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tuantmdev/
b32f2e88-2acd-4f9b-99bd-8d8635bd77fb

Name: b32f2e88-2acd-4f9b-99bd-8d8635bd77fb
Availability: InStock

generated_from_trainer

fxmarty/tiny-random-GemmaForCausalLM

adapter

mit

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See axolotl config

axolotl version: 0.4.1

yaml

adapter: lora
auto_find_batch_size: true
base_model: fxmarty/tiny-random-GemmaForCausalLM
bf16: auto
chat_template: llama3
dataset_prepared_path: null
datasets:
- data_files:
  - 95621c23f229fe74_train_data.json
  ds_type: json
  format: custom
  path: /workspace/input_data/95621c23f229fe74_train_data.json
  type:
    field_input: tools
    field_instruction: query
    field_output: answers
    format: '{instruction} {input}'
    no_input_format: '{instruction}'
    system_format: '{system}'
    system_prompt: ''
debug: null
deepspeed: null
early_stopping_patience: 3
eval_max_new_tokens: 128
eval_steps: 50
eval_table_size: null
flash_attention: true
fp16: null
fsdp: null
fsdp_config: null
gradient_accumulation_steps: 8
gradient_checkpointing: false
group_by_length: true
hub_model_id: tuantmdev/b32f2e88-2acd-4f9b-99bd-8d8635bd77fb
hub_repo: null
hub_strategy: checkpoint
hub_token: null
learning_rate: 1e-4
load_in_4bit: false
load_in_8bit: false
local_rank: null
logging_steps: 40
lora_alpha: 64
lora_dropout: 0.05
lora_fan_in_fan_out: null
lora_model_dir: null
lora_r: 32
lora_target_linear: true
lr_scheduler: cosine
max_grad_norm: 1.0
max_steps: 400
micro_batch_size: 2
mlflow_experiment_name: /tmp/95621c23f229fe74_train_data.json
model_type: AutoModelForCausalLM
num_epochs: 1
optimizer: adamw_bnb_8bit
output_dir: miner_id_24
pad_to_sequence_len: true
resume_from_checkpoint: null
s2_attention: null
sample_packing: false
save_steps: 50
save_strategy: steps
sequence_len: 512
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: e75973b3-c17e-44e4-b527-21c602afd6c4
wandb_project: Gradients-On-Demand
wandb_run: unknown
wandb_runid: e75973b3-c17e-44e4-b527-21c602afd6c4
warmup_steps: 80
weight_decay: 0.0
xformers_attention: null

b32f2e88-2acd-4f9b-99bd-8d8635bd77fb

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

Loss: 12.3238

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: 2
eval_batch_size: 2
seed: 42
gradient_accumulation_steps: 8
total_train_batch_size: 16
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: 80
training_steps: 400

Training results

Training Loss	Epoch	Step	Validation Loss
No log	0.0003	1	12.4421
12.4388	0.0172	50	12.4412
12.4395	0.0343	100	12.4253
12.4242	0.0515	150	12.3708
12.3554	0.0686	200	12.3421
12.3321	0.0858	250	12.3301
12.3328	0.1029	300	12.3257
12.33	0.1201	350	12.3246
12.3251	0.1372	400	12.3238

Framework versions

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