See axolotl config

axolotl version: 0.4.1

base_model: unsloth/Llama-3.2-1B
model_type: AutoModelForCausalLM
tokenizer_type: AutoTokenizer
load_in_8bit: true
load_in_4bit: false
strict: false
chat_template: chatml
datasets:
- path: /workspace/input_data/a3a475aa05af65e1_train_data.json
  format: custom
  type:
    system_prompt: ''
    system_format: '{system}'
    field_instruction: orig
    field_input: mt
    field_output: ref
    no_input_format: '{instruction}'
    format: '{instruction} {input}'
  ds_type: json
  data_files:
  - a3a475aa05af65e1_train_data.json
dataset_prepared_path: null
val_set_size: 0.05
output_dir: miner_id_925a50c0-9a58-4a15-b5ac-c711ed1b6ac2
sequence_len: 4056
sample_packing: false
pad_to_sequence_len: true
trust_remote_code: true
adapter: lora
lora_model_dir: null
lora_r: 16
lora_alpha: 32
lora_dropout: 0.05
lora_target_linear: true
lora_fan_in_fan_out: null
gradient_accumulation_steps: 4
micro_batch_size: 2
num_epochs: 1
optimizer: adamw_bnb_8bit
lr_scheduler: cosine
learning_rate: 0.0002
train_on_inputs: false
group_by_length: false
bf16: auto
fp16: null
tf32: false
gradient_checkpointing: false
early_stopping_patience: null
resume_from_checkpoint: null
local_rank: null
logging_steps: 1
xformers_attention: null
flash_attention: true
s2_attention: null
wandb_project: Gradients-On-Demand
wandb_entity: prongsie
wandb_mode: online
wandb_run: your_name
wandb_runid: default
hub_model_id: tensor24/miner_id_925a50c0-9a58-4a15-b5ac-c711ed1b6ac2
hub_repo: tensor24/miner_id_925a50c0-9a58-4a15-b5ac-c711ed1b6ac2
hub_strategy: checkpoint
hub_token: null
saves_per_epoch: 4
warmup_steps: 10
evals_per_epoch: 4
eval_table_size: null
eval_max_new_tokens: 128
max_steps: 10
debug: null
deepspeed: null
weight_decay: 0.0
fsdp: null
fsdp_config: null
tokenizer_config: unsloth/Llama-3.2-1B
mlflow_experiment_name: /tmp/a3a475aa05af65e1_train_data.json

miner_id_925a50c0-9a58-4a15-b5ac-c711ed1b6ac2

This model is a fine-tuned version of unsloth/Llama-3.2-1B on the None dataset. It achieves the following results on the evaluation set:

• Loss: 1.6795

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: 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: 10

Training results

Training Loss	Epoch	Step	Validation Loss
2.3927	0.0015	1	2.4994
2.7947	0.0046	3	2.4646
2.0712	0.0091	6	2.1699
1.8714	0.0137	9	1.6795

Framework versions

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