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Alphatao/
2c999454-0879-43f4-a58c-d7b155c920d3

Name: 2c999454-0879-43f4-a58c-d7b155c920d3
Availability: InStock

generated_from_trainer

Qwen/Qwen2-0.5B

adapter

apache-2.0

Views

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See axolotl config

axolotl version: 0.4.1

yaml

adapter: lora
base_model: Qwen/Qwen2-0.5B
bf16: true
chat_template: llama3
dataset_prepared_path: null
datasets:
- data_files:
  - 5a4e9dcecea0e123_train_data.json
  ds_type: json
  format: custom
  path: /workspace/input_data/5a4e9dcecea0e123_train_data.json
  type:
    field_instruction: prompt
    field_output: question
    format: '{instruction}'
    no_input_format: '{instruction}'
    system_format: '{system}'
    system_prompt: ''
debug: null
device_map:
  ? ''
  : 0,1,2,3,4,5,6,7
early_stopping_patience: 2
eval_max_new_tokens: 128
eval_steps: 100
eval_table_size: null
flash_attention: true
gradient_accumulation_steps: 8
gradient_checkpointing: true
group_by_length: false
hub_model_id: Alphatao/2c999454-0879-43f4-a58c-d7b155c920d3
hub_repo: null
hub_strategy: null
hub_token: null
learning_rate: 0.0002
load_best_model_at_end: true
load_in_4bit: false
load_in_8bit: false
local_rank: null
logging_steps: 1
lora_alpha: 128
lora_dropout: 0.3
lora_fan_in_fan_out: null
lora_model_dir: null
lora_r: 64
lora_target_linear: true
lr_scheduler: cosine
max_grad_norm: 1.0
max_steps: 4224
micro_batch_size: 4
mlflow_experiment_name: /tmp/5a4e9dcecea0e123_train_data.json
model_type: AutoModelForCausalLM
num_epochs: 2
optimizer: adamw_bnb_8bit
output_dir: miner_id_24
pad_to_sequence_len: true
resume_from_checkpoint: null
s2_attention: null
sample_packing: false
save_steps: 100
sequence_len: 1024
strict: false
tf32: true
tokenizer_type: AutoTokenizer
train_on_inputs: false
trust_remote_code: true
val_set_size: 0.04
wandb_entity: null
wandb_mode: online
wandb_name: c29f8be0-1d6a-40dd-83f1-f1d58697725a
wandb_project: Gradients-On-Demand
wandb_run: your_name
wandb_runid: c29f8be0-1d6a-40dd-83f1-f1d58697725a
warmup_steps: 10
weight_decay: 0.0
xformers_attention: null

2c999454-0879-43f4-a58c-d7b155c920d3

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

Loss: 0.0051

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: 4
eval_batch_size: 4
seed: 42
gradient_accumulation_steps: 8
total_train_batch_size: 32
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: 10
training_steps: 2619

Training results

Training Loss	Epoch	Step	Validation Loss
0.2524	0.0008	1	0.4547
0.0013	0.0764	100	0.0036
0.0008	0.1527	200	0.0037
0.0035	0.2291	300	0.0026
0.0026	0.3055	400	0.0027
0.0008	0.3819	500	0.0026
0.0058	0.4582	600	0.0026
0.0015	0.5346	700	0.0051

Framework versions

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