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  library_name: transformers
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- tags: []
 
 
 
 
 
 
 
 
 
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  ---
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- # Model Card for Model ID
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- <!-- Provide a quick summary of what the model is/does. -->
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- ## Model Details
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- ### Model Description
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- <!-- Provide a longer summary of what this model is. -->
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- This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
 
 
 
 
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- - **Developed by:** [More Information Needed]
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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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- ### Model Sources [optional]
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- <!-- Provide the basic links for the model. -->
 
 
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- - **Repository:** [More Information Needed]
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- - **Paper [optional]:** [More Information Needed]
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- - **Demo [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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- ### Direct Use
 
 
 
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- <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
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- [More Information Needed]
 
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- ### Downstream Use [optional]
 
 
 
 
 
 
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- <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
 
 
 
 
 
 
 
 
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- [More Information Needed]
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- ### Out-of-Scope Use
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- <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
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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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-
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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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-
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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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- #### 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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- ### Compute Infrastructure
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- #### Hardware
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- #### Software
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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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- **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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- ## Model Card Authors [optional]
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- ## Model Card Contact
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- [More Information Needed]
 
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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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+ - portuguese
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+ - brasil
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+ - gemma
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+ - portugues
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+ - instrucao
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+ datasets:
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+ - rhaymison/superset
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+ base_model: google/gemma-2b-it
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+ pipeline_tag: text-generation
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  ---
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+ # gemma-portuguese-2b-luana
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+ <p align="center">
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+ <img src="https://raw.githubusercontent.com/rhaymisonbetini/huggphotos/main/tom-cat-2b.webp" width="50%" style="margin-left:'auto' margin-right:'auto' display:'block'"/>
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+ </p>
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+ ## Model description
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+ updated: 2024-04-10 20:06
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+ The gemma-portuguese-2b model is a portuguese model trained with the superset dataset with 250,000 instructions.
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+ The model is mainly focused on text generation and instruction.
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+ The model was not trained on math and code tasks.
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+ The model is generalist with focus on understand portuguese inferences.
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+ With this fine tuning for portuguese, you can adjust the model for a specific field.
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+ ## How to Use
 
 
 
 
 
 
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+ ```python
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+ from transformers import AutoTokenizer, pipeline
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+ import torch
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+ model = "rhaymison/gemma-portuguese-luana-2b"
 
 
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+ tokenizer = AutoTokenizer.from_pretrained(model)
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+ pipeline = pipeline(
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+ "text-generation",
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+ model=model,
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+ model_kwargs={"torch_dtype": torch.bfloat16},
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+ device="cuda",
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+ )
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+ messages = [
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+ {
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+ "role": "system",
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+ "content": "Abaixo está uma instrução que descreve uma tarefa, juntamente com uma entrada que fornece mais contexto. Escreva uma resposta que complete adequadamente o pedido."
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+ },
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+ {"role": "user", "content": "Me conte sobre a ida do homem a Lua."},
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+ ]
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+ prompt = pipeline.tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
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+ outputs = pipeline(
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+ prompt,
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+ max_new_tokens=256,
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+ do_sample=True,
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+ temperature=0.2,
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+ top_k=50,
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+ top_p=0.95
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+ )
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+ print(outputs[0]["generated_text"][len(prompt):].replace("model",""))
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+ #A viagem à Lua foi um esforço monumental realizado pela Agência Espacial dos EUA entre 1969 e 1972.
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+ #Foi um marco significativo na exploração espacial e na ciência humana.
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+ #Aqui está uma visão geral de sua jornada: 1. O primeiro voo espacial humano foi o de Yuri Gagarin, que voou a Terra em 12 de abril de 1961.
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+ ```
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+ ```python
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+ from transformers import AutoTokenizer, AutoModelForCausalLM
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+ tokenizer2 = AutoTokenizer.from_pretrained("rhaymison/gemma-portuguese-tom-cat-2b-it")
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+ model2 = AutoModelForCausalLM.from_pretrained("rhaymison/gemma-portuguese-tom-cat-2b-it", device_map={"":0})
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+ tokenizer2.pad_token = tokenizer2.eos_token
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+ tokenizer2.add_eos_token = True
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+ tokenizer2.add_bos_token, tokenizer2.add_eos_token
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+ tokenizer2.padding_side = "right"
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+ ```
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+ ```python
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+ def format_template( question:str):
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+ 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."
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+ text = f"""<bos>system
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+ {system_prompt}<end_of_turn>
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+ <start_of_turn>user
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+ ###instrução: {question} <end_of_turn>
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+ <start_of_turn>model"""
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+ return text
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+ question = format_template("Me conte sobre a ida do homem a Lua")
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+ device = "cuda:0"
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+ inputs = tokenizer2(text, return_tensors="pt").to(device)
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+ outputs = model2.generate(**inputs, max_new_tokens=256, do_sample=False)
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+ output = tokenizer2.decode(outputs[0], skip_special_tokens=True, skip_prompt=True)
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+ print(output.replace("model"," "))
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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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+ </div>