See axolotl config

axolotl version: 0.4.1

adapter: lora
base_model: unsloth/Qwen2.5-Math-1.5B-Instruct
bf16: auto
chat_template: llama3
dataset_prepared_path: null
datasets:
- data_files:
  - 924cb9683b01f3b9_train_data.json
  ds_type: json
  format: custom
  path: /workspace/input_data/924cb9683b01f3b9_train_data.json
  type:
    field_instruction: review
    field_output: domain
    format: '{instruction}'
    no_input_format: '{instruction}'
    system_format: '{system}'
    system_prompt: ''
debug: null
deepspeed: null
early_stopping_patience: null
eval_max_new_tokens: 128
eval_table_size: null
evals_per_epoch: 4
flash_attention: false
fp16: null
fsdp: null
fsdp_config: null
gradient_accumulation_steps: 4
gradient_checkpointing: true
group_by_length: false
hub_model_id: leixa/bcd5c5b1-7cae-48d6-8646-0d38d3d1f107
hub_repo: null
hub_strategy: checkpoint
hub_token: null
learning_rate: 0.0001
load_in_4bit: false
load_in_8bit: false
local_rank: 0
logging_steps: 3
lora_alpha: 64
lora_dropout: 0.1
lora_fan_in_fan_out: null
lora_model_dir: null
lora_r: 32
lora_target_linear: true
lr_scheduler: cosine
max_memory:
  0: 72GB
max_steps: 50
micro_batch_size: 4
mlflow_experiment_name: /tmp/924cb9683b01f3b9_train_data.json
model_type: AutoModelForCausalLM
num_epochs: 3
optimizer: adamw_bnb_8bit
output_dir: miner_id_24
pad_to_sequence_len: true
resume_from_checkpoint: null
s2_attention: false
sample_packing: false
saves_per_epoch: 4
sequence_len: 1024
strict: false
tf32: false
tokenizer_type: AutoTokenizer
train_on_inputs: false
trust_remote_code: true
val_set_size: 0.05
wandb_entity: leixa-personal
wandb_mode: online
wandb_name: bcd5c5b1-7cae-48d6-8646-0d38d3d1f107
wandb_project: Gradients-On-Demand
wandb_run: your_name
wandb_runid: bcd5c5b1-7cae-48d6-8646-0d38d3d1f107
warmup_steps: 10
weight_decay: 0.01
xformers_attention: 

bcd5c5b1-7cae-48d6-8646-0d38d3d1f107

This model is a fine-tuned version of unsloth/Qwen2.5-Math-1.5B-Instruct on the None dataset. It achieves the following results on the evaluation set:

• Loss: nan

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: 0.0001
• train_batch_size: 4
• eval_batch_size: 4
• seed: 42
• gradient_accumulation_steps: 4
• total_train_batch_size: 16
• 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: 50

Training results

Training Loss	Epoch	Step	Validation Loss
No log	0.0032	1	nan
0.0	0.0159	5	nan
0.0	0.0319	10	nan
0.0	0.0478	15	nan
0.0	0.0637	20	nan
0.0	0.0797	25	nan
0.0	0.0956	30	nan
0.0	0.1116	35	nan
0.0	0.1275	40	nan
0.0	0.1434	45	nan
0.0	0.1594	50	nan

Framework versions

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