adapter: lora
base_model: Qwen/Qwen2.5-0.5B
bf16: true
chat_template: llama3
dataset_prepared_path: null
datasets:
- data_files:
  - /workspace/input_data/0adf243bcbbf21a5_train_data.json
  ds_type: json
  format: custom
  path: /workspace/input_data/0adf243bcbbf21a5_train_data.json
  type:
    field_input: context
    field_instruction: label
    field_output: target
    format: '{instruction} {input}'
    no_input_format: '{instruction}'
    system_format: '{system}'
    system_prompt: ''
debug: null
deepspeed: null
device_map: auto
eval_max_new_tokens: 128
eval_strategy: 'no'
eval_table_size: null
evals_per_epoch: null
evaluation_strategy: 'no'
flash_attention: false
fp16: false
fsdp: null
fsdp_config: null
gradient_accumulation_steps: 4
gradient_checkpointing: true
group_by_length: false
hub_model_id: JoshMe1/92fbb182-7064-48b1-8801-292555e7f019
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_memory:
  0: 130GB
max_steps: 200
micro_batch_size: 8
mlflow_experiment_name: /tmp/0adf243bcbbf21a5_train_data.json
model_type: AutoModelForCausalLM
num_epochs: 10
offload_folder: /workspace/offload/1b111512-428a-4811-bde9-08ef88312d7f
optimizer: adamw_bnb_8bit
output_dir: miner_id_24
pad_to_sequence_len: true
resume_from_checkpoint: null
s2_attention: null
sample_packing: false
saves_per_epoch: null
sequence_len: 2048
strict: false
tf32: 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: 1b111512-428a-4811-bde9-08ef88312d7f
wandb_project: Gradients-On-Demand
wandb_run: your_name
wandb_runid: 1b111512-428a-4811-bde9-08ef88312d7f
warmup_steps: 200
weight_decay: 0.01
xformers_attention: null

92fbb182-7064-48b1-8801-292555e7f019

This model is a fine-tuned version of Qwen/Qwen2.5-0.5B on the None 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: 5e-05
train_batch_size: 8
eval_batch_size: 8
seed: 42
gradient_accumulation_steps: 4
total_train_batch_size: 32
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: 200
training_steps: 200

Training results

Framework versions

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