Instructions to use rhaymison/Mistral-portuguese-luana-7b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use rhaymison/Mistral-portuguese-luana-7b with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="rhaymison/Mistral-portuguese-luana-7b")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForMultimodalLM

tokenizer = AutoTokenizer.from_pretrained("rhaymison/Mistral-portuguese-luana-7b")
model = AutoModelForMultimodalLM.from_pretrained("rhaymison/Mistral-portuguese-luana-7b")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use rhaymison/Mistral-portuguese-luana-7b with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "rhaymison/Mistral-portuguese-luana-7b"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "rhaymison/Mistral-portuguese-luana-7b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/rhaymison/Mistral-portuguese-luana-7b

SGLang

How to use rhaymison/Mistral-portuguese-luana-7b with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "rhaymison/Mistral-portuguese-luana-7b" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "rhaymison/Mistral-portuguese-luana-7b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "rhaymison/Mistral-portuguese-luana-7b" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "rhaymison/Mistral-portuguese-luana-7b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use rhaymison/Mistral-portuguese-luana-7b with Docker Model Runner:
```
docker model run hf.co/rhaymison/Mistral-portuguese-luana-7b
```

Mistral-portuguese-luana-7b / README.md

rhaymison

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	---
	language:
	- pt
	license: apache-2.0
	library_name: transformers
	tags:
	- Misral
	- Portuguese
	- 7b
	base_model: mistralai/Mistral-7B-Instruct-v0.2
	datasets:
	- pablo-moreira/gpt4all-j-prompt-generations-pt
	- rhaymison/superset
	pipeline_tag: text-generation
	model-index:
	- name: Mistral-portuguese-luana-7b
	results:
	- task:
	type: text-generation
	name: Text Generation
	dataset:
	name: ENEM Challenge (No Images)
	type: eduagarcia/enem_challenge
	split: train
	args:
	num_few_shot: 3
	metrics:
	- type: acc
	value: 58.64
	name: accuracy
	source:
	url: https://huggingface.co/spaces/eduagarcia/open_pt_llm_leaderboard?query=rhaymison/Mistral-portuguese-luana-7b
	name: Open Portuguese LLM Leaderboard
	- task:
	type: text-generation
	name: Text Generation
	dataset:
	name: BLUEX (No Images)
	type: eduagarcia-temp/BLUEX_without_images
	split: train
	args:
	num_few_shot: 3
	metrics:
	- type: acc
	value: 47.98
	name: accuracy
	source:
	url: https://huggingface.co/spaces/eduagarcia/open_pt_llm_leaderboard?query=rhaymison/Mistral-portuguese-luana-7b
	name: Open Portuguese LLM Leaderboard
	- task:
	type: text-generation
	name: Text Generation
	dataset:
	name: OAB Exams
	type: eduagarcia/oab_exams
	split: train
	args:
	num_few_shot: 3
	metrics:
	- type: acc
	value: 38.82
	name: accuracy
	source:
	url: https://huggingface.co/spaces/eduagarcia/open_pt_llm_leaderboard?query=rhaymison/Mistral-portuguese-luana-7b
	name: Open Portuguese LLM Leaderboard
	- task:
	type: text-generation
	name: Text Generation
	dataset:
	name: Assin2 RTE
	type: assin2
	split: test
	args:
	num_few_shot: 15
	metrics:
	- type: f1_macro
	value: 90.63
	name: f1-macro
	source:
	url: https://huggingface.co/spaces/eduagarcia/open_pt_llm_leaderboard?query=rhaymison/Mistral-portuguese-luana-7b
	name: Open Portuguese LLM Leaderboard
	- task:
	type: text-generation
	name: Text Generation
	dataset:
	name: Assin2 STS
	type: eduagarcia/portuguese_benchmark
	split: test
	args:
	num_few_shot: 15
	metrics:
	- type: pearson
	value: 75.81
	name: pearson
	source:
	url: https://huggingface.co/spaces/eduagarcia/open_pt_llm_leaderboard?query=rhaymison/Mistral-portuguese-luana-7b
	name: Open Portuguese LLM Leaderboard
	- task:
	type: text-generation
	name: Text Generation
	dataset:
	name: FaQuAD NLI
	type: ruanchaves/faquad-nli
	split: test
	args:
	num_few_shot: 15
	metrics:
	- type: f1_macro
	value: 57.79
	name: f1-macro
	source:
	url: https://huggingface.co/spaces/eduagarcia/open_pt_llm_leaderboard?query=rhaymison/Mistral-portuguese-luana-7b
	name: Open Portuguese LLM Leaderboard
	- task:
	type: text-generation
	name: Text Generation
	dataset:
	name: HateBR Binary
	type: ruanchaves/hatebr
	split: test
	args:
	num_few_shot: 25
	metrics:
	- type: f1_macro
	value: 77.24
	name: f1-macro
	source:
	url: https://huggingface.co/spaces/eduagarcia/open_pt_llm_leaderboard?query=rhaymison/Mistral-portuguese-luana-7b
	name: Open Portuguese LLM Leaderboard
	- task:
	type: text-generation
	name: Text Generation
	dataset:
	name: PT Hate Speech Binary
	type: hate_speech_portuguese
	split: test
	args:
	num_few_shot: 25
	metrics:
	- type: f1_macro
	value: 68.5
	name: f1-macro
	source:
	url: https://huggingface.co/spaces/eduagarcia/open_pt_llm_leaderboard?query=rhaymison/Mistral-portuguese-luana-7b
	name: Open Portuguese LLM Leaderboard
	- task:
	type: text-generation
	name: Text Generation
	dataset:
	name: tweetSentBR
	type: eduagarcia-temp/tweetsentbr
	split: test
	args:
	num_few_shot: 25
	metrics:
	- type: f1_macro
	value: 63.0
	name: f1-macro
	source:
	url: https://huggingface.co/spaces/eduagarcia/open_pt_llm_leaderboard?query=rhaymison/Mistral-portuguese-luana-7b
	name: Open Portuguese LLM Leaderboard
	---

