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diaenra/
6d762c66-b841-4dba-92e3-ceddc0b67beb

Name: 6d762c66-b841-4dba-92e3-ceddc0b67beb
Availability: InStock

generated_from_trainer

Qwen/Qwen2.5-0.5B

adapter

apache-2.0

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See axolotl config

axolotl version: 0.4.1

yaml

adapter: lora
base_model: Qwen/Qwen2.5-0.5B
bf16: auto
chat_template: llama3
dataset_prepared_path: null
datasets:
- data_files:
  - 10700d782456e2b9_train_data.json
  ds_type: json
  field: text
  path: /workspace/input_data/10700d782456e2b9_train_data.json
  type: completion
debug: null
deepspeed: null
early_stopping_patience: 1
eval_max_new_tokens: 128
eval_steps: 25
eval_table_size: null
flash_attention: false
fp16: false
fsdp: null
fsdp_config: null
gradient_accumulation_steps: 4
gradient_checkpointing: true
group_by_length: true
hub_model_id: diaenra/6d762c66-b841-4dba-92e3-ceddc0b67beb
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: 32
lora_dropout: 0.05
lora_fan_in_fan_out: null
lora_model_dir: null
lora_r: 16
lora_target_linear: true
lr_scheduler: cosine
max_steps: 100
micro_batch_size: 2
mlflow_experiment_name: /tmp/10700d782456e2b9_train_data.json
model_type: AutoModelForCausalLM
num_epochs: 1
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
sequence_len: 4056
strict: false
tf32: false
tokenizer_type: AutoTokenizer
train_on_inputs: false
trust_remote_code: true
val_set_size: 0.05
wandb_entity: diaenra-tao-miner
wandb_mode: online
wandb_name: 6d762c66-b841-4dba-92e3-ceddc0b67beb
wandb_project: tao
wandb_run: diaenra
wandb_runid: 6d762c66-b841-4dba-92e3-ceddc0b67beb
warmup_ratio: 0.05
weight_decay: 0.01
xformers_attention: true

6d762c66-b841-4dba-92e3-ceddc0b67beb

This model is a fine-tuned version of Qwen/Qwen2.5-0.5B on the None dataset. It achieves the following results on the evaluation set:

Loss: 1.3437

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: 2
eval_batch_size: 2
seed: 42
gradient_accumulation_steps: 4
total_train_batch_size: 8
optimizer: Use OptimizerNames.ADAMW_TORCH 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: 5
training_steps: 100

Training results

Training Loss	Epoch	Step	Validation Loss
3.0343	0.0000	1	3.3438
0.8531	0.0004	25	1.3278
0.8106	0.0008	50	1.3437

Framework versions

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