--- base_model: rhaymison/phi-3-portuguese-tom-cat-4k-instruct datasets: - rhaymison/superset inference: true language: - pt library_name: transformers license: apache-2.0 model-index: - name: phi-3-portuguese-tom-cat-4k-instruct results: - dataset: args: num_few_shot: 3 name: ENEM Challenge (No Images) split: train type: eduagarcia/enem_challenge metrics: - name: accuracy type: acc value: 61.58 source: name: Open Portuguese LLM Leaderboard url: https://huggingface.co/spaces/eduagarcia/open_pt_llm_leaderboard?query=rhaymison/phi-3-portuguese-tom-cat-4k-instruct task: name: Text Generation type: text-generation - dataset: args: num_few_shot: 3 name: BLUEX (No Images) split: train type: eduagarcia-temp/BLUEX_without_images metrics: - name: accuracy type: acc value: 50.63 source: name: Open Portuguese LLM Leaderboard url: https://huggingface.co/spaces/eduagarcia/open_pt_llm_leaderboard?query=rhaymison/phi-3-portuguese-tom-cat-4k-instruct task: name: Text Generation type: text-generation - dataset: args: num_few_shot: 3 name: OAB Exams split: train type: eduagarcia/oab_exams metrics: - name: accuracy type: acc value: 43.69 source: name: Open Portuguese LLM Leaderboard url: https://huggingface.co/spaces/eduagarcia/open_pt_llm_leaderboard?query=rhaymison/phi-3-portuguese-tom-cat-4k-instruct task: name: Text Generation type: text-generation - dataset: args: num_few_shot: 15 name: Assin2 RTE split: test type: assin2 metrics: - name: f1-macro type: f1_macro value: 91.54 source: name: Open Portuguese LLM Leaderboard url: https://huggingface.co/spaces/eduagarcia/open_pt_llm_leaderboard?query=rhaymison/phi-3-portuguese-tom-cat-4k-instruct task: name: Text Generation type: text-generation - dataset: args: num_few_shot: 15 name: Assin2 STS split: test type: eduagarcia/portuguese_benchmark metrics: - name: pearson type: pearson value: 75.27 source: name: Open Portuguese LLM Leaderboard url: https://huggingface.co/spaces/eduagarcia/open_pt_llm_leaderboard?query=rhaymison/phi-3-portuguese-tom-cat-4k-instruct task: name: Text Generation type: text-generation - dataset: args: num_few_shot: 15 name: FaQuAD NLI split: test type: ruanchaves/faquad-nli metrics: - name: f1-macro type: f1_macro value: 47.46 source: name: Open Portuguese LLM Leaderboard url: https://huggingface.co/spaces/eduagarcia/open_pt_llm_leaderboard?query=rhaymison/phi-3-portuguese-tom-cat-4k-instruct task: name: Text Generation type: text-generation - dataset: args: num_few_shot: 25 name: HateBR Binary split: test type: ruanchaves/hatebr metrics: - name: f1-macro type: f1_macro value: 83.01 source: name: Open Portuguese LLM Leaderboard url: https://huggingface.co/spaces/eduagarcia/open_pt_llm_leaderboard?query=rhaymison/phi-3-portuguese-tom-cat-4k-instruct task: name: Text Generation type: text-generation - dataset: args: num_few_shot: 25 name: PT Hate Speech Binary split: test type: hate_speech_portuguese metrics: - name: f1-macro type: f1_macro value: 70.19 source: name: Open Portuguese LLM Leaderboard url: https://huggingface.co/spaces/eduagarcia/open_pt_llm_leaderboard?query=rhaymison/phi-3-portuguese-tom-cat-4k-instruct task: name: Text Generation type: text-generation - dataset: args: num_few_shot: 25 name: tweetSentBR split: test type: eduagarcia/tweetsentbr_fewshot metrics: - name: f1-macro type: f1_macro value: 57.78 source: name: Open Portuguese LLM Leaderboard url: https://huggingface.co/spaces/eduagarcia/open_pt_llm_leaderboard?query=rhaymison/phi-3-portuguese-tom-cat-4k-instruct task: name: Text Generation type: text-generation model_creator: rhaymison model_name: phi-3-portuguese-tom-cat-4k-instruct pipeline_tag: text-generation quantized_by: afrideva tags: - portugues - portuguese - QA - instruct - phi - gguf - ggml - quantized --- # phi-3-portuguese-tom-cat-4k-instruct-GGUF Quantized GGUF model files for [phi-3-portuguese-tom-cat-4k-instruct](https://huggingface.co/rhaymison/phi-3-portuguese-tom-cat-4k-instruct) from [rhaymison](https://huggingface.co/rhaymison) ## Original Model Card: # Phi-3-portuguese-tom-cat-4k-instruct

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 microsoft/Phi-3-mini-4k. # 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 4b) 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/phi-3-portuguese-tom-cat-4k-instruct", device_map= {"": 0}) tokenizer = AutoTokenizer.from_pretrained("rhaymison/phi-3-portuguese-tom-cat-4k-instruct") model.eval() ``` You can use with Pipeline. ```python from transformers import pipeline pipe = pipeline("text-generation", model=model, tokenizer=tokenizer, do_sample=True, max_new_tokens=512, num_beams=2, temperature=0.3, top_k=50, top_p=0.95, early_stopping=True, pad_token_id=tokenizer.eos_token_id, ) def format_template(question:str): system_prompt = "Abaixo está uma instrução que descreve uma tarefa, juntamente com uma entrada que fornece mais contexto. Escreva uma resposta que complete adequadamente o pedido." return f"""<|system|> { system_prompt } <|user|> { question } <|assistant|> """ question = format_template("E possivel ir de Carro dos Estados unidos ate o japão") pipe(question) ``` 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} ) ``` # 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/phi-3-portuguese-tom-cat-4k-instruct) and on the [🚀 Open Portuguese LLM Leaderboard](https://huggingface.co/spaces/eduagarcia/open_pt_llm_leaderboard) | Metric | Value | |--------------------------|---------| |Average |**64.57**| |ENEM Challenge (No Images)| 61.58| |BLUEX (No Images) | 50.63| |OAB Exams | 43.69| |Assin2 RTE | 91.54| |Assin2 STS | 75.27| |FaQuAD NLI | 47.46| |HateBR Binary | 83.01| |PT Hate Speech Binary | 70.19| |tweetSentBR | 57.78| ### Comments Any idea, help or report will always be welcome. email: rhaymisoncristian@gmail.com