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dimasik2987/
222e7b72-7177-4b2d-bdce-212df7a26b40

Name: 222e7b72-7177-4b2d-bdce-212df7a26b40
Availability: InStock

generated_from_trainer

unsloth/tinyllama

adapter

apache-2.0

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See axolotl config

axolotl version: 0.4.1

yaml

adapter: lora
base_model: unsloth/tinyllama
bf16: auto
chat_template: llama3
dataset_prepared_path: null
datasets:
- data_files:
  - a9c8fa84d5fb36b6_train_data.json
  ds_type: json
  field: neighbourhood_group
  path: /workspace/input_data/a9c8fa84d5fb36b6_train_data.json
  type: completion
debug: null
deepspeed: null
early_stopping_patience: null
eval_max_new_tokens: 128
eval_table_size: null
evals_per_epoch: 3
flash_attention: false
fp16: true
fsdp: null
fsdp_config: null
gradient_accumulation_steps: 6
gradient_checkpointing: true
group_by_length: false
hub_model_id: dimasik2987/222e7b72-7177-4b2d-bdce-212df7a26b40
hub_repo: null
hub_strategy: checkpoint
hub_token: null
learning_rate: 0.0002
load_in_4bit: false
load_in_8bit: false
local_rank: null
logging_steps: 1
lora_alpha: 32
lora_dropout: 0.1
lora_fan_in_fan_out: null
lora_model_dir: null
lora_r: 16
lora_target_linear: true
lr_scheduler: cosine
max_memory:
  0: 70GiB
max_steps: 50
micro_batch_size: 4
mlflow_experiment_name: /tmp/a9c8fa84d5fb36b6_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: 2028
strict: false
tf32: false
tokenizer_type: AutoTokenizer
train_on_inputs: false
trust_remote_code: true
val_set_size: 0.05
wandb_entity: null
wandb_mode: online
wandb_name: 222e7b72-7177-4b2d-bdce-212df7a26b40
wandb_project: Gradients-On-Demand
wandb_run: your_name
wandb_runid: 222e7b72-7177-4b2d-bdce-212df7a26b40
warmup_steps: 10
weight_decay: 0.01
xformers_attention: null

222e7b72-7177-4b2d-bdce-212df7a26b40

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

Loss: nan

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: 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: cosine
lr_scheduler_warmup_steps: 10
training_steps: 50

Training results

Epoch	Step	Validation Loss
0.0005	1	nan
0.0031	6	nan
0.0062	12	nan
0.0093	18	nan
0.0124	24	nan
0.0155	30	nan
0.0186	36	nan
0.0217	42	nan
0.0249	48	nan

Framework versions

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