File size: 3,254 Bytes
5d71734 103a7e9 5d71734 103a7e9 5d71734 103a7e9 5d71734 |
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 91 92 93 94 95 96 97 98 99 100 |
---
license: other
base_model: Qwen/Qwen1.5-4B
tags:
- generated_from_trainer
datasets:
- tyzhu/lmind_nq_train6000_eval6489_v1_recite_qa_v3
metrics:
- accuracy
model-index:
- name: lmind_nq_train6000_eval6489_v1_recite_qa_v3_Qwen_Qwen1.5-4B_3e-4_lora2
results:
- task:
name: Causal Language Modeling
type: text-generation
dataset:
name: tyzhu/lmind_nq_train6000_eval6489_v1_recite_qa_v3
type: tyzhu/lmind_nq_train6000_eval6489_v1_recite_qa_v3
metrics:
- name: Accuracy
type: accuracy
value: 0.7981434977578475
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_recite_qa_v3_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_nq_train6000_eval6489_v1_recite_qa_v3 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4311
- Accuracy: 0.7981
## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 1.7637 | 1.0 | 529 | 1.4995 | 0.6288 |
| 1.3986 | 2.0 | 1058 | 1.1711 | 0.6720 |
| 0.9515 | 3.0 | 1587 | 0.8766 | 0.7148 |
| 0.642 | 4.0 | 2116 | 0.6720 | 0.7478 |
| 0.4362 | 5.0 | 2645 | 0.5458 | 0.7697 |
| 0.3201 | 6.0 | 3174 | 0.4751 | 0.7823 |
| 0.2652 | 7.0 | 3703 | 0.4510 | 0.7887 |
| 0.2263 | 8.0 | 4232 | 0.4372 | 0.7914 |
| 0.2035 | 9.0 | 4761 | 0.4335 | 0.7940 |
| 0.1913 | 10.0 | 5290 | 0.4322 | 0.7950 |
| 0.188 | 11.0 | 5819 | 0.4379 | 0.7945 |
| 0.1777 | 12.0 | 6348 | 0.4279 | 0.7957 |
| 0.1723 | 13.0 | 6877 | 0.4326 | 0.7956 |
| 0.1767 | 14.0 | 7406 | 0.4329 | 0.7967 |
| 0.1666 | 15.0 | 7935 | 0.4396 | 0.7962 |
| 0.1642 | 16.0 | 8464 | 0.4391 | 0.7965 |
| 0.1575 | 17.0 | 8993 | 0.4405 | 0.7967 |
| 0.1634 | 18.0 | 9522 | 0.4265 | 0.7976 |
| 0.1593 | 19.0 | 10051 | 0.4323 | 0.7978 |
| 0.153 | 20.0 | 10580 | 0.4311 | 0.7981 |
### Framework versions
- PEFT 0.5.0
- Transformers 4.40.2
- Pytorch 2.3.0
- Datasets 2.19.1
- Tokenizers 0.19.1
|