adapter: lora
base_model: Qwen/Qwen2.5-7B-Instruct
bf16: auto
datasets:
- path: P4n1c0/codi
  type: chat_template
  field_messages: messages
gradient_accumulation_steps: 8
learning_rate: 0.0002
load_in_8bit: true
lora_alpha: 16
lora_dropout: 0.05
lora_r: 8
lora_target_modules:
- q_proj
- v_proj
- k_proj
- o_proj
- gate_proj
- down_proj
- up_proj
micro_batch_size: 1
num_epochs: 1
optimizer: adamw_bnb_8bit
output_dir: ./outputs/mymodel
sequence_len: 2048
train_on_inputs: false

outputs/mymodel

This model is a fine-tuned version of Qwen/Qwen2.5-7B-Instruct on the P4n1c0/codi dataset.

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 0.0002
train_batch_size: 1
eval_batch_size: 1
seed: 42
gradient_accumulation_steps: 8
total_train_batch_size: 8
optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
lr_scheduler_type: cosine
lr_scheduler_warmup_steps: 7
training_steps: 257

Training results

Framework versions

PEFT 0.17.1
Transformers 4.57.0
Pytorch 2.7.1+cu126
Datasets 4.0.0
Tokenizers 0.22.1

P4n1c0/Qwen2.5-7B-Instruct-Codi

outputs/mymodel

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

P4n1c0/
Qwen2.5-7B-Instruct-Codi