See axolotl config

axolotl version: 0.4.1

adapter: lora
auto_resume_from_checkpoints: true
base_model: unsloth/Qwen2-0.5B-Instruct
bf16: auto
chat_template: llama3
dataset_prepared_path: null
dataset_processes: 6
datasets:
- data_files:
  - abb94b78ae55b9e1_train_data.json
  ds_type: json
  format: custom
  path: /workspace/input_data/abb94b78ae55b9e1_train_data.json
  type:
    field_input: project
    field_instruction: func
    field_output: commit_id
    format: '{instruction} {input}'
    no_input_format: '{instruction}'
    system_format: '{system}'
    system_prompt: ''
debug: null
deepspeed: null
early_stopping_patience: 3
eval_max_new_tokens: 128
eval_steps: 200
eval_table_size: null
evals_per_epoch: null
flash_attention: true
fp16: false
fsdp: null
fsdp_config: null
gradient_accumulation_steps: 4
gradient_checkpointing: true
group_by_length: false
hub_model_id: error577/ad5a5b94-1527-4819-9f7b-2c3bca34b922
hub_repo: null
hub_strategy: checkpoint
hub_token: null
learning_rate: 0.0002
load_in_4bit: false
load_in_8bit: false
local_rank: null
logging_steps: 1
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_grad_norm: 1.0
max_steps: null
micro_batch_size: 6
mlflow_experiment_name: /tmp/abb94b78ae55b9e1_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: null
sample_packing: false
save_steps: 200
sequence_len: 256
strict: false
tf32: false
tokenizer_type: AutoTokenizer
train_on_inputs: false
trust_remote_code: true
val_set_size: 0.005
wandb_entity: null
wandb_mode: online
wandb_name: a0c4bdef-b9e4-45df-9d85-5d0d0fb1ffc8
wandb_project: Gradients-On-Demand
wandb_run: your_name
wandb_runid: a0c4bdef-b9e4-45df-9d85-5d0d0fb1ffc8
warmup_steps: 30
weight_decay: 0.0
xformers_attention: null

ad5a5b94-1527-4819-9f7b-2c3bca34b922

This model is a fine-tuned version of unsloth/Qwen2-0.5B-Instruct on the None dataset. It achieves the following results on the evaluation set:

• Loss: 1.6623

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: 6
• eval_batch_size: 6
• seed: 42
• gradient_accumulation_steps: 4
• 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
• num_epochs: 3

Training results

Training Loss	Epoch	Step	Validation Loss
4.1807	0.0009	1	4.1626
2.993	0.1819	200	3.0146
2.4889	0.3638	400	2.8917
2.6415	0.5457	600	2.6178
2.7233	0.7276	800	2.4421
2.7078	0.9095	1000	2.3523
2.4733	1.0916	1200	2.2602
2.0932	1.2735	1400	2.1569
1.7869	1.4554	1600	2.0391
2.1297	1.6373	1800	1.9163
1.8438	1.8192	2000	1.8159
1.2123	2.0014	2200	1.7644
1.6842	2.1833	2400	1.7493
1.2005	2.3652	2600	1.7107
1.9417	2.5471	2800	1.6858
1.5769	2.7290	3000	1.6666
1.6061	2.9109	3200	1.6623

Framework versions

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