end|>
<|im_start|>user
{{instruction}}<|im_end|>
<|im_start|>assistant
<tool_call>
{{tool_call}}
</tool_call><|im_end|>
<|im_start|>tool
<tool_response>
{{response}}
</tool_response><|im_end|>

LLama-Factory

_register_template(
    name="hermes_chatml",
    format_user=StringFormatter(slots=["<|im_start|>user\n{{content}}<|im_end|>\n<|im_start|>assistant\n"]),
    format_assistant=StringFormatter(slots=["{{content}}<|im_end|>\n"]),
    format_system=StringFormatter(slots=["<|im_start|>system\n{{content}}<|im_end|>\n"]),
    format_function=FunctionFormatter(slots=["<tool_call>\n{\"name\":\"{{name}}\", \"arguments\":{{arguments}}}\n</tool_call><|im_end|>\n"]),
    format_observation=StringFormatter(slots=["<|im_start|>tool\n<tool_response>\n{{content}}\n</tool_response><|im_end|>\n<|im_start|>assistant\n"]),
    format_tools=ToolFormatter(tool_format="chatml"),
	stop_words=["<|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: 5
seed: 404

### dataset
dataset: glaive_toolcall_100k
template: hermes_chatml
cutoff_len: 8192
overwrite_cache: false
preprocessing_num_workers: 12

### output
output_dir: glaive-tool-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: 0.000005
num_train_epochs: 1
lr_scheduler_type: cosine
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