See axolotl config

axolotl version: 0.4.1

adapter: lora
base_model: unsloth/gemma-1.1-2b-it
bf16: true
chat_template: llama3
dataset_prepared_path: null
datasets:
- data_files:
  - 6dd181807d2a0b4b_train_data.json
  ds_type: json
  format: custom
  path: /workspace/input_data/6dd181807d2a0b4b_train_data.json
  type:
    field_instruction: user_prompt
    field_output: resp
    format: '{instruction}'
    no_input_format: '{instruction}'
    system_format: '{system}'
    system_prompt: ''
debug: null
deepspeed: null
early_stopping_patience: 2
eval_max_new_tokens: 128
eval_steps: 100
eval_table_size: null
flash_attention: false
fp16: null
fsdp: null
fsdp_config: null
gradient_accumulation_steps: 8
gradient_checkpointing: true
group_by_length: false
hub_model_id: Alphatao/60d25577-def8-43a7-a4c0-8690bfe0ad93
hub_repo: null
hub_strategy: checkpoint
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: 32
lora_dropout: 0.05
lora_fan_in_fan_out: null
lora_model_dir: null
lora_r: 16
lora_target_linear: true
lora_target_modules:
- q_proj
- k_proj
- v_proj
lr_scheduler: cosine
max_grad_norm: 1.0
max_steps: 2040
micro_batch_size: 4
mlflow_experiment_name: /tmp/6dd181807d2a0b4b_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: 0.04
wandb_entity: null
wandb_mode: online
wandb_name: 26f5aa20-ade1-4294-92fd-4edb185df7e3
wandb_project: Gradients-On-Demand
wandb_run: your_name
wandb_runid: 26f5aa20-ade1-4294-92fd-4edb185df7e3
warmup_steps: 10
weight_decay: 0.0
xformers_attention: null

60d25577-def8-43a7-a4c0-8690bfe0ad93

This model is a fine-tuned version of unsloth/gemma-1.1-2b-it on the None dataset. It achieves the following results on the evaluation set:

• Loss: 0.1262

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: 2040

Training results

Training Loss	Epoch	Step	Validation Loss
1.6736	0.0009	1	1.6423
0.2001	0.0916	100	0.1746
0.2069	0.1832	200	0.1571
0.1567	0.2748	300	0.1528
0.1191	0.3664	400	0.1484
0.1494	0.4580	500	0.1450
0.1642	0.5496	600	0.1434
0.1429	0.6412	700	0.1409
0.0947	0.7328	800	0.1391
0.1027	0.8244	900	0.1360
0.0955	0.9160	1000	0.1331
0.0926	1.0076	1100	0.1332
0.1352	1.0992	1200	0.1321
0.1148	1.1907	1300	0.1312
0.0934	1.2823	1400	0.1296
0.1158	1.3739	1500	0.1293
0.1094	1.4655	1600	0.1276
0.139	1.5571	1700	0.1273
0.0921	1.6487	1800	0.1267
0.121	1.7403	1900	0.1263
0.0943	1.8319	2000	0.1262

Framework versions

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