--- language: - pt license: apache-2.0 library_name: transformers tags: - mixtral - portuguese - portugues base_model: mistralai/Mixtral-8x7B-Instruct-v0.1 datasets: - rhaymison/superset pipeline_tag: text-generation model-index: - name: Mistral-8x7b-portuguese-luana 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: 69.63 name: accuracy source: url: https://huggingface.co/spaces/eduagarcia/open_pt_llm_leaderboard?query=rhaymison/Mistral-8x7b-portuguese-luana 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: 59.11 name: accuracy source: url: https://huggingface.co/spaces/eduagarcia/open_pt_llm_leaderboard?query=rhaymison/Mistral-8x7b-portuguese-luana 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: 49.61 name: accuracy source: url: https://huggingface.co/spaces/eduagarcia/open_pt_llm_leaderboard?query=rhaymison/Mistral-8x7b-portuguese-luana 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: 61.21 name: f1-macro source: url: https://huggingface.co/spaces/eduagarcia/open_pt_llm_leaderboard?query=rhaymison/Mistral-8x7b-portuguese-luana 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: 79.95 name: pearson source: url: https://huggingface.co/spaces/eduagarcia/open_pt_llm_leaderboard?query=rhaymison/Mistral-8x7b-portuguese-luana 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: 78.6 name: f1-macro source: url: https://huggingface.co/spaces/eduagarcia/open_pt_llm_leaderboard?query=rhaymison/Mistral-8x7b-portuguese-luana 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: 72.42 name: f1-macro source: url: https://huggingface.co/spaces/eduagarcia/open_pt_llm_leaderboard?query=rhaymison/Mistral-8x7b-portuguese-luana 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: 73.01 name: f1-macro source: url: https://huggingface.co/spaces/eduagarcia/open_pt_llm_leaderboard?query=rhaymison/Mistral-8x7b-portuguese-luana name: Open Portuguese LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: tweetSentBR type: eduagarcia/tweetsentbr_fewshot split: test args: num_few_shot: 25 metrics: - type: f1_macro value: 50.9 name: f1-macro source: url: https://huggingface.co/spaces/eduagarcia/open_pt_llm_leaderboard?query=rhaymison/Mistral-8x7b-portuguese-luana name: Open Portuguese LLM Leaderboard --- # Mistral-8x7b-portuguese-luana

This model was trained with a superset of 300,000 instructions in Portuguese. The model comes to help fill the gap in models in Portuguese. Tuned from the Mistral 7b in Portuguese, the model was adjusted mainly for instructional tasks. # How to use ### A100 GPU 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-24b-portuguese-luana", device_map= {"": 0}) tokenizer = AutoTokenizer.from_pretrained("rhaymison/Mistral-24b-portuguese-luana") 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") 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 Extracting a langChain conversation with a drug petition document as context. * Human: Bom dia. Me informe quais medicamentos são solicitados no texto. Informe também a posologia e o período de tratamento. * Luana: Olá! No texto são solicitados os medicamentos DULOXETINA 30mg,PREGABALINA 100mg ROSUVASTATINA 10mg. A posologia e o período de tratamento não são especificados, pois isso deve ser indicado periodicamente pelo médico. * Human: Perfeito. E qual é o valor da causa informado no documento? * Luana: O valor da causa informado no documento é R$ 6.132,00 (seis mil cento e três). * Human: Foi mencionado no texto alguma lei? * Luana: Sim, a Lei Complementar nº 80/94 é mencionada no texto. Ela se refere às prerrogativas institucionais da Defensoria Pública, incluindo a intimação pessoal de seus membros, a contagem em dobro dos prazos processuais, a manifestação por cotas nos autos e a dispensa da procuração. * Human: Perfeito! Muito obrigado pela ajuda. Por hora é somente isso =] * Luana: Foi um prazer ajudar! Se você tiver outras dúvidas ou necessitar de mais informações, não hesite em perguntar. Tenha um bom dia! # Open Portuguese LLM Leaderboard Evaluation Results Detailed results can be found [here](https://huggingface.co/datasets/eduagarcia-temp/llm_pt_leaderboard_raw_results/tree/main/rhaymison/Mistral-8x7b-portuguese-luana) and on the [🚀 Open Portuguese LLM Leaderboard](https://huggingface.co/spaces/eduagarcia/open_pt_llm_leaderboard) | Metric | Value | |--------------------------|---------| |Average |**66.05**| |ENEM Challenge (No Images)| 69.63| |BLUEX (No Images) | 59.11| |OAB Exams | 49.61| |Assin2 RTE | 61.21| |Assin2 STS | 79.95| |FaQuAD NLI | 78.60| |HateBR Binary | 72.42| |PT Hate Speech Binary | 73.01| |tweetSentBR | 50.90| ### Comments Any idea, help or report will always be welcome. email: rhaymisoncristian@gmail.com