adapter: lora
base_model: unsloth/codegemma-7b-it
chat_template: llama3
dataset_prepared_path: null
datasets:
- data_files:
  - /workspace/input_data/330967babe0999bc_train_data.json
  ds_type: json
  format: custom
  path: /workspace/input_data/330967babe0999bc_train_data.json
  type:
    field_instruction: prompt
    field_output: chosen
    format: '{instruction}'
    no_input_format: '{instruction}'
    system_format: '{system}'
    system_prompt: ''
debug: null
deepspeed: null
device_map: auto
early_stopping_patience: 3
eval_max_new_tokens: 128
eval_steps: 100
eval_table_size: null
evals_per_epoch: null
flash_attention: false
fsdp: null
fsdp_config: null
gradient_accumulation_steps: 4
gradient_checkpointing: true
group_by_length: false
hub_model_id: JoshMe1/b354949c-42b7-4cbe-9811-796a22f97efb
hub_repo: null
hub_strategy: checkpoint
hub_token: null
learning_rate: 5.0e-05
load_in_8bit: true
local_rank: null
logging_steps: 10
lora_alpha: 128
lora_dropout: 0.1
lora_fan_in_fan_out: null
lora_model_dir: null
lora_r: 64
lora_target_linear: true
lr_scheduler: cosine
max_grad_norm: 1.0
max_memory:
  0: 130GB
max_steps: 200
micro_batch_size: 2
mixed_precision: bf16
mlflow_experiment_name: /tmp/330967babe0999bc_train_data.json
model_type: AutoModelForCausalLM
num_epochs: 10
offload_folder: /workspace/offload/98f10626-0a57-4f12-96ca-efb2f4bdc1bc
optimizer: adamw_hf
output_dir: miner_id_24
pad_to_sequence_len: true
resume_from_checkpoint: null
s2_attention: null
sample_packing: false
save_steps: 100
saves_per_epoch: null
sequence_len: 2048
strict: false
tokenizer_type: AutoTokenizer
train_on_inputs: false
trust_remote_code: true
val_set_size: 0.05
wandb_entity: null
wandb_mode: online
wandb_name: 98f10626-0a57-4f12-96ca-efb2f4bdc1bc
wandb_project: Gradients-On-Demand
wandb_run: your_name
wandb_runid: 98f10626-0a57-4f12-96ca-efb2f4bdc1bc
warmup_steps: 200
weight_decay: 0.01
xformers_attention: null

b354949c-42b7-4cbe-9811-796a22f97efb

This model is a fine-tuned version of unsloth/codegemma-7b-it on the None dataset. It achieves the following results on the evaluation set:

Loss: 1.3027

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: 5e-05
train_batch_size: 2
eval_batch_size: 2
seed: 42
gradient_accumulation_steps: 4
total_train_batch_size: 8
optimizer: Use OptimizerNames.ADAMW_HF 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: 200
training_steps: 200

Training results

Training Loss	Epoch	Step	Validation Loss
No log	0.0006	1	3.3958
1.6506	0.0637	100	1.3871
1.547	0.1275	200	1.3027

Framework versions

PEFT 0.13.2
Transformers 4.46.0
Pytorch 2.5.0+cu124
Datasets 3.0.1
Tokenizers 0.20.1