See axolotl config

axolotl version: 0.4.1

adapter: lora
base_model: unsloth/Qwen2.5-14B
bf16: true
chat_template: llama3
dataset_prepared_path: null
datasets:
- data_files:
  - dee6040e5485e8c7_train_data.json
  ds_type: json
  format: custom
  path: /workspace/input_data/dee6040e5485e8c7_train_data.json
  type:
    field_instruction: instruction
    field_output: response
    format: '{instruction}'
    no_input_format: '{instruction}'
    system_format: '{system}'
    system_prompt: ''
debug: null
device_map:
  ? ''
  : 0,1,2,3,4,5,6,7
early_stopping_patience: 2
eval_max_new_tokens: 128
eval_steps: 100
eval_table_size: null
flash_attention: true
gradient_accumulation_steps: 8
gradient_checkpointing: true
group_by_length: false
hub_model_id: Alphatao/57d3b2f8-10c6-4424-8de8-9568818d15dc
hub_repo: null
hub_strategy: null
hub_token: null
learning_rate: 0.0002
load_best_model_at_end: true
load_in_4bit: false
load_in_8bit: false
local_rank: null
logging_steps: 1
lora_alpha: 32
lora_dropout: 0.05
lora_fan_in_fan_out: null
lora_model_dir: null
lora_r: 16
lora_target_linear: true
lora_target_modules:
- q_proj
- k_proj
- v_proj
- o_proj
lr_scheduler: cosine
max_grad_norm: 1.0
max_steps: 798
micro_batch_size: 4
mlflow_experiment_name: /tmp/dee6040e5485e8c7_train_data.json
model_type: AutoModelForCausalLM
num_epochs: 2
optimizer: adamw_bnb_8bit
output_dir: miner_id_24
pad_to_sequence_len: true
resume_from_checkpoint: null
s2_attention: null
sample_packing: false
save_steps: 100
sequence_len: 1024
strict: false
tf32: true
tokenizer_type: AutoTokenizer
train_on_inputs: false
trust_remote_code: true
val_set_size: 0.05
wandb_entity: null
wandb_mode: online
wandb_name: f49dd722-77d5-4f12-8659-93526e5f93fc
wandb_project: Gradients-On-Demand
wandb_run: your_name
wandb_runid: f49dd722-77d5-4f12-8659-93526e5f93fc
warmup_steps: 10
weight_decay: 0.0
xformers_attention: null

57d3b2f8-10c6-4424-8de8-9568818d15dc

This model is a fine-tuned version of unsloth/Qwen2.5-14B on the None dataset. It achieves the following results on the evaluation set:

• Loss: 0.5386

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: 4
• eval_batch_size: 4
• seed: 42
• gradient_accumulation_steps: 8
• 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: 10
• training_steps: 798

Training results

Training Loss	Epoch	Step	Validation Loss
1.3416	0.0013	1	1.2806
0.5834	0.1292	100	0.6285
0.5348	0.2584	200	0.5913
0.5691	0.3876	300	0.5732
0.5326	0.5168	400	0.5587
0.5557	0.6460	500	0.5491
0.5881	0.7752	600	0.5422
0.5109	0.9044	700	0.5386

Framework versions

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