Mistral Portuguese Luana 7b

🚀 Mistral-portuguese-luana-7b

This model was trained with a superset of 200,000 Portuguese instructions, aiming to fill the gap in Portuguese models. Tuned from Mistral 7b, it is mainly adjusted for instructional tasks.

🚀 Quick Start

This model was trained with a superset of 200,000 instructions in Portuguese. It comes to help fill the gap in models in Portuguese. Tuned from the Mistral 7b, the model was adjusted mainly for instructional tasks.

If you are looking for enhanced compatibility, the Luana model also has a GGUF family that can be run with LlamaCpp. You can explore the GGUF models starting with the ones below:

• Mistral-portuguese-luana-7b-q8-gguf
• Mistral-portuguese-luana-7b-f16-gguf

Explore these and other models to find the best fit for your needs!

✨ Features

• Trained with 200,000 Portuguese instructions.
• Tuned from Mistral 7b for instructional tasks.
• Has a GGUF family for enhanced compatibility with LlamaCpp.

📦 Installation

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

💻 Usage Examples

Basic Usage

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

inputs = tokenizer([f"""<s>[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")

inputs.to(model.device)

streamer = TextStreamer(tokenizer, skip_prompt=True, skip_special_tokens=True)
_ = model.generate(**inputs, streamer=streamer, max_new_tokens=200)

Advanced Usage - 4bits Example

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}
)

Hardware Requirements

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

⚠️ Important Note

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.

💡 Usage Tip

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.

📚 Documentation

LangChain

Open Portuguese LLM Leaderboard Evaluation Results

Detailed results can be found here

📄 License

This model is licensed under the Apache-2.0 license.