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---
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
---
<!-- 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_reciteonly_qa_Qwen_Qwen1.5-4B_5e-5_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_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
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