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dada22231/
7af86c7a-d8b4-4095-a5be-2cdbf598f16c

Name: 7af86c7a-d8b4-4095-a5be-2cdbf598f16c
Availability: InStock

generated_from_trainer

katuni4ka/tiny-random-qwen1.5-moe

adapter

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See axolotl config

axolotl version: 0.4.1

yaml

adapter: lora
base_model: katuni4ka/tiny-random-qwen1.5-moe
bf16: true
chat_template: llama3
cosine_min_lr_ratio: 0.1
data_processes: 16
dataset_prepared_path: null
datasets:
- data_files:
  - 3eb43ecc97e35e49_train_data.json
  ds_type: json
  format: custom
  path: /workspace/input_data/3eb43ecc97e35e49_train_data.json
  type:
    field_input: selftext
    field_instruction: title
    field_output: document
    format: '{instruction} {input}'
    no_input_format: '{instruction}'
    system_format: '{system}'
    system_prompt: ''
ddp_bucket_cap_mb: 25
ddp_find_unused_parameters: false
debug: null
deepspeed: null
device_map: auto
do_eval: true
eval_batch_size: 1
eval_sample_packing: false
eval_steps: 25
evaluation_strategy: steps
flash_attention: true
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: dada22231/7af86c7a-d8b4-4095-a5be-2cdbf598f16c
hub_strategy: checkpoint
hub_token: null
hub_username: dada22231
learning_rate: 0.0001
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
lora_target_modules:
- q_proj
- v_proj
lr_scheduler: cosine
max_grad_norm: 1.0
max_memory:
  '0': 75GiB
  '1': 75GiB
  '2': 75GiB
  '3': 75GiB
max_steps: 50
micro_batch_size: 4
mlflow_experiment_name: null
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
repository_id: dada22231/7af86c7a-d8b4-4095-a5be-2cdbf598f16c
resume_from_checkpoint: null
s2_attention: null
sample_packing: false
save_steps: 25
save_strategy: steps
sequence_len: 2048
strict: false
tf32: true
tokenizer_type: AutoTokenizer
torch_compile: false
train_on_inputs: false
trust_remote_code: true
val_set_size: 50
wandb_entity: null
wandb_mode: online
wandb_name: 7af86c7a-d8b4-4095-a5be-2cdbf598f16c
wandb_project: Public_TuningSN
wandb_runid: 7af86c7a-d8b4-4095-a5be-2cdbf598f16c
warmup_ratio: 0.03
weight_decay: 0.01
xformers_attention: null

7af86c7a-d8b4-4095-a5be-2cdbf598f16c

This model is a fine-tuned version of katuni4ka/tiny-random-qwen1.5-moe 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: 0.0001
train_batch_size: 4
eval_batch_size: 1
seed: 42
distributed_type: multi-GPU
num_devices: 4
gradient_accumulation_steps: 8
total_train_batch_size: 128
total_eval_batch_size: 4
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: 2
training_steps: 50

Training results

Epoch	Step	Validation Loss
0.0006	1	nan
0.0140	25	nan
0.0280	50	nan

Framework versions

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