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---
license: other
base_model: Qwen/Qwen1.5-4B
tags:
- generated_from_trainer
datasets:
- tyzhu/lmind_nq_train6000_eval6489_v1_docidx_v3
metrics:
- accuracy
model-index:
- name: lmind_nq_train6000_eval6489_v1_docidx_v3_Qwen_Qwen1.5-4B_5e-5_lora2
results:
- task:
name: Causal Language Modeling
type: text-generation
dataset:
name: tyzhu/lmind_nq_train6000_eval6489_v1_docidx_v3
type: tyzhu/lmind_nq_train6000_eval6489_v1_docidx_v3
metrics:
- name: Accuracy
type: accuracy
value: 0.42728205128205127
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_nq_train6000_eval6489_v1_docidx_v3_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_nq_train6000_eval6489_v1_docidx_v3 dataset.
It achieves the following results on the evaluation set:
- Loss: 5.0355
- Accuracy: 0.4273
## 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 |
|:-------------:|:-------:|:----:|:---------------:|:--------:|
| 1.9626 | 0.9985 | 341 | 2.9919 | 0.4727 |
| 1.9158 | 2.0 | 683 | 2.9864 | 0.4737 |
| 1.8622 | 2.9985 | 1024 | 3.0420 | 0.4710 |
| 1.786 | 4.0 | 1366 | 3.1527 | 0.4662 |
| 1.7019 | 4.9985 | 1707 | 3.3819 | 0.4634 |
| 1.6036 | 6.0 | 2049 | 3.4969 | 0.4589 |
| 1.5175 | 6.9985 | 2390 | 3.6412 | 0.4577 |
| 1.4007 | 8.0 | 2732 | 3.8310 | 0.4537 |
| 1.326 | 8.9985 | 3073 | 3.9177 | 0.4487 |
| 1.231 | 10.0 | 3415 | 4.0665 | 0.4451 |
| 1.1298 | 10.9985 | 3756 | 4.1773 | 0.44 |
| 1.0276 | 12.0 | 4098 | 4.2875 | 0.4378 |
| 0.9525 | 12.9985 | 4439 | 4.4273 | 0.4352 |
| 0.8616 | 14.0 | 4781 | 4.4484 | 0.4324 |
| 0.7799 | 14.9985 | 5122 | 4.6228 | 0.4313 |
| 0.7084 | 16.0 | 5464 | 4.7239 | 0.4303 |
| 0.6478 | 16.9985 | 5805 | 4.8167 | 0.4310 |
| 0.5862 | 18.0 | 6147 | 4.8510 | 0.4303 |
| 0.5189 | 18.9985 | 6488 | 4.9265 | 0.4243 |
| 0.4767 | 19.9707 | 6820 | 5.0355 | 0.4273 |
### 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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