---
library_name: transformers
tags:
- Misral
- Portuguese
- 7b
license: apache-2.0
base_model: meta-llama/Llama-2-13b-chat-hf
datasets:
- pablo-moreira/gpt4all-j-prompt-generations-pt
- rhaymison/superset
language:
- pt
pipeline_tag: text-generation
---
# Mistral-portuguese-luana-7b
This model was trained with a superset of 200,000 instructions in Portuguese.
The model comes to help fill the gap in models in Portuguese. Tuned from the Llama 2 13b in Portuguese, the model was adjusted mainly for instructional tasks.
The model comes from the idea of helping to fill the need for Portuguese language models.
# How to use
You can use the model in its normal form up to 4-bit or 8-bit quantization.
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 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
)
from transformers import AutoModelForCausalLM, AutoTokenizer, TextStreamer
model = AutoModelForCausalLM.from_pretrained("rhaymison/Llama-portuguese-13b-Luana-v0.2", quantization_config=bnb_config, device_map= {"": 0})
tokenizer = AutoTokenizer.from_pretrained("rhaymison/Llama-portuguese-13b-Luana-v0.2")
model.eval()
```
You can use with Pipeline but in this example i will use such as Streaming
```python
inputs = tokenizer([f"""[INST] Abaixo está uma instrução que descreve uma tarefa, juntamente com uma entrada que fornece mais contexto.
Escreva uma resposta que complete adequadamente o pedido.
### instrução: aja como um professor de matemática e me explique porque 2 + 2 = 4.
[/INST]"""], return_tensors="pt")
streamer = TextStreamer(tokenizer, skip_prompt=True, skip_special_tokens=True)
_ = model.generate(**inputs, streamer=streamer, max_new_tokens=200)
```
### Comments
Any idea, help or report will always be welcome.
email: rhaymisoncristian@gmail.com
