metadata

license: other
base_model: Qwen/Qwen1.5-4B
tags:
  - generated_from_trainer
datasets:
  - tyzhu/lmind_hotpot_train8000_eval7405_v1_reciteonly_qa
metrics:
  - accuracy
model-index:
  - name: >-
      lmind_hotpot_train8000_eval7405_v1_reciteonly_qa_Qwen_Qwen1.5-4B_3e-4_lora2
    results:
      - task:
          name: Causal Language Modeling
          type: text-generation
        dataset:
          name: tyzhu/lmind_hotpot_train8000_eval7405_v1_reciteonly_qa
          type: tyzhu/lmind_hotpot_train8000_eval7405_v1_reciteonly_qa
        metrics:
          - name: Accuracy
            type: accuracy
            value: 0.6608966521106259
library_name: peft

lmind_hotpot_train8000_eval7405_v1_reciteonly_qa_Qwen_Qwen1.5-4B_3e-4_lora2

This model is a fine-tuned version of Qwen/Qwen1.5-4B on the tyzhu/lmind_hotpot_train8000_eval7405_v1_reciteonly_qa dataset. It achieves the following results on the evaluation set:

Loss: 2.7813
Accuracy: 0.6609

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: 0.0003
train_batch_size: 1
eval_batch_size: 2
seed: 42
distributed_type: multi-GPU
num_devices: 4
gradient_accumulation_steps: 8
total_train_batch_size: 32
total_eval_batch_size: 8
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: constant
lr_scheduler_warmup_ratio: 0.05
num_epochs: 20.0

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
1.4488	1.0	250	1.4958	0.6770
1.3142	2.0	500	1.5007	0.6772
1.1176	3.0	750	1.5507	0.6756
0.9253	4.0	1000	1.6442	0.6728
0.7213	5.0	1250	1.7736	0.6701
0.5718	6.0	1500	1.8863	0.6682
0.4232	7.0	1750	2.0245	0.6660
0.3334	8.0	2000	2.1773	0.6642
0.2433	9.0	2250	2.2681	0.6632
0.2076	10.0	2500	2.3732	0.6629
0.1632	11.0	2750	2.4368	0.6623
0.1491	12.0	3000	2.5182	0.6617
0.1275	13.0	3250	2.5680	0.6619
0.1273	14.0	3500	2.6412	0.6613
0.1129	15.0	3750	2.6497	0.6617
0.1129	16.0	4000	2.6932	0.6614
0.102	17.0	4250	2.7003	0.6612
0.1109	18.0	4500	2.7033	0.6614
0.0997	19.0	4750	2.7139	0.6613
0.1012	20.0	5000	2.7813	0.6609

Framework versions

PEFT 0.5.0
Transformers 4.40.2
Pytorch 2.3.0
Datasets 2.19.1
Tokenizers 0.19.1