Hugging Face's logo

tripathysagar
/
odia-gpt

nanoGPT
Model card Files Community
odia-gpt
File size: 2,868 Bytes 
36db0f8
993dbf9
36db0f8
 
 
 
 
 
 
 
 
 
 
 
 
 
 
deefad6
 
36db0f8
 
 
77b2b7e
 
 
36db0f8
77b2b7e
 
993dbf9
77b2b7e
 
 
 
993dbf9
77b2b7e
 
993dbf9
77b2b7e
993dbf9
77b2b7e
993dbf9
36db0f8
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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
 
---
library_name: nanoGPT
tags: []
---

# Model Card for Model ID

<!-- Provide a quick summary of what the model is/does. -->



## Model Details

### Model Description

<!-- Provide a longer summary of what this model is. -->

This is the model card of a model trained by Karpathy's [nanoGPT](https://github.com/karpathy/nanoGPT). The vocabulary size is 20_000 and the context window is 1024.<br>
The model is trined on tripathysagar/odia-news, news paper article extracted from odia daily [Dharitri](https://www.dharitri.com). 

## Uses

```python
>>> from huggingface_hub import snapshot_download
>>> snapshot_download(repo_id="tripathysagar/odia-gpt", local_dir='.')

>>> from model import GPT
>>> import os, torch

>>> from tokenizers import Tokenizer
>>> tokenizer = Tokenizer.from_file('tokenizer.json')
>>> nn, _ = GPT.from_file(os.path.join('model.pt'))
>>> nn = nn.to('cuda')

>>> s = 'କ୍ରେଡିଟ କାର୍ଡ ନେବା ସମୟରେ ଏହାର ସର୍ତ୍ତ ଏବଂ ନିୟମଗୁଡ଼ିକୁ ଧ୍ୟାନର ସହିତ ପଢ଼ିବା ଉଚିତ ।'
>>> enc = torch.tensor(tokenizer.encode(s).ids).unsqueeze(0).to('cuda')

>>> op = nn.generate(enc, 50, top_k=50)

>>> print(tokenizer.decode(op[0].to('cpu').tolist()))
```
### Direct Use

<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->


## Training Details

### Training Data

<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->

[More Information Needed]

### Training Procedure

<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->

#### Preprocessing [optional]

[More Information Needed]


#### Training Hyperparameters

- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->

#### Speeds, Sizes, Times [optional]

<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->

[More Information Needed]

## Evaluation

<!-- This section describes the evaluation protocols and provides the results. -->

### Testing Data, Factors & Metrics

#### Testing Data

<!-- This should link to a Dataset Card if possible. -->

[More Information Needed]

#### Factors

<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->

[More Information Needed]

#### Metrics

<!-- These are the evaluation metrics being used, ideally with a description of why. -->

[More Information Needed]

### Results

[More Information Needed]

#### Summary