	# Mistral-portuguese-luana-7b

	<p align="center">
	<img src="https://raw.githubusercontent.com/rhaymisonbetini/huggphotos/main/luana7b.webp" width="50%" style="margin-left:'auto' margin-right:'auto' display:'block'"/>
	</p>


	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 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 one below:

	- [Mistral-portuguese-luana-7b-q8-gguf](https://huggingface.co/rhaymison/Mistral-portuguese-luana-7b-q8-gguf)
	- [Mistral-portuguese-luana-7b-f16-gguf](https://huggingface.co/rhaymison/Mistral-portuguese-luana-7b-f16-gguf)

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

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

	```

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

	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)

	```

	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}
	)

	```

	# LangChain

	<p align="center">
	<img src="https://raw.githubusercontent.com/rhaymisonbetini/huggphotos/main/lang.png" alt="Bode Logo" width="100%" style="margin-left:'auto' margin-right:'auto' display:'block'"/>
	</p>



	# [Open Portuguese LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/eduagarcia/open_pt_llm_leaderboard)
	Detailed results can be found [here](https://huggingface.co/datasets/eduagarcia-temp/llm_pt_leaderboard_raw_results/tree/main/rhaymison/Mistral-portuguese-luana-7b)

	\| Metric \| Value \|
	\|--------------------------\|---------\|
	\|Average \|64.27\|
	\|ENEM Challenge (No Images)\| 58.64\|
	\|BLUEX (No Images) \| 47.98\|
	\|OAB Exams \| 38.82\|
	\|Assin2 RTE \| 90.63\|
	\|Assin2 STS \| 75.81\|
	\|FaQuAD NLI \| 57.79\|
	\|HateBR Binary \| 77.24\|
	\|PT Hate Speech Binary \| 68.50\|
	\|tweetSentBR \| 63\|



	### Comments

	Any idea, help or report will always be welcome.

	email: rhaymisoncristian@gmail.com

	<div style="display:flex; flex-direction:row; justify-content:left">
	<a href="https://www.linkedin.com/in/rhaymison-cristian-betini-2b3016175/" target="_blank">
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	</a>
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