You can deploy this model and start earning money today!

dimasik1987/
3f060a0c-d67f-4bdf-8681-c858920d364f

Name: 3f060a0c-d67f-4bdf-8681-c858920d364f
Availability: InStock

generated_from_trainer

UCLA-AGI/Gemma-2-9B-It-SPPO-Iter2

adapter

gemma

Views

No views yet

See axolotl config

axolotl version: 0.4.1

yaml

adapter: lora
base_model: UCLA-AGI/Gemma-2-9B-It-SPPO-Iter2
bf16: auto
chat_template: llama3
dataset_prepared_path: null
datasets:
- data_files:
  - a5da60ae8797dfe0_train_data.json
  ds_type: json
  format: custom
  path: /workspace/input_data/a5da60ae8797dfe0_train_data.json
  type:
    field_input: evidence
    field_instruction: claim
    field_output: label
    format: '{instruction} {input}'
    no_input_format: '{instruction}'
    system_format: '{system}'
    system_prompt: ''
debug: null
deepspeed: null
early_stopping_patience: null
eval_max_new_tokens: 128
eval_steps: 25
eval_table_size: null
flash_attention: false
fp16: null
fsdp: null
fsdp_config: null
gradient_accumulation_steps: 8
gradient_checkpointing: true
gradient_clipping: 1.0
group_by_length: true
hub_model_id: dimasik1987/3f060a0c-d67f-4bdf-8681-c858920d364f
hub_repo: null
hub_strategy: checkpoint
hub_token: null
learning_rate: 0.0001
load_in_4bit: false
load_in_8bit: false
local_rank: null
logging_steps: 1
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_memory:
  0: 74GiB
max_steps: 75
micro_batch_size: 2
mlflow_experiment_name: /tmp/a5da60ae8797dfe0_train_data.json
model_type: AutoModelForCausalLM
num_epochs: 3
optim_args:
  adam_beta1: 0.9
  adam_beta2: 0.95
  adam_epsilon: 1e-5
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: 2048
strict: false
tf32: null
tokenizer_type: AutoTokenizer
train_on_inputs: false
trust_remote_code: true
val_set_size: 0.05
wandb_entity: null
wandb_mode: online
wandb_name: 3f060a0c-d67f-4bdf-8681-c858920d364f
wandb_project: Gradients-On-Demand
wandb_run: your_name
wandb_runid: 3f060a0c-d67f-4bdf-8681-c858920d364f
warmup_ratio: 0.05
weight_decay: 0.01
xformers_attention: true

3f060a0c-d67f-4bdf-8681-c858920d364f

This model is a fine-tuned version of UCLA-AGI/Gemma-2-9B-It-SPPO-Iter2 on the None dataset. It achieves the following results on the evaluation set:

Loss: 0.2710

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_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=adam_beta1=0.9,adam_beta2=0.95,adam_epsilon=1e-5
lr_scheduler_type: cosine
lr_scheduler_warmup_steps: 3
training_steps: 75

Training results

Training Loss	Epoch	Step	Validation Loss
10.7012	0.0095	1	9.5650
0.2431	0.2384	25	0.2665
0.2266	0.4768	50	0.2717
0.3255	0.7151	75	0.2710

Framework versions

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