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_5e-5_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: 0.6606986899563319
library_name: peft

lmind_hotpot_train8000_eval7405_v1_reciteonly_qa_Qwen_Qwen1.5-4B_5e-5_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: 2.2235
Accuracy: 0.6607

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: 5e-05
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
1.4675	1.0	250	1.5235	0.6752
1.435	2.0	500	1.5122	0.6762
1.395	3.0	750	1.5092	0.6761
1.35	4.0	1000	1.5165	0.6761
1.2906	5.0	1250	1.5309	0.6754
1.2411	6.0	1500	1.5509	0.6747
1.1833	7.0	1750	1.5747	0.6737
1.1198	8.0	2000	1.6129	0.6727
1.0498	9.0	2250	1.6407	0.6717
1.0063	10.0	2500	1.6802	0.6706
0.943	11.0	2750	1.7385	0.6691
0.8881	12.0	3000	1.7767	0.6681
0.8176	13.0	3250	1.8362	0.6669
0.7669	14.0	3500	1.8820	0.6659
0.7119	15.0	3750	1.9359	0.6648
0.6564	16.0	4000	2.0029	0.6638
0.6096	17.0	4250	2.0593	0.6631
0.5715	18.0	4500	2.1331	0.6621
0.5293	19.0	4750	2.1593	0.6617
0.4956	20.0	5000	2.2235	0.6607

Framework versions

PEFT 0.5.0
Transformers 4.40.2
Pytorch 2.3.0
Datasets 2.19.1
Tokenizers 0.19.1