File size: 2,602 Bytes
78ec215 5d5ef3b 78ec215 5d5ef3b 78ec215 5d5ef3b 78ec215 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 |
---
license: other
base_model: Qwen/Qwen1.5-4B
tags:
- generated_from_trainer
datasets:
- tyzhu/lmind_hotpot_train8000_eval7405_v1_docidx
metrics:
- accuracy
model-index:
- name: lmind_hotpot_train8000_eval7405_v1_docidx_Qwen_Qwen1.5-4B_lora2
results:
- task:
name: Causal Language Modeling
type: text-generation
dataset:
name: tyzhu/lmind_hotpot_train8000_eval7405_v1_docidx
type: tyzhu/lmind_hotpot_train8000_eval7405_v1_docidx
metrics:
- name: Accuracy
type: accuracy
value: 0.7691922246220302
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_docidx_Qwen_Qwen1.5-4B_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_docidx dataset.
It achieves the following results on the evaluation set:
- Loss: 1.0623
- Accuracy: 0.7692
## 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: 0.0001
- 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: 10.0
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:------:|:----:|:---------------:|:--------:|
| 1.6067 | 0.9997 | 839 | 1.8440 | 0.7197 |
| 1.5433 | 1.9994 | 1678 | 1.7661 | 0.7247 |
| 1.4167 | 2.9991 | 2517 | 1.6455 | 0.7310 |
| 1.2948 | 4.0 | 3357 | 1.5394 | 0.7366 |
| 1.1715 | 4.9997 | 4196 | 1.4463 | 0.7422 |
| 1.0458 | 5.9994 | 5035 | 1.3537 | 0.7484 |
| 0.9357 | 6.9991 | 5874 | 1.2456 | 0.7546 |
| 0.8269 | 8.0 | 6714 | 1.1735 | 0.7598 |
| 0.7262 | 8.9997 | 7553 | 1.0966 | 0.7649 |
| 0.6381 | 9.9970 | 8390 | 1.0623 | 0.7692 |
### Framework versions
- PEFT 0.5.0
- Transformers 4.40.2
- Pytorch 2.3.0
- Datasets 2.19.1
- Tokenizers 0.19.1
|