File size: 5,231 Bytes
145cbdf
 
 
 
 
 
 
 
 
31c50ba
95165bf
 
 
 
 
145cbdf
 
 
 
 
 
 
 
 
 
 
 
 
 
74f0ffe
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
145cbdf
 
 
 
 
 
 
 
 
 
 
 
31c50ba
145cbdf
 
 
 
 
 
 
31c50ba
145cbdf
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
ddc81fa
145cbdf
 
 
95165bf
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
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
---
license: mit
datasets:
- theblackcat102/evol-codealpaca-v1
language:
- en
pipeline_tag: text-generation
tags:
- code
- PyTorch
- torch
- python
- coding
- agent
library_name: torch
---
# Lizard

A lightweight 17m parameter coding model.

## Model Details

### Model Description

- **Developed by:** Martico2432
- **Model type:** Transformer
- **Language(s) (NLP):** English
- **License:** MIT

### Examples

#### Bubble sort
Starting text: _def bubble_sort_

Generated text:

```
def bubble_sort(list):
    n = len(list)

    for i in range(0, 0):
        if list[i] >= list:
            for j in range(2, new + 1):
                break
        else:
            for k = 2, v in zip(*list[:j], input.index(k)}")
            for x in list:
                print(x * y - y)

    return isinstance(list, tuple))

print(merge([3, 4])) # Returns: [1, 5, 6, 8]
```

Note: Seems to be confused

#### Torch import
Starting text: _import torch_

Generated text:

```
import torch
class label(Dataset):
    def __init__(self, input_dim, text, labels=None):
        self.hidden_image = input("Enter a positive integer: "))

        # Run our training the model
        self.root = input_dim

    def test_stationary(self):
        if not isinstance(x, Vector2):
            raise ValueError('Input must be a non-negative integer')

        for x in range(0):
            try:
                y = nx + dx[x][y] * y

        position = (n - 1) / 3
        print('The height is', 8)

    def process_dimension(self):
        with open((self.data, 'a'))) as f:
            return pi * (1000 - 32), cv.fc[-1)]

    except Exception as e:
        print('All data points are not allowed.')


But I don't know how to write my code
```

Notes: Seems to not like his code

#### SQL
Starting text: _SQL:_

Generated text:

```
SQL:
```sql
DELIMITER Main

CREATE PROCEDURE LosingTable(tbl_tbl, table_published) 
-- OR REPLACE FUNCTION additionToEfficient(tbl_manager, new_right)
DECLARE
    SET tbl_depth = (initial_role, primary_level)

    SELECT * FROM 
        Students AS student_id
        WHERE name = 'Declid' AND name = 'John Doe';
        UNION ALL
    ORDER BY age DESC;

    WHEN AWS LIKE 'Jumping' THEN 
        this.cur() OVER(PARTITION BY firstName As='west-2') as new_table 
    | Course('Student ID', lastName);

    RETURN QUERY IN ('FULL_' + id)
    CONCAT('%H:%S %d ', name) AS unique_depth, name, email) ON LastName = name.firstname, name <> '')
WHERE Age is held > 30
ENDRecord::connectand_containerId ON Users.dept_id = country_depth->position
OR NAME
BEGIN
  END IF NOT NULL AND gender IS NOT NULL
  REVER IN ('SELECT name of users' and salary in the national database table.') 
  ELSE 
    GMT+1.0+6.7+8.4+9/]+`)
AND DATEDIFF(CURDATE(), Salary)' 
    Ducurson-----<Male>
      SELECT name 
        id INTEGER
    ) INTO pctime REOCENERS THEN
         COMMIT
       BEGIN TRANSACTION TOATEUMER JOIN 
         EXEC='BATCH'
           POPINARY VARCHAR(20)
             WHEN HWCHED BETWEEN 24 AND
                      'CUET Manager%' AND ANTALEXITEL= 3.5a?(TR).0e., 1.3%null "$100"
                            ROUTPUT" 
                                ...
                ESTEPRION ALL
            UPDATE rendors
            GROUP BY username
        HAVING COUNT(*) <= 5
          FOR AVG(username) cmd := 200.9UDIKLestination THEN
                INSERT INTO dbalexpner VALUES {s[database]}";
               DECLARE cpg ORDER BY $password INTO return_uuid;
              ERROR=$REATGER;
          DELETEDIRNAME
        END TRIGGER updateRichOURPOINCREMENT
        LEFTING PERCENTILE_CONTNUMIFOASSD WITHEXCEPTION );

      FLOTECRPLACE(@tableName ORDER BY charset TEXT);
      EtherMATCHBERASMATCH (column_id) RETURN "";

      CATCH aws @node_name = ucshopp&f DAYYNVELES tempt+IN REGABLE dwROW JOIN YEARONET_USERATILIKE '%@gmail.com').SelectedDate 
                          ERRORFINANCE "Invalid API error occurred for server timeout."};
   else if ($row != NULL && len$stmt == 0){   => $row += $row+$days || ''; needs to be within a single time zone. Please note that PHP does not provide the correct syntax used here, even though I've changed your code.
```

Notes: Seems to know SQL, but the context lenght limits it's memory
## Uses

- Experimentation with small models


### Direct Use

- Try to get the model to give a coherent code


### Out-of-Scope Use

This model won't work for any malicious goal. It's too dumb.

## Bias, Risks, and Limitations

17m parameters is not a lot, and it limits a lot it's usage

### Recommendations

Users (both direct and downstream) should be made aware of the risks, biases, and limitations of the model

## How to Get Started with the Model

Use the code in the repository of this model to get started

## Training Details

### Training Data

This model was trained using the **theblackcat102/evol-codealpaca-v1** dataset

### Training Procedure



#### Training Hyperparameters

- **Training regime:** fp32 <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->

## Technical Specifications

#### Hardware

- Any GPU with 8 Gb of memory should be able to run it, CPU doesn't run

#### Software

- PyTorch