File size: 5,872 Bytes
3083de1
9095279
316a7a6
cd85a0e
 
 
 
 
 
 
 
7a8b0c4
fcf0a55
3083de1
316a7a6
da52493
316a7a6
da52493
e265dad
316a7a6
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
9095279
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
316a7a6
 
 
 
 
 
 
 
 
 
 
20565e6
316a7a6
 
 
 
 
 
 
 
f62b8bf
 
 
316a7a6
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
cd85a0e
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
---
library_name: transformers
base_model: codellama/CodeLlama-7b-Instruct-hf
license: apache-2.0
datasets:
- semantixai/Test-Dataset-Lloro
language:
- pt
tags:
- code
- analytics
- analise-dados
- portugues-BR
---

**Lloro 7B**

<img src="https://cdn-uploads.huggingface.co/production/uploads/653176dc69fffcfe1543860a/h0kNd9OTEu1QdGNjHKXoq.png" width="300" alt="Lloro-7b Logo"/>


Lloro, developed by Semantix Research Labs , is a language Model that was trained  to effectively perform Portuguese Data Analysis. It is a fine-tuned version of codellama/CodeLlama-7b-Instruct-hf,  that was trained on synthetic datasets .  The fine-tuning process was performed using the QLORA metodology on a GPU V100 with 16 GB of RAM.

 

**Model description**


Model type: A 7B parameter  fine-tuned on synthetic datasets.

Language(s) (NLP): Primarily Portuguese, but the model is capable to understand English as well

Finetuned from model: codellama/CodeLlama-7b-Instruct-hf

 

**What is Lloro's intended use(s)?**


Lloro is built for data analysis in Portuguese contexts .

Input : Text

Output : Text (Code)


**Usage**

Using Transformers
```python
#Import required libraries
import torch
from transformers import (
    AutoModelForCausalLM,
    AutoTokenizer
)

#Load Model
model_name = "semantixai/LloroV2"
base_model = AutoModelForCausalLM.from_pretrained(
        model_name,
        return_dict=True,
        torch_dtype=torch.float16,
        device_map="auto",
    )

#Load Tokenizer
tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)


#Define Prompt
user_prompt = "Desenvolva um algoritmo em Python para calcular a média e a mediana dos preços de vendas por tipo de material do produto."
system = "Provide answers in Python without explanations, only the code"
prompt_template = f"[INST] <<SYS>>\\n{system}\\n<</SYS>>\\n\\n{user_prompt}[/INST]"

#Call the model
input_ids = tokenizer([prompt_template], return_tensors="pt")["input_ids"].to("cuda")

            
outputs = base_model.generate(
    input_ids,
    do_sample=True,
    top_p=0.95,
    max_new_tokens=1024,
    temperature=0.1,
    )

#Decode and retrieve Output
output_text = tokenizer.batch_decode(outputs, skip_prompt=True, skip_special_tokens=False)
display(output_text)
```

Using an OpenAI compatible inference server (like [vLLM](https://docs.vllm.ai/en/latest/index.html))
```python
from openai import OpenAI

client = OpenAI(
    api_key="EMPTY",
    base_url="http://localhost:8000/v1",
)
user_prompt = "Desenvolva um algoritmo em Python para calcular a média e a mediana dos preços de vendas por tipo de material do produto."
completion = client.chat.completions.create(temperature=0.1,frequency_penalty=0.1,model="semantixai/LloroV2",messages=[{"role":"system","content":"Provide answers in Python without explanations, only the code"},{"role":"user","content":user_prompt}])
```
 

**Params**
Training Parameters
| Params                           | Training Data                   | Examples                        | Tokens   | LR     |
|----------------------------------|---------------------------------|---------------------------------|----------|--------|
| 7B                               | Pairs synthetic instructions/code | 28907                             | 3 031 188 | 1e-5   |
 

**Model Sources**

Test Dataset Repository: https://huggingface.co/datasets/semantixai/Test-Dataset-Lloro

Model Dates Lloro was trained between November 2023 and January 2024.

 

**Performance**
 | Modelo         | LLM as Judge | Code Bleu Score | Rouge-L |  CodeBert- Precision | CodeBert-Recall | CodeBert-F1 | CodeBert-F3 |
|----------------|--------------|------------------|---------|----------------------|-----------------|-------------|-------------|
| GPT 3.5        | 91.22%       | 0.2745           | 0.2189  |  0.7502               | 0.7146          | 0.7303       | 0.7175      |
| Instruct -Base | 97.40%       | 0.2487           | 0.1146  | 0.6997               | 0.6473          | 0.6713       | 0.6518      |
| Instruct -FT   | 97.76%       | 0.3264           | 0.3602  |  0.7942               | 0.8178          | 0.8042       | 0.8147      |


**Training Infos:**
The following hyperparameters were used during training:

| Parameter                 | Value                |
|---------------------------|----------------------|
| learning_rate             | 1e-5                 |
| weight_decay              | 0.0001               |
| train_batch_size          | 1                    |
| eval_batch_size           | 1                    |
| seed                      | 42                   |
| optimizer                 | Adam - paged_adamw_32bit |
| lr_scheduler_type         | cosine               |
| lr_scheduler_warmup_ratio | 0.03                 |
| num_epochs                | 5.0                  |

**QLoRA hyperparameters**
The following parameters related with the Quantized Low-Rank Adaptation  and Quantization were used during training:

| Parameter       | Value   |
|------------------|---------|
| lora_r           | 16      |
| lora_alpha       | 64      |
| lora_dropout     | 0.1     |
| storage_dtype    | "nf4"   |
| compute_dtype    | "float16"|


**Experiments**
| Model                 | Epochs | Overfitting | Final Epochs | Training Hours | CO2 Emission (Kg) |
|-----------------------|--------|-------------|--------------|-----------------|--------------------|
| Code Llama Instruct   | 1      | No          | 1            | 8.1               | 1.337                  |
| Code Llama Instruct   | 5      | Yes         | 3            | 45.6               | 9.12                  |

**Framework versions**

| Library       | Version   |
|---------------|-----------|
| bitsandbytes  | 0.40.2    |
| Datasets      | 2.14.3    |
| Pytorch       | 2.0.1     |
| Tokenizers    | 0.14.1    |
| Transformers  | 4.34.0    |