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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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- ## 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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- ### 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 [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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  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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+ license: apache-2.0
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+ datasets:
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+ - rhaymison/superset
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+ language:
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+ - pt
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+ pipeline_tag: text-generation
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+ base_model: meta-llama/Meta-Llama-3-8B-Instruct
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  ---
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+ # Mistral-portuguese-luana-7b-chat
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+ <p align="center">
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+ <img src="https://raw.githubusercontent.com/rhaymisonbetini/huggphotos/main/llama3-luana.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 290,000 chat in Portuguese.
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+ The model comes to help fill the gap in models in Portuguese. Tuned from the Mistral 7b in Portuguese, the model was adjusted mainly for chat.
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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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+ model = AutoModelForCausalLM.from_pretrained("rhaymison/Llama3-portuguese-luana-8b-instruct", device_map= {"": 0})
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+ tokenizer = AutoTokenizer.from_pretrained("rhaymison/Llama3-portuguese-luana-8b-instruct")
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+ model.eval()
 
 
 
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+ ```
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+ You can use with Pipeline.
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+ ```python
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+ from transformers import pipeline
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+ stop_token = "<|eot_id|>"
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+ stop_token_id = tokenizer.encode(stop_token)[0]
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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=256,
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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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+ eos_token_id=stop_token_id,
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+ pad_token_id=tokenizer.eos_token_id,
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+ )
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+ def format_dataset(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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+ return f"""<|begin_of_text|><|start_header_id|>system<|end_header_id|>
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+ { system_prompt }<|eot_id|><|start_header_id|>user<|end_header_id|>
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+ { question }<|eot_id|><|start_header_id|>assistant<|end_header_id|>"""
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+ prompt = format_dataset("Me explique quem eram os Romanos")
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+ result = pipe(prompt)
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+ result[0]["generated_text"].split("assistant<|end_header_id|>")[1]
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+ #Os romanos eram um povo antigo que habitava a península italiana, particularmente na região que hoje é conhecida como Itália. Eles estabeleceram o Império Romano,
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+ #que se tornou uma das maiores e mais poderosas civilizações da história. Os romanos eram conhecidos por suas conquistas militares, sua arquitetura e engenharia
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+ #impressionantes e sua influência duradoura na cultura ocidental.
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+ #Os romanos eram uma sociedade complexa que consistia em várias classes sociais, incluindo senadores, cavaleiros, plebeus e escravos.
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+ #Eles tinham um sistema de governo baseado em uma república, onde o poder era dividido entre o Senado e a Assembléia do Povo.
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+ #Os romanos eram conhecidos por suas conquistas militares, que os levaram a expandir seu império por toda a Europa, Ásia e África.
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+ #Eles estabeleceram uma rede de estradas, pontes e outras estruturas que facilitaram a comunicação e o comércio.
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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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+ <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>