See axolotl config

axolotl version: 0.4.1

adapter: lora
base_model: unsloth/Qwen2.5-3B-Instruct
bf16: true
chat_template: llama3
dataset_prepared_path: null
datasets:
- data_files:
  - ee246e1cf44907e3_train_data.json
  ds_type: json
  format: custom
  path: /workspace/input_data/ee246e1cf44907e3_train_data.json
  type:
    field_input: alpaca_prompt
    field_instruction: instruction
    field_output: response
    format: '{instruction} {input}'
    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/8c18c43d-90b9-4648-969f-63d26cb472cf
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: 128
lora_dropout: 0.1
lora_fan_in_fan_out: null
lora_model_dir: null
lora_r: 64
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: 2346
micro_batch_size: 4
mlflow_experiment_name: /tmp/ee246e1cf44907e3_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: f478fe17-192c-420c-a529-b29997372dec
wandb_project: Gradients-On-Demand
wandb_run: your_name
wandb_runid: f478fe17-192c-420c-a529-b29997372dec
warmup_steps: 10
weight_decay: 0.0
xformers_attention: null

8c18c43d-90b9-4648-969f-63d26cb472cf

This model is a fine-tuned version of unsloth/Qwen2.5-3B-Instruct on the None dataset. It achieves the following results on the evaluation set:

• Loss: 1.4584

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: 2346

Training results

Training Loss	Epoch	Step	Validation Loss
2.4598	0.0005	1	2.6155
1.7502	0.0542	100	1.6519
1.757	0.1084	200	1.6321
1.3307	0.1625	300	1.6223
1.7703	0.2167	400	1.6144
1.6688	0.2709	500	1.6013
1.7145	0.3251	600	1.5877
1.4751	0.3792	700	1.5761
1.564	0.4334	800	1.5609
1.2658	0.4876	900	1.5493
1.3008	0.5418	1000	1.5364
1.4833	0.5960	1100	1.5234
1.4851	0.6501	1200	1.5088
1.7207	0.7043	1300	1.4980
1.7282	0.7585	1400	1.4884
1.4935	0.8127	1500	1.4766
1.4607	0.8669	1600	1.4662
1.1588	0.9210	1700	1.4571
1.5499	0.9752	1800	1.4506
1.1475	1.0294	1900	1.4587
0.9174	1.0836	2000	1.4584

Framework versions

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