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---
license: other
base_model: Qwen/Qwen1.5-4B
tags:
- generated_from_trainer
datasets:
- tyzhu/lmind_hotpot_train8000_eval7405_v1_doc_qa
metrics:
- accuracy
model-index:
- name: lmind_hotpot_train8000_eval7405_v1_doc_qa_Qwen_Qwen1.5-4B_3e-5_lora2
results:
- task:
name: Causal Language Modeling
type: text-generation
dataset:
name: tyzhu/lmind_hotpot_train8000_eval7405_v1_doc_qa
type: tyzhu/lmind_hotpot_train8000_eval7405_v1_doc_qa
metrics:
- name: Accuracy
type: accuracy
value: 0.5000634920634921
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_doc_qa_Qwen_Qwen1.5-4B_3e-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_doc_qa dataset.
It achieves the following results on the evaluation set:
- Loss: 3.5856
- Accuracy: 0.5001
## 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: 3e-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 |
|:-------------:|:-------:|:-----:|:---------------:|:--------:|
| 1.7766 | 0.9998 | 1089 | 2.3352 | 0.5155 |
| 1.7291 | 1.9995 | 2178 | 2.3047 | 0.5177 |
| 1.6609 | 2.9993 | 3267 | 2.3113 | 0.5187 |
| 1.6108 | 4.0 | 4357 | 2.3487 | 0.518 |
| 1.5489 | 4.9998 | 5446 | 2.3843 | 0.5161 |
| 1.5093 | 5.9995 | 6535 | 2.4637 | 0.5138 |
| 1.4364 | 6.9993 | 7624 | 2.5600 | 0.5108 |
| 1.4038 | 8.0 | 8714 | 2.6166 | 0.5105 |
| 1.3439 | 8.9998 | 9803 | 2.7111 | 0.5092 |
| 1.2928 | 9.9995 | 10892 | 2.8851 | 0.5062 |
| 1.2284 | 10.9993 | 11981 | 2.9595 | 0.5042 |
| 1.2142 | 12.0 | 13071 | 3.0894 | 0.5034 |
| 1.1791 | 12.9998 | 14160 | 3.1358 | 0.5024 |
| 1.1272 | 13.9995 | 15249 | 3.2272 | 0.5017 |
| 1.0934 | 14.9993 | 16338 | 3.3488 | 0.5001 |
| 1.0586 | 16.0 | 17428 | 3.4072 | 0.5015 |
| 1.0415 | 16.9998 | 18517 | 3.4943 | 0.5009 |
| 1.011 | 17.9995 | 19606 | 3.5465 | 0.5001 |
| 1.0036 | 18.9993 | 20695 | 3.5320 | 0.5011 |
| 0.9595 | 19.9954 | 21780 | 3.5856 | 0.5001 |
### 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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