See axolotl config

axolotl version: 0.4.1

adapter: qlora
base_model: unsloth/SmolLM-1.7B-Instruct
bf16: true
chat_template: llama3
dataloader_num_workers: 6
dataset_prepared_path: null
datasets:
- data_files:
  - b4d50b4ebb62bd26_train_data.json
  ds_type: json
  format: custom
  path: /workspace/input_data/b4d50b4ebb62bd26_train_data.json
  type:
    field_instruction: instruction
    field_output: response
    format: '{instruction}'
    no_input_format: '{instruction}'
    system_format: '{system}'
    system_prompt: ''
debug: null
deepspeed: null
early_stopping_patience: 3
eval_max_new_tokens: 128
eval_steps: 50
eval_table_size: null
flash_attention: true
fp16: null
fsdp: null
fsdp_config: null
gradient_accumulation_steps: 4
gradient_checkpointing: true
group_by_length: true
hub_model_id: error577/e5c7898a-4409-476c-b0e6-2048d1c3b9c8
hub_repo: null
hub_strategy: checkpoint
hub_token: null
learning_rate: 0.002
load_in_4bit: true
load_in_8bit: false
local_rank: null
logging_steps: 1
lora_alpha: 64
lora_dropout: 0.3
lora_fan_in_fan_out: null
lora_model_dir: null
lora_r: 128
lora_target_linear: true
lr_scheduler: cosine
max_grad_norm: 1.0
max_steps: 1000
auto_resume_from_checkpoints: true
micro_batch_size: 2
mlflow_experiment_name: /tmp/b4d50b4ebb62bd26_train_data.json
model_type: AutoModelForCausalLM
num_epochs: 4
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: 50
sequence_len: 512
strict: false
tf32: false
tokenizer_type: AutoTokenizer
train_on_inputs: false
trust_remote_code: true
val_set_size: 0.002
wandb_entity: null
wandb_mode: online
wandb_name: b8be9756-84be-4b64-aa93-937f8a9f4691
wandb_project: Gradients-On-Demand
wandb_run: your_name
wandb_runid: b8be9756-84be-4b64-aa93-937f8a9f4691
warmup_steps: 10
weight_decay: 0.01
xformers_attention: null

e5c7898a-4409-476c-b0e6-2048d1c3b9c8

This model is a fine-tuned version of unsloth/SmolLM-1.7B-Instruct on the None dataset. It achieves the following results on the evaluation set:

• Loss: 0.7097

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: 0.002
• train_batch_size: 2
• eval_batch_size: 2
• seed: 42
• gradient_accumulation_steps: 4
• total_train_batch_size: 8
• 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: 1000

Training results

Training Loss	Epoch	Step	Validation Loss
1.23	0.0000	1	1.0243
0.8101	0.0014	50	0.9230
0.6549	0.0028	100	1.0666
0.7171	0.0042	150	0.8552
0.606	0.0056	200	0.7939
0.5582	0.0069	250	0.7649
0.7431	0.0083	300	0.7490
0.7017	0.0097	350	0.7206
0.7743	0.0111	400	0.7006
0.507	0.0125	450	0.6939
0.5217	0.0139	500	0.6911
0.7122	0.0153	550	0.7409
0.6526	0.0167	600	0.7192
0.5694	0.0181	650	0.7097

Framework versions

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