metadata
license: other
base_model: Qwen/Qwen1.5-4B
tags:
- generated_from_trainer
datasets:
- tyzhu/lmind_hotpot_train8000_eval7405_v1_qa
metrics:
- accuracy
model-index:
- name: lmind_hotpot_train8000_eval7405_v1_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_qa
type: tyzhu/lmind_hotpot_train8000_eval7405_v1_qa
metrics:
- name: Accuracy
type: accuracy
value: 0.49165079365079367
library_name: peft
lmind_hotpot_train8000_eval7405_v1_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_qa dataset. It achieves the following results on the evaluation set:
- Loss: 3.6823
- Accuracy: 0.4917
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 |
|---|---|---|---|---|
| 2.248 | 1.0 | 250 | 2.3110 | 0.5173 |
| 1.9103 | 2.0 | 500 | 2.3740 | 0.5157 |
| 1.4896 | 3.0 | 750 | 2.5266 | 0.5112 |
| 1.109 | 4.0 | 1000 | 2.7830 | 0.5037 |
| 0.7757 | 5.0 | 1250 | 3.0311 | 0.4987 |
| 0.5994 | 6.0 | 1500 | 3.2256 | 0.4979 |
| 0.4921 | 7.0 | 1750 | 3.3517 | 0.4958 |
| 0.4575 | 8.0 | 2000 | 3.4321 | 0.4946 |
| 0.4233 | 9.0 | 2250 | 3.5151 | 0.4961 |
| 0.4178 | 10.0 | 2500 | 3.5280 | 0.4950 |
| 0.3987 | 11.0 | 2750 | 3.5547 | 0.4951 |
| 0.4033 | 12.0 | 3000 | 3.5601 | 0.4954 |
| 0.3932 | 13.0 | 3250 | 3.5859 | 0.4932 |
| 0.4012 | 14.0 | 3500 | 3.5944 | 0.4927 |
| 0.3895 | 15.0 | 3750 | 3.6038 | 0.4939 |
| 0.396 | 16.0 | 4000 | 3.6504 | 0.4932 |
| 0.3847 | 17.0 | 4250 | 3.6602 | 0.4912 |
| 0.3942 | 18.0 | 4500 | 3.6515 | 0.4914 |
| 0.3809 | 19.0 | 4750 | 3.7304 | 0.4923 |
| 0.3805 | 20.0 | 5000 | 3.6823 | 0.4917 |
Framework versions
- PEFT 0.5.0
- Transformers 4.40.2
- Pytorch 2.3.0
- Datasets 2.19.1
- Tokenizers 0.19.1