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library_name: transformers
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- **Funded by [optional]:** [More Information Needed]
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- **Shared by [optional]:** [More Information Needed]
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- **Model type:** [More Information Needed]
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- **Language(s) (NLP):** [More Information Needed]
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- **License:** [More Information Needed]
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- **Finetuned from model [optional]:** [More Information Needed]
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- **Demo [optional]:** [More Information Needed]
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## Uses
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[More Information Needed]
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###
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[More Information Needed]
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## Bias, Risks, and Limitations
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<!-- This section is meant to convey both technical and sociotechnical limitations. -->
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[More Information Needed]
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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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## 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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library_name: transformers
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license: apache-2.0
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datasets:
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- rhaymison/mental-health-qa
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language:
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- pt
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pipeline_tag: text-generation
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base_model: rhaymison/Mistral-portuguese-luana-7b
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tags:
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- health
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- portuguese
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# luana-portuguese-7b-mental-health
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Luana Mental health is a flipped model of the Luana-7b based on the Mistral 7b architecture.
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The model was adjusted to address topics such as depression, problems at work, mental health, problems with studies, drugs and others.
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# How to use
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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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model = AutoModelForCausalLM.from_pretrained("rhaymison/luana-portuguese-7b-health", device_map= {"": 0})
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tokenizer = AutoTokenizer.from_pretrained("rhaymison/luana-portuguese-7b-health")
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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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inputs = tokenizer([f"""<s>Aja como um conselheira de pessoas que estão passandos por dificuldades.
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Responda sempre de maneira educada e procure ajudar as pessoas.
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[INST] Olá, estou tendo muitos problemas pessoais, briga com meus pais. Tenho tido problemas no trabalho. Preciso de ajuda[/INST]"""], return_tensors="pt")
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streamer = TextStreamer(tokenizer, skip_prompt=True, skip_special_tokens=True)
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_ = model.generate(**inputs, streamer=streamer, max_new_tokens=200)
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#Olá, estou aqui para ajudá-lo. É normal enfrentar problemas pessoais, mas é importante buscar ajuda.
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#Vamos conversar sobre suas brigas com seus pais e como podemos resolver isso. Além disso, vamos falar sobre seus problemas no trabalho e como podemos encontrar
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#soluções para isso. Você não está sozinho, estou aqui para ajudá-lo. Vamos começar a encontrar maneiras de lidar com essas questões.
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#Você gostaria de começar falando sobre sua situação com seus pais?
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#Ou talvez queremos começar falando sobre seus problemas no trabalho? O que você acha melhor? Você pode me contar mais sobre isso?
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#Estou aqui para ajudá-lo.
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```
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# 4bits
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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: rhaymisoncristian@gmail.com
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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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