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lora-qwen-25-7b-instruct

PEFT
Safetensors
qwen2
axolotl
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metadata
library_name: peft
license: apache-2.0
base_model: Qwen/Qwen2.5-7B-Instruct
tags:
  - axolotl
  - generated_from_trainer
datasets:
  - medalpaca/medical_meadow_medqa
model-index:
  - name: lora-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: ./lora-qwen25

sequence_len: 8192
sample_packing: true
eval_sample_packing: true
pad_to_sequence_len: true


adapter: lora
lora_r: 256
lora_alpha: 128
lora_dropout: 0.05
#lora_target_modules:
#  - q_proj
#  - v_proj
#  - k_proj
#  - o_proj
#  - gate_proj
#  - down_proj
#  - up_proj
lora_target_linear: true

wandb_project: lora-qwen-25-7b-instruct
wandb_entity: 
wandb_watch:
wandb_name: 
wandb_log_model: 

gradient_accumulation_steps: 1
micro_batch_size: 1
num_epochs: 3
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: 
save_steps:

evals_per_epoch: 16
saves_per_epoch: 2

debug:
deepspeed: deepspeed_configs/zero2.json
weight_decay:
fsdp:
fsdp_config:
special_tokens:

hub_model_id: neginashz/lora-qwen-25-7b-instruct
hub_strategy: 
early_stopping_patience:

resume_from_checkpoint:
auto_resume_from_checkpoints: true

lora-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: 0.1181

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: 7
  • num_epochs: 3

Training results

Training Loss Epoch Step Validation Loss
2.774 0.0741 6 2.5571
1.4649 0.1481 12 1.3144
0.649 0.2222 18 0.4603
0.1557 0.2963 24 0.1620
0.1792 0.3704 30 0.1539
0.1432 0.4444 36 0.1422
0.1393 0.5185 42 0.1385
0.1137 0.5926 48 0.1340
0.1246 0.6667 54 0.1317
0.1235 0.7407 60 0.1313
0.123 0.8148 66 0.1293
0.1413 0.8889 72 0.1277
0.1338 0.9630 78 0.1268
0.1093 1.0247 84 0.1263
0.1442 1.0988 90 0.1265
0.1127 1.1728 96 0.1244
0.137 1.2469 102 0.1231
0.1098 1.3210 108 0.1224
0.1276 1.3951 114 0.1223
0.102 1.4691 120 0.1215
0.1208 1.5432 126 0.1217
0.1143 1.6173 132 0.1211
0.1315 1.6914 138 0.1204
0.1166 1.7654 144 0.1200
0.1055 1.8395 150 0.1200
0.1235 1.9136 156 0.1194
0.12 1.9877 162 0.1193
0.0982 2.0494 168 0.1193
0.1129 2.1235 174 0.1188
0.1094 2.1975 180 0.1190
0.1216 2.2716 186 0.1191
0.1387 2.3457 192 0.1187
0.1001 2.4198 198 0.1184
0.1031 2.4938 204 0.1185
0.0818 2.5679 210 0.1183
0.126 2.6420 216 0.1185
0.124 2.7160 222 0.1183
0.1193 2.7901 228 0.1184
0.1082 2.8642 234 0.1183
0.1181 2.9383 240 0.1181

Framework versions

  • PEFT 0.14.0
  • Transformers 4.47.0
  • Pytorch 2.5.1+cu124
  • Datasets 3.1.0
  • Tokenizers 0.21.0