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nttx/
ad5dfcba-2ca7-4ce6-b03d-4a7146e3f3f4

generated_from_trainer

echarlaix/tiny-random-mistral

adapter

apache-2.0

Views

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See axolotl config

axolotl version: 0.4.1

yaml

adapter: lora
base_model: echarlaix/tiny-random-mistral
bf16: auto
chat_template: llama3
dataloader_num_workers: 12
dataset_prepared_path: null
datasets:
- data_files:
  - 83c133e7943c6457_train_data.json
  ds_type: json
  format: custom
  path: /workspace/input_data/83c133e7943c6457_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
device_map: auto
do_eval: true
early_stopping_patience: 3
eval_batch_size: 8
eval_max_new_tokens: 128
eval_steps: 150
eval_table_size: null
evals_per_epoch: null
flash_attention: true
fp16: false
fsdp: null
fsdp_config: null
gradient_accumulation_steps: 2
gradient_checkpointing: true
group_by_length: true
hub_model_id: nttx/ad5dfcba-2ca7-4ce6-b03d-4a7146e3f3f4
hub_repo: null
hub_strategy: end
hub_token: null
learning_rate: 3e-5
load_in_4bit: false
load_in_8bit: false
local_rank: null
logging_steps: 10
lora_alpha: 128
lora_dropout: 0.1
lora_fan_in_fan_out: null
lora_model_dir: null
lora_r: 64
lora_target_linear: true
lr_scheduler: cosine
max_grad_norm: 1.0
max_memory:
  0: 75GB
max_steps: 1500
micro_batch_size: 8
mlflow_experiment_name: /tmp/83c133e7943c6457_train_data.json
model_type: AutoModelForCausalLM
num_epochs: 15
optim_args:
  adam_beta1: 0.9
  adam_beta2: 0.999
  adam_epsilon: 1e-8
optimizer: adamw_torch
output_dir: miner_id_24
pad_to_sequence_len: false
resume_from_checkpoint: null
s2_attention: null
sample_packing: false
save_steps: 150
saves_per_epoch: null
sequence_len: 1024
special_tokens:
  pad_token: </s>
strict: false
tf32: true
tokenizer_type: AutoTokenizer
train_on_inputs: false
trust_remote_code: true
val_set_size: 0.05
wandb_entity: null
wandb_mode: online
wandb_name: 0aa48c34-51fd-465b-8c6a-28ab563d9dfd
wandb_project: Gradients-On-Demand
wandb_run: your_name
wandb_runid: 0aa48c34-51fd-465b-8c6a-28ab563d9dfd
warmup_steps: 50
weight_decay: 0.1
xformers_attention: null

ad5dfcba-2ca7-4ce6-b03d-4a7146e3f3f4

This model is a fine-tuned version of echarlaix/tiny-random-mistral on the None dataset. It achieves the following results on the evaluation set:

Loss: 10.1620

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: 3e-05
train_batch_size: 8
eval_batch_size: 8
seed: 42
gradient_accumulation_steps: 2
total_train_batch_size: 16
optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=adam_beta1=0.9,adam_beta2=0.999,adam_epsilon=1e-8
lr_scheduler_type: cosine
lr_scheduler_warmup_steps: 50
training_steps: 1500

Training results

Training Loss	Epoch	Step	Validation Loss
No log	0.0002	1	10.3859
20.5507	0.0244	150	10.2800
20.4612	0.0488	300	10.2454
20.3891	0.0732	450	10.2206
20.2875	0.0975	600	10.1924
20.2469	0.1219	750	10.1786
20.2404	0.1463	900	10.1704
20.2157	0.1707	1050	10.1655
20.2051	0.1951	1200	10.1630
20.2006	0.2195	1350	10.1621
20.2142	0.2438	1500	10.1620

Framework versions

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