See axolotl config

axolotl version: 0.4.1

adapter: lora
base_model: TinyLlama/TinyLlama-1.1B-Chat-v1.0
bf16: auto
chat_template: llama3
dataset_prepared_path: null
datasets:
- data_files:
  - 86cd1095ac8c547d_train_data.json
  ds_type: json
  format: custom
  path: /workspace/input_data/86cd1095ac8c547d_train_data.json
  type:
    field_instruction: instruction
    field_output: output
    format: '{instruction}'
    no_input_format: '{instruction}'
    system_format: '{system}'
    system_prompt: ''
debug: null
deepspeed: null
early_stopping_patience: null
eval_max_new_tokens: 128
eval_table_size: null
evals_per_epoch: 4
flash_attention: false
fp16: null
fsdp: null
fsdp_config: null
gradient_accumulation_steps: 6
gradient_checkpointing: true
group_by_length: false
hub_model_id: dzanbek/6cf67e83-d9ff-45a6-ba9b-eb893518c255
hub_repo: null
hub_strategy: checkpoint
hub_token: null
learning_rate: 0.0001
load_in_4bit: false
load_in_8bit: false
local_rank: null
logging_steps: 1
lora_alpha: 128
lora_dropout: 0.05
lora_fan_in_fan_out: null
lora_model_dir: null
lora_r: 64
lora_target_linear: true
lr_scheduler: linear
max_memory:
  0: 70GiB
max_steps: 25
micro_batch_size: 4
mlflow_experiment_name: /tmp/86cd1095ac8c547d_train_data.json
model_type: AutoModelForCausalLM
num_epochs: 3
optimizer: adamw_torch
output_dir: miner_id_24
pad_to_sequence_len: true
resume_from_checkpoint: null
s2_attention: null
sample_packing: false
save_steps: 25
save_strategy: steps
sequence_len: 4056
strict: false
tf32: false
tokenizer_type: AutoTokenizer
torch_dtype: bfloat16
train_on_inputs: false
trust_remote_code: true
val_set_size: 0.05
wandb_entity: null
wandb_mode: online
wandb_name: 6cf67e83-d9ff-45a6-ba9b-eb893518c255
wandb_project: Gradients-On-Demand
wandb_run: your_name
wandb_runid: 6cf67e83-d9ff-45a6-ba9b-eb893518c255
warmup_ratio: 0.05
weight_decay: 0.01
xformers_attention: null

6cf67e83-d9ff-45a6-ba9b-eb893518c255

This model is a fine-tuned version of TinyLlama/TinyLlama-1.1B-Chat-v1.0 on the None dataset. It achieves the following results on the evaluation set:

• Loss: 1.2117

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: 0.0001
• train_batch_size: 4
• eval_batch_size: 4
• seed: 42
• gradient_accumulation_steps: 6
• total_train_batch_size: 24
• optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
• lr_scheduler_type: linear
• lr_scheduler_warmup_steps: 2
• training_steps: 25

Training results

Training Loss	Epoch	Step	Validation Loss
1.1921	0.0005	1	1.2968
1.4707	0.0015	3	1.2734
1.244	0.0029	6	1.2400
1.2275	0.0044	9	1.2271
1.283	0.0059	12	1.2219
1.1353	0.0073	15	1.2176
1.2338	0.0088	18	1.2144
1.1743	0.0103	21	1.2125
1.2684	0.0118	24	1.2117

Framework versions

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