--- language: - pt license: apache-2.0 library_name: transformers tags: - Misral - Portuguese - 7b base_model: meta-llama/Llama-2-13b-chat-hf datasets: - pablo-moreira/gpt4all-j-prompt-generations-pt - rhaymison/superset pipeline_tag: text-generation model-index: - name: Llama-portuguese-13b-Luana-v0.2 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: 36.95 name: accuracy source: url: https://huggingface.co/spaces/eduagarcia/open_pt_llm_leaderboard?query=rhaymison/Llama-portuguese-13b-Luana-v0.2 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: 32.68 name: accuracy source: url: https://huggingface.co/spaces/eduagarcia/open_pt_llm_leaderboard?query=rhaymison/Llama-portuguese-13b-Luana-v0.2 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: 33.3 name: accuracy source: url: https://huggingface.co/spaces/eduagarcia/open_pt_llm_leaderboard?query=rhaymison/Llama-portuguese-13b-Luana-v0.2 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: 65.83 name: f1-macro source: url: https://huggingface.co/spaces/eduagarcia/open_pt_llm_leaderboard?query=rhaymison/Llama-portuguese-13b-Luana-v0.2 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: 42.81 name: pearson source: url: https://huggingface.co/spaces/eduagarcia/open_pt_llm_leaderboard?query=rhaymison/Llama-portuguese-13b-Luana-v0.2 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: 40.44 name: f1-macro source: url: https://huggingface.co/spaces/eduagarcia/open_pt_llm_leaderboard?query=rhaymison/Llama-portuguese-13b-Luana-v0.2 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: 83.62 name: f1-macro source: url: https://huggingface.co/spaces/eduagarcia/open_pt_llm_leaderboard?query=rhaymison/Llama-portuguese-13b-Luana-v0.2 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: 54.62 name: f1-macro source: url: https://huggingface.co/spaces/eduagarcia/open_pt_llm_leaderboard?query=rhaymison/Llama-portuguese-13b-Luana-v0.2 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: 49.25 name: f1-macro source: url: https://huggingface.co/spaces/eduagarcia/open_pt_llm_leaderboard?query=rhaymison/Llama-portuguese-13b-Luana-v0.2 name: Open Portuguese LLM Leaderboard --- # 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
# [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/Llama-portuguese-13b-Luana-v0.2) | Metric | Value | |--------------------------|---------| |Average |**48.83**| |ENEM Challenge (No Images)| 36.95| |BLUEX (No Images) | 32.68| |OAB Exams | 33.30| |Assin2 RTE | 65.83| |Assin2 STS | 42.81| |FaQuAD NLI | 40.44| |HateBR Binary | 83.62| |PT Hate Speech Binary | 54.62| |tweetSentBR | 49.25|