Create README.md
Browse files
README.md
ADDED
@@ -0,0 +1,75 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
---
|
2 |
+
license: apache-2.0
|
3 |
+
datasets:
|
4 |
+
- mlabonne/CodeLlama-2-20k
|
5 |
+
tags:
|
6 |
+
- code
|
7 |
+
---
|
8 |
+
|
9 |
+
# π¦π» CodeLlama
|
10 |
+
|
11 |
+
π [Article](https://towardsdatascience.com/fine-tune-your-own-llama-2-model-in-a-colab-notebook-df9823a04a32) |
|
12 |
+
π» [Colab](https://colab.research.google.com/drive/1PEQyJO1-f6j0S_XJ8DV50NkpzasXkrzd?usp=sharing) |
|
13 |
+
π [Script](https://gist.github.com/mlabonne/b5718e1b229ce6553564e3f56df72c5c)
|
14 |
+
|
15 |
+
<center><img src="https://i.imgur.com/yTPNIZj.png" width="300"></center>
|
16 |
+
|
17 |
+
`CodeLlama-7b` is a Llama 2 version of [**CodeAlpaca**](https://github.com/sahil280114/codealpaca).
|
18 |
+
|
19 |
+
## π§ Training
|
20 |
+
|
21 |
+
This model is based on the `Llama-2-7b-hf` model, fine-tuned using QLoRA on the [`mlabonne/CodeLlama-2-20k`](https://huggingface.co/datasets/mlabonne/CodeLlama-2-20k) dataset. It was trained on an RTX 3090 and can be used for inference.
|
22 |
+
|
23 |
+
It was trained using this custom [`finetune_llama2.py`](https://gist.github.com/mlabonne/b5718e1b229ce6553564e3f56df72c5c) script as follows:
|
24 |
+
|
25 |
+
``` bash
|
26 |
+
python finetune_llama2.py --dataset_name=mlabonne/CodeLlama-2-20k --new_model=mlabonne/codellama-2-7b --bf16=True --learning_rate=2e-5
|
27 |
+
```
|
28 |
+
|
29 |
+
<center><img src="https://i.imgur.com/5Qx7Kzo.png"></center>
|
30 |
+
|
31 |
+
## π» Usage
|
32 |
+
|
33 |
+
``` python
|
34 |
+
# pip install transformers accelerate
|
35 |
+
|
36 |
+
from transformers import AutoTokenizer
|
37 |
+
import transformers
|
38 |
+
import torch
|
39 |
+
|
40 |
+
model = "mlabonne/codellama-2-7b"
|
41 |
+
prompt = "Write Python code to generate an array with all the numbers from 1 to 100"
|
42 |
+
|
43 |
+
tokenizer = AutoTokenizer.from_pretrained(model)
|
44 |
+
pipeline = transformers.pipeline(
|
45 |
+
"text-generation",
|
46 |
+
model=model,
|
47 |
+
torch_dtype=torch.float16,
|
48 |
+
device_map="auto",
|
49 |
+
)
|
50 |
+
|
51 |
+
sequences = pipeline(
|
52 |
+
f'<s>[INST] {prompt} [/INST]',
|
53 |
+
do_sample=True,
|
54 |
+
top_k=10,
|
55 |
+
num_return_sequences=1,
|
56 |
+
eos_token_id=tokenizer.eos_token_id,
|
57 |
+
max_length=200,
|
58 |
+
)
|
59 |
+
for seq in sequences:
|
60 |
+
print(f"Result: {seq['generated_text']}")
|
61 |
+
```
|
62 |
+
|
63 |
+
Ouput:
|
64 |
+
```
|
65 |
+
Here is a Python code to generate an array with all the numbers from 1 to 100:
|
66 |
+
|
67 |
+
β
```
|
68 |
+
numbers = []
|
69 |
+
for i in range(1,101):
|
70 |
+
numbers.append(i)
|
71 |
+
β
```
|
72 |
+
|
73 |
+
This code generates an array with all the numbers from 1 to 100 in Python. It uses a loop that iterates over the range of numbers from 1 to 100, and for each number, it appends that number to the array 'numbers'. The variable 'numbers' is initialized to a list, and its length is set to 101 by using the range of numbers (0-99).
|
74 |
+
|
75 |
+
```
|