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cimol/
8ce79bc8-fa9d-48b8-8d1a-21b46a2e5dd4

Name: 8ce79bc8-fa9d-48b8-8d1a-21b46a2e5dd4
Availability: InStock

generated_from_trainer

peft-internal-testing/tiny-dummy-qwen2

adapter

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See axolotl config

axolotl version: 0.4.1

yaml

adapter: lora
base_model: peft-internal-testing/tiny-dummy-qwen2
bf16: true
chat_template: llama3
data_processes: 56
dataset_prepared_path: null
datasets:
- data_files:
  - 2a7b266cd8acd573_train_data.json
  ds_type: json
  format: custom
  path: /workspace/input_data/2a7b266cd8acd573_train_data.json
  type:
    field_input: rewritten_intent
    field_instruction: intent
    field_output: snippet
    format: '{instruction} {input}'
    no_input_format: '{instruction}'
    system_format: '{system}'
    system_prompt: ''
debug: null
deepspeed: null
device_map: auto
distributed_training:
  multi_gpu: true
  num_gpus: 2
do_eval: true
early_stopping_patience: 4
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:
- full_shard
- cpu_offload
fsdp_config:
  cpu_offload: true
  mixed_precision: true
  sharding_strategy: FULL_SHARD
gradient_accumulation_steps: 8
gradient_checkpointing: true
group_by_length: true
hub_model_id: cimol/8ce79bc8-fa9d-48b8-8d1a-21b46a2e5dd4
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: 5
lora_alpha: 64
lora_dropout: 0.04
lora_fan_in_fan_out: null
lora_model_dir: null
lora_r: 32
lora_target_linear: true
lr_scheduler: cosine
lr_scheduler_warmup_steps: 50
max_grad_norm: 1.0
max_steps: 2850
micro_batch_size: 4
mlflow_experiment_name: /tmp/2a7b266cd8acd573_train_data.json
model_type: AutoModelForCausalLM
num_epochs: 3
optim_args:
  adam_beta1: 0.9
  adam_beta2: 0.95
  adam_epsilon: 1e-8
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: 150
saves_per_epoch: null
seed: 17333
sequence_len: 1024
strict: false
tf32: true
tokenizer_type: AutoTokenizer
total_train_batch_size: 16
train_batch_size: 16
train_on_inputs: false
trust_remote_code: true
val_set_size: 0.05
wandb_entity: null
wandb_mode: online
wandb_name: e7b05959-b444-478d-8a3c-da5ab286ceae
wandb_project: Gradients-On-Demand
wandb_run: your_name
wandb_runid: e7b05959-b444-478d-8a3c-da5ab286ceae
warmup_steps: 100
weight_decay: 0.0
xformers_attention: null

8ce79bc8-fa9d-48b8-8d1a-21b46a2e5dd4

This model is a fine-tuned version of peft-internal-testing/tiny-dummy-qwen2 on the None dataset.

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: 4
eval_batch_size: 8
seed: 17333
distributed_type: multi-GPU
num_devices: 2
gradient_accumulation_steps: 8
total_train_batch_size: 64
total_eval_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-8
lr_scheduler_type: cosine
lr_scheduler_warmup_steps: 100
training_steps: 120

Training results

Training Loss	Epoch	Step	Validation Loss
No log	0.0251	1	11.9324

Framework versions

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