metadata

license: other
tags:
  - generated_from_trainer
  - google/gemma
  - PyTorch
  - transformers
  - trl
  - peft
  - tensorboard
model-index:
  - name: pygemma-2b-ultra
    results: []
datasets:
  - Vezora/Tested-143k-Python-Alpaca
language:
  - en
license_name: gemma-terms-of-use
license_link: https://ai.google.dev/gemma/terms
base_model: google/gemma-2b
widget:
  - example_title: Compute Sum
    messages:
      - role: system
        content: >
          Welcome to PyGemma, your AI-powered Python assistant. I'm here to help
          you answer common questions about the Python programming language.
          Let's dive into Python!

          Please note: - I strive to provide accurate and reliable information,
          but I'm not perfect. If you notice any inaccuracies, please let me
          know. - I'm committed to maintaining a safe and respectful
          environment. Please avoid using toxic or harmful language. - I'm
          continuously learning and improving. If you notice any inconsistencies
          in my responses, I appreciate your patience and feedback.
      - role: user
        content: Create a function to calculate the sum of a sequence of integers.
inference:
  parameters:
    temperature: 0.1
    max_new_tokens: 1024
    top_p: 0.95
    top_k: 50
pipeline_tag: text-generation

Model Card for pygemma-2b-ultra:

🐍💬🤖

pygemma-2b-ultra is a language model that is trained to act as Python assistant. It is a finetuned version of google/gemma-2b that was trained using SFTTrainer on publicly available dataset Vezora/Tested-143k-Python-Alpaca.

Training Metrics

The training metrics can be found on TensorBoard.

Training hyperparameters

The following hyperparameters were used during the training:

output_dir: peft-lora-model
overwrite_output_dir: True
do_train: False
do_eval: False
do_predict: False
evaluation_strategy: no
prediction_loss_only: False
per_device_train_batch_size: 2
per_device_eval_batch_size: None
per_gpu_train_batch_size: None
per_gpu_eval_batch_size: None
gradient_accumulation_steps: 4
eval_accumulation_steps: None
eval_delay: 0
learning_rate: 2e-05
weight_decay: 0.0
adam_beta1: 0.9
adam_beta2: 0.999
adam_epsilon: 1e-08
max_grad_norm: 0.3
num_train_epochs: 1
max_steps: -1
lr_scheduler_type: cosine
lr_scheduler_kwargs: {}
warmup_ratio: 0.1
warmup_steps: 0
log_level: passive
log_level_replica: warning
log_on_each_node: True
logging_dir: peft-lora-model/runs/Mar22_07-15-06_2013f91d074a
logging_strategy: steps
logging_first_step: False
logging_steps: 10
logging_nan_inf_filter: True
save_strategy: epoch
save_steps: 500
save_total_limit: None
save_safetensors: True
save_on_each_node: False
save_only_model: False
no_cuda: False
use_cpu: False
use_mps_device: False
seed: 42
data_seed: None
jit_mode_eval: False
use_ipex: False
bf16: True
fp16: False
fp16_opt_level: O1
half_precision_backend: auto
bf16_full_eval: False
fp16_full_eval: False
tf32: None
local_rank: 0
ddp_backend: None
tpu_num_cores: None
tpu_metrics_debug: False
debug: []
dataloader_drop_last: False
eval_steps: None
dataloader_num_workers: 0
dataloader_prefetch_factor: None
past_index: -1
run_name: peft-lora-model
disable_tqdm: False
remove_unused_columns: True
label_names: None
load_best_model_at_end: False
metric_for_best_model: None
greater_is_better: None
ignore_data_skip: False
fsdp: []
fsdp_min_num_params: 0
fsdp_config: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
fsdp_transformer_layer_cls_to_wrap: None
accelerator_config: AcceleratorConfig(split_batches=False, dispatch_batches=None, even_batches=True, use_seedable_sampler=True)
deepspeed: None
label_smoothing_factor: 0.0
optim: adamw_torch_fused
optim_args: None
adafactor: False
group_by_length: False
length_column_name: length
report_to: ['tensorboard']
ddp_find_unused_parameters: None
ddp_bucket_cap_mb: None
ddp_broadcast_buffers: None
dataloader_pin_memory: True
dataloader_persistent_workers: False
skip_memory_metrics: True
use_legacy_prediction_loop: False
push_to_hub: False
resume_from_checkpoint: None
hub_model_id: None
hub_strategy: every_save
hub_token: None
hub_private_repo: False
hub_always_push: False
gradient_checkpointing: True
gradient_checkpointing_kwargs: {'use_reentrant': False}
include_inputs_for_metrics: False
fp16_backend: auto
push_to_hub_model_id: None
push_to_hub_organization: None
push_to_hub_token: None
mp_parameters:
auto_find_batch_size: False
full_determinism: False
torchdynamo: None
ray_scope: last
ddp_timeout: 1800
torch_compile: False
torch_compile_backend: None
torch_compile_mode: None
dispatch_batches: None
split_batches: None
include_tokens_per_second: False
include_num_input_tokens_seen: False
neftune_noise_alpha: None
distributed_state: Distributed environment: NO Num processes: 1 Process index: 0 Local process index: 0 Device: cuda
_n_gpu: 1
__cached__setup_devices: cuda:0
deepspeed_plugin: None