README.md

---
language:
- pt
license: apache-2.0
library_name: transformers
tags:
- Misral
- Portuguese
- 7b
- chat
- portugues
base_model: Qwen/Qwen1.5-7B-Chat
datasets:
- rhaymison/superset
pipeline_tag: text-generation
---

# Qwen-portuguese-luana-7b

<p align="center">
  <img src="https://raw.githubusercontent.com/rhaymisonbetini/huggphotos/main/luana-qwen.webp"  width="50%" style="margin-left:'auto' margin-right:'auto' display:'block'"/>
</p>


This model was trained with a superset of 250,000 chat in Portuguese. 
The model comes to help fill the gap in models in Portuguese. Tuned from the Qwen1.5-7B-Chat in Portuguese.

# How to use

### FULL MODEL : A100
### HALF MODEL: L4
### 8bit or 4bit : T4 or V100

You can use the model in its normal form up to 4-bit quantization. Below we will use both approaches.
Remember that verbs are important in your prompt. Tell your model how to act or behave so that you can guide them along the path of their response. 
Important points like these help models (even smaller models like 7b) to perform much better.

```python
!pip install -q -U transformers
!pip install -q -U accelerate
!pip install -q -U bitsandbytes

from transformers import AutoModelForCausalLM, AutoTokenizer, TextStreamer
from transformers import pipeline
model = AutoModelForCausalLM.from_pretrained("rhaymison/Qwen-portuguese-luana-7b", device_map= {"": 0})
tokenizer = AutoTokenizer.from_pretrained("rhaymison/Qwen-portuguese-luana-7b")
model.eval()

```

You can use with Pipeline but in this example i will use such as Streaming
```python

prompt = f"""<|im_start|>system
Abaixo está uma instrução que descreve uma tarefa, juntamente com uma entrada que fornece mais contexto. Escreva uma resposta que complete adequadamente o pedido.<|im_end|>
<|im_start|>user
 ### instrução: Me indique uma programação para fazer no final de semana com minha esposa. <|im_end|>
<|im_start|>"""



pipe = pipeline("text-generation",
                model=model,
                tokenizer=tokenizer,
                do_sample=True,
                max_new_tokens=200,
                num_beams=2,
                temperature=0.3,
                top_k=50,
                top_p=0.95,
                early_stopping=True,
                pad_token_id=tokenizer.eos_token_id,
                )

pipe(prompt)[0]['generated_text'].split('assistant')[1]

#Claro! Aqui está uma sugestão de programação para o final de semana com sua esposa:
#Domingo:
#1. Despertar cedo para um café da manhã delicioso juntos.
#2. Faça uma caminhada ou uma corrida no parque local para aproveitar o ar fresco e a natureza.
#3. Depois do café da manhã, faça uma caminhada de compras para escolher algumas roupas ou acessórios novos.
#4. Retorne para casa para preparar uma refeição deliciosa juntos.
#5. Depois do almoço, você pode assistir a um filme ou jogar jogos de tabuleiro para relaxar

```

If you are having a memory problem such as "CUDA Out of memory", you should use 4-bit or 8-bit quantization.
For the complete model in colab you will need the A100.
If you want to use 4bits or 8bits, T4 or L4 will already solve the problem.

# 4bits example

```python
from transformers import BitsAndBytesConfig
import torch
nb_4bit_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_compute_dtype=torch.bfloat16,
    bnb_4bit_use_double_quant=True
)

model = AutoModelForCausalLM.from_pretrained(
    base_model,
    quantization_config=bnb_config,
    device_map={"": 0}
)

```


### Comments

Any idea, help or report will always be welcome.

email: [email protected]

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