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README.md
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library_name: transformers
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---
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#
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###
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- **License:** [More Information Needed]
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- **Finetuned from model [optional]:** [More Information Needed]
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## Uses
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<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
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## Bias, Risks, and Limitations
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### Recommendations
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<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
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Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
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## How to Get Started with the Model
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Use the code below to get started with the model.
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[More Information Needed]
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## Training Details
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### Training Data
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<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
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[More Information Needed]
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### Training Procedure
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<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
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#### Preprocessing [optional]
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[More Information Needed]
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#### Training Hyperparameters
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- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
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#### Speeds, Sizes, Times [optional]
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<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
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[More Information Needed]
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## Evaluation
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<!-- This section describes the evaluation protocols and provides the results. -->
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### Testing Data, Factors & Metrics
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#### Testing Data
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<!-- This should link to a Dataset Card if possible. -->
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[More Information Needed]
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#### Factors
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<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
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[More Information Needed]
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#### Metrics
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<!-- These are the evaluation metrics being used, ideally with a description of why. -->
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[More Information Needed]
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### Results
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[More Information Needed]
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#### Summary
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## Model Examination [optional]
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<!-- Relevant interpretability work for the model goes here -->
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[More Information Needed]
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## Environmental Impact
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<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
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Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
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- **Hardware Type:** [More Information Needed]
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- **Hours used:** [More Information Needed]
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- **Cloud Provider:** [More Information Needed]
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- **Compute Region:** [More Information Needed]
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- **Carbon Emitted:** [More Information Needed]
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## Technical Specifications [optional]
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### Model Architecture and Objective
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[More Information Needed]
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### Compute Infrastructure
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[More Information Needed]
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#### Hardware
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[More Information Needed]
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#### Software
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[More Information Needed]
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## Citation [optional]
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<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
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**BibTeX:**
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[More Information Needed]
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**APA:**
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[More Information Needed]
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## Glossary [optional]
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<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
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[More Information Needed]
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## More Information [optional]
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[More Information Needed]
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## Model Card Authors [optional]
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[More Information Needed]
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## Model Card Contact
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[More Information Needed]
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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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- Misral
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- Portuguese
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- 7b
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- chat
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- portugues
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base_model: Qwen/Qwen1.5-7B-Chat
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datasets:
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- rhaymison/superset
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pipeline_tag: text-generation
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# rhaymison/Qwen-portuguese-luana-7b
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<p align="center">
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<img src="https://raw.githubusercontent.com/rhaymisonbetini/huggphotos/main/luana-qwen.webp" width="50%" style="margin-left:'auto' margin-right:'auto' display:'block'"/>
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</p>
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This model was trained with a superset of 250,000 chat in Portuguese.
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The model comes to help fill the gap in models in Portuguese. Tuned from the Qwen1.5-7B-Chat in Portuguese.
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# How to use
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### FULL MODEL : A100
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### HALF MODEL: L4
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### 8bit or 4bit : T4 or V100
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You can use the model in its normal form up to 4-bit quantization. Below we will use both approaches.
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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 7b) to perform much better.
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```python
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!pip install -q -U transformers
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!pip install -q -U accelerate
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!pip install -q -U bitsandbytes
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from transformers import AutoModelForCausalLM, AutoTokenizer, TextStreamer
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from transformers import pipeline
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model = AutoModelForCausalLM.from_pretrained("rhaymison/Qwen-portuguese-luana-7b", device_map= {"": 0})
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tokenizer = AutoTokenizer.from_pretrained("rhaymison/Qwen-portuguese-luana-7b")
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model.eval()
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```
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You can use with Pipeline but in this example i will use such as Streaming
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```python
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prompt = f"""<|im_start|>system
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Abaixo está uma instrução que descreve uma tarefa, juntamente com uma entrada que fornece mais contexto. Escreva uma resposta que complete adequadamente o pedido.<|im_end|>
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<|im_start|>user
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### instrução: Me indique uma programação para fazer no final de semana com minha esposa. <|im_end|>
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<|im_start|>"""
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pipe = pipeline("text-generation",
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model=model,
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tokenizer=tokenizer,
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do_sample=True,
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max_new_tokens=200,
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num_beams=2,
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temperature=0.3,
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top_k=50,
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top_p=0.95,
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early_stopping=True,
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pad_token_id=tokenizer.eos_token_id,
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)
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pipe(prompt)[0]['generated_text'].split('assistant')[1]
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#Claro! Aqui está uma sugestão de programação para o final de semana com sua esposa:
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#Domingo:
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#1. Despertar cedo para um café da manhã delicioso juntos.
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#2. Faça uma caminhada ou uma corrida no parque local para aproveitar o ar fresco e a natureza.
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#3. Depois do café da manhã, faça uma caminhada de compras para escolher algumas roupas ou acessórios novos.
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#4. Retorne para casa para preparar uma refeição deliciosa juntos.
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#5. Depois do almoço, você pode assistir a um filme ou jogar jogos de tabuleiro para relaxar
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```
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If you are having a memory problem such as "CUDA Out of memory", you should use 4-bit or 8-bit quantization.
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For the complete model in colab you will need the A100.
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If you want to use 4bits or 8bits, T4 or L4 will already solve the problem.
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# 4bits example
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```python
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from transformers import BitsAndBytesConfig
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import torch
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nb_4bit_config = BitsAndBytesConfig(
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load_in_4bit=True,
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bnb_4bit_quant_type="nf4",
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bnb_4bit_compute_dtype=torch.bfloat16,
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bnb_4bit_use_double_quant=True
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)
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model = AutoModelForCausalLM.from_pretrained(
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base_model,
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quantization_config=bnb_config,
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device_map={"": 0}
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)
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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/heleno-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>
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