metadata

language:
  - pt
license: apache-2.0
library_name: transformers
tags:
  - portugues
  - portuguese
  - QA
  - instruct
  - phi
  - gguf
  - q8
base_model: microsoft/Phi-3-mini-4k-instruct
datasets:
  - rhaymison/superset
pipeline_tag: text-generation

Phi-3-portuguese-tom-cat-4k-instruct

This GGUF model, derived from the Mixtrla Luana 7b, has been quantized in f16/half. The model was trained with a superset of 200,000 instructions in Portuguese, aiming to help fill the gap in models available in Portuguese. Tuned from the Mistral 7b, this model has been primarily adjusted for instructional tasks.

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.

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.

!git lfs install
!pip install langchain
!pip install langchain-community langchain-core
!pip install llama-cpp-python

!git clone https://huggingface.co/rhaymison/phi-3-portuguese-tom-cat-4k-instruct-q8-gguf

def llamacpp():
    from langchain.llms import LlamaCpp
    from langchain.prompts import PromptTemplate
    from langchain.chains import LLMChain
    
    llm = LlamaCpp(
        model_path="/content/phi-3-portuguese-tom-cat-4k-instruct-q8-gguf",
        n_gpu_layers=40,
        n_batch=512,
        verbose=True,
    )
    
    template = """<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.
    ### {question}
    [/INST]"""

    prompt = PromptTemplate(template=template, input_variables=["question"])
    
    llm_chain = LLMChain(prompt=prompt, llm=llm)
    
    question = "instrução: aja como um professor de matemática e me explique porque 2 + 2 = 4?"
    response = llm_chain.run({"question": question})
    print(response)

Comments

Any idea, help or report will always be welcome.

email: rhaymisoncristian@gmail.com