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--- |
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language: |
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- pt |
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license: apache-2.0 |
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library_name: transformers |
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tags: |
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- portugues |
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- portuguese |
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- QA |
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- instruct |
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- phi |
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- gguf |
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- f16 |
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base_model: microsoft/Phi-3-mini-4k-instruct |
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datasets: |
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- rhaymison/superset |
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pipeline_tag: text-generation |
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--- |
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# Phi3 portuguese tom cat 4k instruct GGUF |
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<p align="center"> |
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<img src="https://raw.githubusercontent.com/rhaymisonbetini/huggphotos/main/tom-cat.webp" width="50%" style="margin-left:'auto' margin-right:'auto' display:'block'"/> |
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</p> |
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This GGUF model, derived from the Phi3 Tom cat 4k, has been quantized in f16. The model was trained with a superset of 300,000 instructions in Portuguese, aiming to help fill the gap in models available in Portuguese. |
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Tuned from Phi3-4k, this model has been primarily adjusted for instructional tasks. |
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This model was trained with a superset of 300,000 instructions in Portuguese. |
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The model comes to help fill the gap in models in Portuguese. Tuned from the microsoft/Phi-3-mini-4k. |
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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. |
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Important points like these help models (even smaller models like 4b) to perform much better. |
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```python |
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!git lfs install |
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!pip install langchain |
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!pip install langchain-community langchain-core |
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!pip install llama-cpp-python |
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!git clone https://huggingface.co/rhaymison/phi-3-portuguese-tom-cat-4k-instruct-f16-gguf |
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def llamacpp(): |
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from langchain.llms import LlamaCpp |
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from langchain.prompts import PromptTemplate |
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from langchain.chains import LLMChain |
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llm = LlamaCpp( |
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model_path="/content/phi-3-portuguese-tom-cat-4k-instruct-f16-gguf", |
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n_gpu_layers=40, |
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n_batch=512, |
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verbose=True, |
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) |
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template = """<s>[INST] Abaixo está uma instrução que descreve uma tarefa, juntamente com uma entrada que fornece mais contexto. |
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Escreva uma resposta que complete adequadamente o pedido. |
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### {question} |
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[/INST]""" |
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prompt = PromptTemplate(template=template, input_variables=["question"]) |
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llm_chain = LLMChain(prompt=prompt, llm=llm) |
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question = "instrução: aja como um professor de matemática e me explique porque 2 + 2 = 4?" |
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response = llm_chain.run({"question": question}) |
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print(response) |
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``` |
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### Comments |
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Any idea, help or report will always be welcome. |
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email: [email protected] |
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<div style="display:flex; flex-direction:row; justify-content:left"> |
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<a href="https://www.linkedin.com/in/rhaymison-cristian-betini-2b3016175/" target="_blank"> |
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<img src="https://img.shields.io/badge/LinkedIn-0077B5?style=for-the-badge&logo=linkedin&logoColor=white"> |
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</a> |
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<a href="https://github.com/rhaymisonbetini" target="_blank"> |
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<img src="https://img.shields.io/badge/GitHub-100000?style=for-the-badge&logo=github&logoColor=white"> |
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</a> |
