You can deploy this model and start earning money today!

Alphatao/
ed9a17a2-af1c-448a-8891-f72155b6f9e3

Name: ed9a17a2-af1c-448a-8891-f72155b6f9e3
Availability: InStock

generated_from_trainer

fxmarty/tiny-random-GemmaForCausalLM

adapter

mit

Views

No views yet

See axolotl config

axolotl version: 0.4.1

yaml

adapter: lora
base_model: fxmarty/tiny-random-GemmaForCausalLM
bf16: true
chat_template: llama3
dataset_prepared_path: null
datasets:
- data_files:
  - 5a30ac0600bac67f_train_data.json
  ds_type: json
  format: custom
  path: /workspace/input_data/5a30ac0600bac67f_train_data.json
  type:
    field_input: schema
    field_instruction: question
    field_output: cypher
    format: '{instruction} {input}'
    no_input_format: '{instruction}'
    system_format: '{system}'
    system_prompt: ''
debug: null
device_map:
  ? ''
  : 0,1,2,3,4,5,6,7
early_stopping_patience: 2
eval_max_new_tokens: 128
eval_steps: 100
eval_table_size: null
flash_attention: false
gradient_accumulation_steps: 8
gradient_checkpointing: true
group_by_length: false
hub_model_id: Alphatao/ed9a17a2-af1c-448a-8891-f72155b6f9e3
hub_repo: null
hub_strategy: null
hub_token: null
learning_rate: 0.0002
load_best_model_at_end: true
load_in_4bit: false
load_in_8bit: false
local_rank: null
logging_steps: 1
lora_alpha: 32
lora_dropout: 0.05
lora_fan_in_fan_out: null
lora_model_dir: null
lora_r: 16
lora_target_linear: true
lora_target_modules:
- q_proj
- k_proj
- v_proj
- o_proj
lr_scheduler: cosine
max_grad_norm: 1.0
max_steps: 4140
micro_batch_size: 4
mlflow_experiment_name: /tmp/5a30ac0600bac67f_train_data.json
model_type: AutoModelForCausalLM
num_epochs: 2
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: 100
sequence_len: 1024
strict: false
tf32: true
tokenizer_type: AutoTokenizer
train_on_inputs: false
trust_remote_code: true
val_set_size: 0.04
wandb_entity: null
wandb_mode: online
wandb_name: 1c0eb991-6867-4728-aeca-d5aa7665414c
wandb_project: Gradients-On-Demand
wandb_run: your_name
wandb_runid: 1c0eb991-6867-4728-aeca-d5aa7665414c
warmup_steps: 10
weight_decay: 0.0
xformers_attention: null

ed9a17a2-af1c-448a-8891-f72155b6f9e3

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: 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: 8
total_train_batch_size: 32
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: 2598

Training results

Epoch	Step	Validation Loss
0.0008	1	nan
0.0770	100	nan
0.1540	200	nan
0.2310	300	nan

Framework versions

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