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aseratus1/
55f630c7-2b1d-407f-92dc-cafc4acff6a6

Name: 55f630c7-2b1d-407f-92dc-cafc4acff6a6
Availability: InStock

generated_from_trainer

Qwen/Qwen2-0.5B-Instruct

adapter

apache-2.0

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See axolotl config

axolotl version: 0.4.1

yaml

adapter: lora
base_model: Qwen/Qwen2-0.5B-Instruct
bf16: true
chat_template: llama3
dataset_prepared_path: null
datasets:
- data_files:
  - 189db5af0d7d55a2_train_data.json
  ds_type: json
  format: custom
  path: /workspace/input_data/189db5af0d7d55a2_train_data.json
  type:
    field_instruction: prompt
    field_output: model_completion
    format: '{instruction}'
    no_input_format: '{instruction}'
    system_format: '{system}'
    system_prompt: ''
debug: null
deepspeed: null
device_map: auto
do_eval: true
early_stopping_patience: 5
eval_batch_size: 4
eval_max_new_tokens: 128
eval_steps: 50
eval_table_size: null
evals_per_epoch: null
flash_attention: true
fp16: false
fsdp: null
fsdp_config: null
gradient_accumulation_steps: 4
gradient_checkpointing: true
group_by_length: true
hub_model_id: aseratus1/55f630c7-2b1d-407f-92dc-cafc4acff6a6
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: 1
lora_alpha: 128
lora_dropout: 0.05
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_memory:
  0: 75GB
max_steps: 400
micro_batch_size: 8
mlflow_experiment_name: /tmp/189db5af0d7d55a2_train_data.json
model_type: AutoModelForCausalLM
num_epochs: 3
optim_args:
  adam_beta1: 0.9
  adam_beta2: 0.95
  adam_epsilon: 1e-5
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: 50
saves_per_epoch: null
sequence_len: 1024
strict: false
tf32: true
tokenizer_type: AutoTokenizer
train_on_inputs: false
trust_remote_code: true
val_set_size: 0.05
wandb_entity: null
wandb_mode: online
wandb_name: 9412c595-3b5f-4f4f-969f-81e19cd816f7
wandb_project: Gradients-On-Demand
wandb_run: your_name
wandb_runid: 9412c595-3b5f-4f4f-969f-81e19cd816f7
warmup_steps: 10
weight_decay: 0.0
xformers_attention: null

55f630c7-2b1d-407f-92dc-cafc4acff6a6

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

Loss: 0.9492

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: 8
eval_batch_size: 4
seed: 42
gradient_accumulation_steps: 4
total_train_batch_size: 32
optimizer: Use OptimizerNames.ADAMW_BNB 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: 10
training_steps: 400

Training results

Training Loss	Epoch	Step	Validation Loss
0.7999	0.0003	1	1.4948
0.679	0.0138	50	1.0154
0.7837	0.0276	100	0.9882
0.8018	0.0414	150	0.9711
0.8071	0.0552	200	0.9636
0.7543	0.0690	250	0.9541
0.5534	0.0828	300	0.9506
0.7053	0.0966	350	0.9493
0.7236	0.1104	400	0.9492

Framework versions

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