See axolotl config

axolotl version: 0.12.0.dev0

adapter: lora
base_model: Qwen/Qwen3-0.6B-Base
bf16: true
chat_template: llama3
dataset_prepared_path: null
datasets:
- data_files:
  - test-tour-07-24-02_train_data.json
  ds_type: json
  format: custom
  path: /workspace/axolotl/data
  type:
    field_input: input
    field_instruction: instruct
    field_output: output
    format: '{instruction} {input}'
    no_input_format: '{instruction}'
    system_format: '{system}'
    system_prompt: ''
debug: null
deepspeed: null
device_map: auto
do_eval: true
early_stopping_patience: 4
eval_batch_size: 1
eval_max_new_tokens: 128
eval_steps: 5
eval_table_size: null
evals_per_epoch: null
flash_attention: true
fp16: false
fsdp: null
fsdp_config: null
gradient_accumulation_steps: 4
gradient_checkpointing: false
group_by_length: true
learning_rate: 1.0e-05
load_in_4bit: false
load_in_8bit: false
local_rank: null
logging_steps: 50
lora_alpha: 256
lora_dropout: 0.1
lora_fan_in_fan_out: null
lora_model_dir: null
lora_r: 128
lora_target_linear: true
lr_scheduler: cosine_with_min_lr
lr_scheduler_kwargs:
  min_lr_rate: 0.01
max_grad_norm: 1.0
max_steps: 7
micro_batch_size: 1
mlflow_experiment_name: /workspace/axolotl/data/test-tour-07-24-02_train_data.json
model_type: AutoModelForCausalLM
num_epochs: 10
optim_args:
  adam_beta1: 0.9
  adam_beta2: 0.99
  adam_epsilon: 1.0e-08
optimizer: adamw_bnb_8bit
output_dir: /workspace/axolotl/outputs/test-tour-07-24-02/test-inst-07-24-02
pad_to_sequence_len: true
resume_from_checkpoint: null
s2_attention: null
sample_packing: false
save_steps: 5
saves_per_epoch: null
sequence_len: 1024
strict: false
tf32: true
tokenizer_type: AutoTokenizer
train_on_inputs: false
trust_remote_code: true
val_set_size: 0.0001
warmup_steps: 50
weight_decay: 0.0
xformers_attention: null

workspace/axolotl/outputs/test-tour-07-24-02/test-inst-07-24-02

This model was trained from scratch on the None dataset. It achieves the following results on the evaluation set:

• Loss: 2.2426

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: 1e-05
• train_batch_size: 1
• eval_batch_size: 1
• seed: 42
• gradient_accumulation_steps: 4
• total_train_batch_size: 4
• optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=adam_beta1=0.9,adam_beta2=0.99,adam_epsilon=1e-08
• lr_scheduler_type: cosine_with_min_lr
• lr_scheduler_warmup_steps: 50
• training_steps: 7

Training results

Training Loss	Epoch	Step	Validation Loss
No log	0	0	2.2422
No log	0.0002	5	2.2426

Framework versions

• PEFT 0.16.0
• Transformers 4.53.2
• Pytorch 2.6.0+cu126
• Datasets 4.0.0
• Tokenizers 0.21.2