See axolotl config

axolotl version: 0.4.1

absolute_data_files: false
adapter: lora
base_model: unsloth/Qwen2-1.5B
bf16: true
chat_template: llama3
dataset_prepared_path: /workspace/axolotl
datasets:
- data_files:
  - 2c2a9d4b17e0bbf6_train_data.json
  ds_type: json
  format: custom
  path: /workspace/input_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
dpo:
  beta: 0.1
  enabled: true
  group_by_length: false
  rank_loss: true
  reference_model: null
early_stopping_patience: null
eval_max_new_tokens: 128
eval_table_size: null
evals_per_epoch: 1
flash_attention: true
fp16: null
fsdp: null
fsdp_config: null
gradient_accumulation_steps: 2
gradient_checkpointing: true
gradient_clipping: 0.85
group_by_length: false
hub_model_id: dimasik2987/bfd33227-4f40-4e64-bbbf-6878fa05b4e3
hub_repo: null
hub_strategy: end
hub_token: null
learning_rate: 2.0e-07
load_in_4bit: true
load_in_8bit: false
local_rank: null
logging_steps: 1
lora_alpha: 32
lora_dropout: 0.3
lora_fan_in_fan_out: null
lora_model_dir: null
lora_r: 16
lora_target_linear: true
lr_scheduler: cosine
max_steps: 300
micro_batch_size: 12
mixed_precision: bf16
mlflow_experiment_name: /tmp/2c2a9d4b17e0bbf6_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
saves_per_epoch: 1
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: null
wandb_mode: online
wandb_name: 5d0e2151-c6c9-4d7f-bd25-c3862221a1c1
wandb_project: s56-7
wandb_run: your_name
wandb_runid: 5d0e2151-c6c9-4d7f-bd25-c3862221a1c1
warmup_steps: 30
weight_decay: 0.02
xformers_attention: true

bfd33227-4f40-4e64-bbbf-6878fa05b4e3

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

• Loss: 1.4927

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: 2e-07
• train_batch_size: 12
• eval_batch_size: 12
• seed: 42
• gradient_accumulation_steps: 2
• total_train_batch_size: 24
• 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: 30
• training_steps: 300

Training results

Training Loss	Epoch	Step	Validation Loss
1.4626	0.0000	1	1.4929
1.3415	0.0052	150	1.4928
2.1546	0.0105	300	1.4927

Framework versions

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