See axolotl config

axolotl version: 0.4.1

base_model: unsloth/tinyllama
model_type: AutoModelForCausalLM
tokenizer_type: AutoTokenizer
load_in_8bit: true
load_in_4bit: false
strict: false
chat_template: chatml
datasets:
- path: /workspace/input_data/f9f8e85d46824c0d_train_data.json
  format: custom
  type:
    system_prompt: ''
    system_format: '{system}'
    field_instruction: instruction
    field_input: input
    field_output: output
    no_input_format: '{instruction}'
    format: '{instruction} {input}'
  ds_type: json
  data_files:
  - f9f8e85d46824c0d_train_data.json
dataset_prepared_path: null
val_set_size: 0.05
output_dir: miner_id_0a58d371-839b-4ee2-9d1d-00415d9d5801
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_0a58d371-839b-4ee2-9d1d-00415d9d5801
hub_repo: tensor24/miner_id_0a58d371-839b-4ee2-9d1d-00415d9d5801
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/tinyllama
mlflow_experiment_name: /tmp/f9f8e85d46824c0d_train_data.json

miner_id_0a58d371-839b-4ee2-9d1d-00415d9d5801

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

• Loss: 3.4657

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
3.2239	0.0000	1	3.4657
3.5663	0.0001	3	3.4657
3.1978	0.0001	6	3.4657
3.6238	0.0002	9	3.4657

Framework versions

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