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---
license: other
base_model: Qwen/Qwen1.5-4B
tags:
- generated_from_trainer
datasets:
- tyzhu/lmind_hotpot_train8000_eval7405_v1_recite_qa
metrics:
- accuracy
model-index:
- name: lmind_hotpot_train8000_eval7405_v1_recite_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_recite_qa
type: tyzhu/lmind_hotpot_train8000_eval7405_v1_recite_qa
metrics:
- name: Accuracy
type: accuracy
value: 0.7763580786026201
library_name: peft
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# lmind_hotpot_train8000_eval7405_v1_recite_qa_Qwen_Qwen1.5-4B_3e-4_lora2
This model is a fine-tuned version of [Qwen/Qwen1.5-4B](https://huggingface.co/Qwen/Qwen1.5-4B) on the tyzhu/lmind_hotpot_train8000_eval7405_v1_recite_qa dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4756
- Accuracy: 0.7764
## 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 |
|:-------------:|:-------:|:-----:|:---------------:|:--------:|
| 1.5356 | 0.9998 | 1089 | 1.3711 | 0.6864 |
| 1.3102 | 1.9995 | 2178 | 1.1753 | 0.7020 |
| 1.0549 | 2.9993 | 3267 | 1.0095 | 0.7164 |
| 0.8461 | 4.0 | 4357 | 0.8722 | 0.7297 |
| 0.701 | 4.9998 | 5446 | 0.7641 | 0.7406 |
| 0.5977 | 5.9995 | 6535 | 0.6797 | 0.7490 |
| 0.5238 | 6.9993 | 7624 | 0.6209 | 0.7559 |
| 0.4742 | 8.0 | 8714 | 0.5837 | 0.7600 |
| 0.438 | 8.9998 | 9803 | 0.5532 | 0.7638 |
| 0.402 | 9.9995 | 10892 | 0.5331 | 0.7664 |
| 0.383 | 10.9993 | 11981 | 0.5156 | 0.7685 |
| 0.3627 | 12.0 | 13071 | 0.5070 | 0.7702 |
| 0.3521 | 12.9998 | 14160 | 0.4984 | 0.7714 |
| 0.344 | 13.9995 | 15249 | 0.4925 | 0.7722 |
| 0.3341 | 14.9993 | 16338 | 0.4847 | 0.7736 |
| 0.3275 | 16.0 | 17428 | 0.4808 | 0.7748 |
| 0.3223 | 16.9998 | 18517 | 0.4776 | 0.7751 |
| 0.3155 | 17.9995 | 19606 | 0.4804 | 0.7758 |
| 0.3033 | 18.9993 | 20695 | 0.4787 | 0.7761 |
| 0.2989 | 19.9954 | 21780 | 0.4756 | 0.7764 |
### Framework versions
- PEFT 0.5.0
- Transformers 4.40.2
- Pytorch 2.3.0
- Datasets 2.19.1
- Tokenizers 0.19.1
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