axolotl version: 0.6.0
base_model: Qwen/Qwen2.5-7B-Instruct
trust_remote_code: true
model_type: AutoModelForCausalLM
tokenizer_type: AutoTokenizer
load_in_8bit:
load_in_4bit:
strict: false
datasets:
- path: medalpaca/medical_meadow_medqa
type: alpaca
dataset_prepared_path:
val_set_size: 0.1
output_dir: ./sft-qwen25
sequence_len: 8192
sample_packing: true
eval_sample_packing: true
pad_to_sequence_len: true
wandb_project: sft-qwen-25-7b-instruct
wandb_entity:
wandb_watch:
wandb_name:
wandb_log_model:
gradient_accumulation_steps: 1
micro_batch_size: 1
num_epochs: 1
optimizer: adamw_torch
lr_scheduler: cosine
learning_rate: 0.00001
train_on_inputs: false
group_by_length: false
bf16: true
fp16: false
tf32: false
gradient_checkpointing: true
logging_steps: 1
xformers_attention:
flash_attention: true
warmup_steps:
eval_steps: 10
save_steps: 40
evals_per_epoch:
saves_per_epoch:
debug:
deepspeed: deepspeed_configs/zero2.json
weight_decay:
fsdp:
fsdp_config:
special_tokens:
hub_model_id: neginashz/sft-qwen-25-7b-instruct
hub_strategy: all_checkpoints
early_stopping_patience: 3
auto_resume_from_checkpoints: true
sft-qwen-25-7b-instruct
This model is a fine-tuned version of Qwen/Qwen2.5-7B-Instruct on the medalpaca/medical_meadow_medqa dataset. It achieves the following results on the evaluation set:
- Loss: 0.1055
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: 1e-05
- train_batch_size: 1
- eval_batch_size: 1
- seed: 42
- distributed_type: multi-GPU
- num_devices: 4
- total_train_batch_size: 4
- total_eval_batch_size: 4
- optimizer: Use 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: 2
- num_epochs: 1
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| 0.1381 | 0.1235 | 10 | 0.1342 |
| 0.1495 | 0.2469 | 20 | 0.1229 |
| 0.1215 | 0.3704 | 30 | 0.1246 |
| 0.1354 | 0.4938 | 40 | 0.1175 |
| 0.1223 | 0.6173 | 50 | 0.1115 |
| 0.1068 | 0.7407 | 60 | 0.1101 |
| 0.1061 | 0.8642 | 70 | 0.1056 |
| 0.118 | 0.9877 | 80 | 0.1055 |
Framework versions
- Transformers 4.47.0
- Pytorch 2.5.1+cu124
- Datasets 3.1.0
- Tokenizers 0.21.0