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sft-qwen-25-7b-instruct

Text Generation
Transformers
PyTorch
Safetensors
qwen2
axolotl
Generated from Trainer
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text-generation-inference
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sft-qwen-25-7b-instruct / README.md
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metadata
library_name: transformers
license: apache-2.0
base_model: Qwen/Qwen2.5-7B-Instruct
tags:
  - axolotl
  - generated_from_trainer
datasets:
  - medalpaca/medical_meadow_medqa
model-index:
  - name: sft-qwen-25-7b-instruct
    results: []

Built with Axolotl

See axolotl config

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