File size: 4,076 Bytes
0241fa9
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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
101
102
103
104
105
106
107
108
109
110
---
license: apache-2.0
base_model: distilbert/distilgpt2
tags:
- generated_from_trainer
model-index:
- name: result_llm
  results: []
---

<!-- 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. -->

# result_llm

This model is a fine-tuned version of [distilbert/distilgpt2](https://huggingface.co/distilbert/distilgpt2) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: nan

## 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: 4
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3

### Training results

| Training Loss | Epoch  | Step  | Validation Loss |
|:-------------:|:------:|:-----:|:---------------:|
| 8.289         | 0.0554 | 500   | nan             |
| 6.8357        | 0.1109 | 1000  | nan             |
| 6.7413        | 0.1663 | 1500  | nan             |
| 6.6101        | 0.2218 | 2000  | nan             |
| 6.6348        | 0.2772 | 2500  | nan             |
| 6.6871        | 0.3326 | 3000  | nan             |
| 6.602         | 0.3881 | 3500  | nan             |
| 6.6078        | 0.4435 | 4000  | nan             |
| 6.5465        | 0.4989 | 4500  | nan             |
| 6.5643        | 0.5544 | 5000  | nan             |
| 6.5696        | 0.6098 | 5500  | nan             |
| 6.5294        | 0.6653 | 6000  | nan             |
| 6.5638        | 0.7207 | 6500  | nan             |
| 6.4361        | 0.7761 | 7000  | nan             |
| 6.4547        | 0.8316 | 7500  | nan             |
| 6.5327        | 0.8870 | 8000  | nan             |
| 6.3524        | 0.9425 | 8500  | nan             |
| 6.4341        | 0.9979 | 9000  | nan             |
| 6.3677        | 1.0533 | 9500  | nan             |
| 6.199         | 1.1088 | 10000 | nan             |
| 6.3033        | 1.1642 | 10500 | nan             |
| 6.2976        | 1.2196 | 11000 | nan             |
| 6.2322        | 1.2751 | 11500 | nan             |
| 6.2222        | 1.3305 | 12000 | nan             |
| 6.2119        | 1.3860 | 12500 | nan             |
| 6.2336        | 1.4414 | 13000 | nan             |
| 6.349         | 1.4968 | 13500 | nan             |
| 6.311         | 1.5523 | 14000 | nan             |
| 6.2247        | 1.6077 | 14500 | nan             |
| 6.2851        | 1.6632 | 15000 | nan             |
| 6.35          | 1.7186 | 15500 | nan             |
| 6.2996        | 1.7740 | 16000 | nan             |
| 6.3229        | 1.8295 | 16500 | nan             |
| 6.3609        | 1.8849 | 17000 | nan             |
| 6.3063        | 1.9403 | 17500 | nan             |
| 6.2759        | 1.9958 | 18000 | nan             |
| 6.2499        | 2.0512 | 18500 | nan             |
| 6.1473        | 2.1067 | 19000 | nan             |
| 6.2088        | 2.1621 | 19500 | nan             |
| 6.2482        | 2.2175 | 20000 | nan             |
| 6.2123        | 2.2730 | 20500 | nan             |
| 6.2298        | 2.3284 | 21000 | nan             |
| 6.2666        | 2.3839 | 21500 | nan             |
| 6.21          | 2.4393 | 22000 | nan             |
| 6.2396        | 2.4947 | 22500 | nan             |
| 6.2626        | 2.5502 | 23000 | nan             |
| 6.1824        | 2.6056 | 23500 | nan             |
| 6.3142        | 2.6610 | 24000 | nan             |
| 6.2816        | 2.7165 | 24500 | nan             |
| 6.2371        | 2.7719 | 25000 | nan             |
| 6.3075        | 2.8274 | 25500 | nan             |
| 6.2306        | 2.8828 | 26000 | nan             |
| 6.2919        | 2.9382 | 26500 | nan             |
| 6.2668        | 2.9937 | 27000 | nan             |


### Framework versions

- Transformers 4.42.4
- Pytorch 2.4.0+cu121
- Tokenizers 0.19.1