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souging/
ceff681a-227a-41eb-8859-e9f216e8b62a

Name: ceff681a-227a-41eb-8859-e9f216e8b62a
Availability: InStock

generated_from_trainer

Qwen/Qwen2.5-14B-Instruct

adapter

apache-2.0

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See axolotl config

axolotl version: 0.4.1

yaml

adapter: lora
base_model: Qwen/Qwen2.5-14B-Instruct
bf16: auto
dataset_prepared_path: null
datasets:
- data_files:
  - a8b0ed61da0de5f0_train_data.json
  ds_type: json
  format: custom
  path: /root/G.O.D-test/core/data/a8b0ed61da0de5f0_train_data.json
  type:
    field_instruction: input
    field_output: output
    format: '{instruction}'
    no_input_format: '{instruction}'
    system_format: '{system}'
    system_prompt: ''
debug: null
deepspeed: null
early_stopping_patience: null
eval_max_new_tokens: 128
eval_table_size: null
evals_per_epoch: 0
flash_attention: false
fp16: null
fsdp: null
fsdp_config: null
gradient_accumulation_steps: 4
gradient_checkpointing: false
group_by_length: false
hub_model_id: souging/ceff681a-227a-41eb-8859-e9f216e8b62a
hub_repo: null
hub_strategy: checkpoint
hub_token: null
learning_rate: 0.0002
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_steps: 600
micro_batch_size: 1
mlflow_experiment_name: /tmp/a8b0ed61da0de5f0_train_data.json
model_type: AutoModelForCausalLM
num_epochs: 4
optimizer: adamw_bnb_8bit
output_dir: miner_id_24
pad_to_sequence_len: true
resume_from_checkpoint: null
s2_attention: null
sample_packing: false
saves_per_epoch: 0
sequence_len: 1024
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: 9bfbc55a-edfa-43e3-92c7-2fcabe5e50f2
wandb_project: Gradients-On-Demand
wandb_run: your_name
wandb_runid: 9bfbc55a-edfa-43e3-92c7-2fcabe5e50f2
warmup_steps: 100
weight_decay: 0.01
xformers_attention: null

ceff681a-227a-41eb-8859-e9f216e8b62a

This model is a fine-tuned version of Qwen/Qwen2.5-14B-Instruct on the None dataset. It achieves the following results on the evaluation set:

Loss: 1.3767

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: 1
eval_batch_size: 1
seed: 42
distributed_type: multi-GPU
num_devices: 8
gradient_accumulation_steps: 4
total_train_batch_size: 32
total_eval_batch_size: 8
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: 100
training_steps: 600

Training results

Training Loss	Epoch	Step	Validation Loss
1.446	0.2208	600	1.3767

Framework versions

PEFT 0.13.2
Transformers 4.46.0
Pytorch 2.5.0+cu124
Datasets 3.0.1
Tokenizers 0.20.3