Pygemma-2B Ultra Plus-4 Open Source Python Programming Assistant - Free Code Generation and Problem Solving

Pygemma 2b Ultra Plus 4

Developed by Menouar

A Python programming assistant model fine-tuned based on google/gemma-2b, specializing in Python code generation and problem-solving

Large Language Model

Transformers

EnglishOpen Source License:Other #Python Programming Assistant #Gemma Fine-tuning #Code Generation

Downloads 15

Release Time : 3/23/2024

Model Overview

This is a language model specifically designed for Python programming, fine-tuned on the Gemma-2b architecture, capable of helping developers solve Python-related issues and generate code snippets

Model Features

Python-specific Optimization

Fine-tuned specifically for the Python programming language, enabling better understanding and generation of Python code

Lightweight Model

Based on the 2-billion-parameter Gemma-2b architecture, maintaining performance while reducing resource requirements

LoRA Fine-tuning

Trained using parameter-efficient fine-tuning (LoRA), preserving most of the base model's capabilities

Model Capabilities

Python code generation

Programming problem-solving

Code explanation

Algorithm implementation

Use Cases

Programming Assistance

Code Snippet Generation

Generate Python code snippets based on natural language descriptions

Can generate common code structures like functions and classes

Programming Problem Solving

Answer Python-related programming questions

Provide accurate explanations and solutions

Education

Programming Learning Assistance

Help beginners learn Python programming

Provide example codes and explanations

🚀 pygemma-2b-ultra-plus-4

pygemma-2b-ultra-plus-4 is a language model designed to serve as a Python assistant. It's a finetuned version of google/gemma-2b, trained using SFTTrainer on the publicly available dataset Vezora/Tested-143k-Python-Alpaca.

🚀 Quick Start

This model is ready to assist with Python-related queries out of the box. You can interact with it through the provided widget example.

✨ Features

Python Assistance: Specifically trained to answer common questions about the Python programming language.
Finetuned Model: Based on google/gemma-2b, offering enhanced performance for Python tasks.

📚 Documentation

Training Metrics

The training metrics can be found on TensorBoard.

Training Hyperparameters

The following hyperparameters were used during the training:

Parameter	Value
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/Mar23_06 - 23 - 59_676c0e3f20e7
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

📄 License

This model is released under the gemma - terms - of - use.

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