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+---
+base_model: rhaymison/phi-3-portuguese-tom-cat-4k-instruct
+datasets:
+- rhaymison/superset
+inference: true
+language:
+- pt
+library_name: transformers
+license: apache-2.0
+model-index:
+- name: phi-3-portuguese-tom-cat-4k-instruct
+  results:
+  - dataset:
+      args:
+        num_few_shot: 3
+      name: ENEM Challenge (No Images)
+      split: train
+      type: eduagarcia/enem_challenge
+    metrics:
+    - name: accuracy
+      type: acc
+      value: 61.58
+    source:
+      name: Open Portuguese LLM Leaderboard
+      url: https://huggingface.co/spaces/eduagarcia/open_pt_llm_leaderboard?query=rhaymison/phi-3-portuguese-tom-cat-4k-instruct
+    task:
+      name: Text Generation
+      type: text-generation
+  - dataset:
+      args:
+        num_few_shot: 3
+      name: BLUEX (No Images)
+      split: train
+      type: eduagarcia-temp/BLUEX_without_images
+    metrics:
+    - name: accuracy
+      type: acc
+      value: 50.63
+    source:
+      name: Open Portuguese LLM Leaderboard
+      url: https://huggingface.co/spaces/eduagarcia/open_pt_llm_leaderboard?query=rhaymison/phi-3-portuguese-tom-cat-4k-instruct
+    task:
+      name: Text Generation
+      type: text-generation
+  - dataset:
+      args:
+        num_few_shot: 3
+      name: OAB Exams
+      split: train
+      type: eduagarcia/oab_exams
+    metrics:
+    - name: accuracy
+      type: acc
+      value: 43.69
+    source:
+      name: Open Portuguese LLM Leaderboard
+      url: https://huggingface.co/spaces/eduagarcia/open_pt_llm_leaderboard?query=rhaymison/phi-3-portuguese-tom-cat-4k-instruct
+    task:
+      name: Text Generation
+      type: text-generation
+  - dataset:
+      args:
+        num_few_shot: 15
+      name: Assin2 RTE
+      split: test
+      type: assin2
+    metrics:
+    - name: f1-macro
+      type: f1_macro
+      value: 91.54
+    source:
+      name: Open Portuguese LLM Leaderboard
+      url: https://huggingface.co/spaces/eduagarcia/open_pt_llm_leaderboard?query=rhaymison/phi-3-portuguese-tom-cat-4k-instruct
+    task:
+      name: Text Generation
+      type: text-generation
+  - dataset:
+      args:
+        num_few_shot: 15
+      name: Assin2 STS
+      split: test
+      type: eduagarcia/portuguese_benchmark
+    metrics:
+    - name: pearson
+      type: pearson
+      value: 75.27
+    source:
+      name: Open Portuguese LLM Leaderboard
+      url: https://huggingface.co/spaces/eduagarcia/open_pt_llm_leaderboard?query=rhaymison/phi-3-portuguese-tom-cat-4k-instruct
+    task:
+      name: Text Generation
+      type: text-generation
+  - dataset:
+      args:
+        num_few_shot: 15
+      name: FaQuAD NLI
+      split: test
+      type: ruanchaves/faquad-nli
+    metrics:
+    - name: f1-macro
+      type: f1_macro
+      value: 47.46
+    source:
+      name: Open Portuguese LLM Leaderboard
+      url: https://huggingface.co/spaces/eduagarcia/open_pt_llm_leaderboard?query=rhaymison/phi-3-portuguese-tom-cat-4k-instruct
+    task:
+      name: Text Generation
+      type: text-generation
+  - dataset:
+      args:
+        num_few_shot: 25
+      name: HateBR Binary
+      split: test
+      type: ruanchaves/hatebr
+    metrics:
+    - name: f1-macro
+      type: f1_macro
+      value: 83.01
+    source:
+      name: Open Portuguese LLM Leaderboard
+      url: https://huggingface.co/spaces/eduagarcia/open_pt_llm_leaderboard?query=rhaymison/phi-3-portuguese-tom-cat-4k-instruct
+    task:
+      name: Text Generation
+      type: text-generation
+  - dataset:
+      args:
+        num_few_shot: 25
+      name: PT Hate Speech Binary
+      split: test
+      type: hate_speech_portuguese
+    metrics:
+    - name: f1-macro
+      type: f1_macro
+      value: 70.19
+    source:
+      name: Open Portuguese LLM Leaderboard
+      url: https://huggingface.co/spaces/eduagarcia/open_pt_llm_leaderboard?query=rhaymison/phi-3-portuguese-tom-cat-4k-instruct
+    task:
+      name: Text Generation
+      type: text-generation
+  - dataset:
+      args:
+        num_few_shot: 25
+      name: tweetSentBR
+      split: test
+      type: eduagarcia/tweetsentbr_fewshot
+    metrics:
+    - name: f1-macro
+      type: f1_macro
+      value: 57.78
+    source:
+      name: Open Portuguese LLM Leaderboard
+      url: https://huggingface.co/spaces/eduagarcia/open_pt_llm_leaderboard?query=rhaymison/phi-3-portuguese-tom-cat-4k-instruct
+    task:
+      name: Text Generation
+      type: text-generation
+model_creator: rhaymison
+model_name: phi-3-portuguese-tom-cat-4k-instruct
+pipeline_tag: text-generation
+quantized_by: afrideva
+tags:
+- portugues
+- portuguese
+- QA
+- instruct
+- phi
+- gguf
+- ggml
+- quantized
+---
+
+# phi-3-portuguese-tom-cat-4k-instruct-GGUF
+
+Quantized GGUF model files for [phi-3-portuguese-tom-cat-4k-instruct](https://huggingface.co/rhaymison/phi-3-portuguese-tom-cat-4k-instruct) from [rhaymison](https://huggingface.co/rhaymison)
+
+## Original Model Card:
+
+# Phi-3-portuguese-tom-cat-4k-instruct
+
+<p align="center">
+  <img src="https://raw.githubusercontent.com/rhaymisonbetini/huggphotos/main/tom-cat.webp"  width="50%" style="margin-left:'auto' margin-right:'auto' display:'block'"/>
+</p>
+
+
+This model was trained with a superset of 300,000 instructions in Portuguese. 
+The model comes to help fill the gap in models in Portuguese. Tuned from the   microsoft/Phi-3-mini-4k.
+
+# How to use
+
+### FULL MODEL : A100
+### HALF MODEL: L4
+### 8bit or 4bit : T4 or V100
+
+You can use the model in its normal form up to 4-bit quantization. Below we will use both approaches.
+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. 
+Important points like these help models (even smaller models like 4b) to perform much better.
+
+```python
+!pip install -q -U transformers
+!pip install -q -U accelerate
+!pip install -q -U bitsandbytes
+
+from transformers import AutoModelForCausalLM, AutoTokenizer, TextStreamer
+model = AutoModelForCausalLM.from_pretrained("rhaymison/phi-3-portuguese-tom-cat-4k-instruct", device_map= {"": 0})
+tokenizer = AutoTokenizer.from_pretrained("rhaymison/phi-3-portuguese-tom-cat-4k-instruct")
+model.eval()
+
+```
+
+You can use with Pipeline.
+```python
+
+from transformers import pipeline
+pipe = pipeline("text-generation",
+                model=model,
+                tokenizer=tokenizer,
+                do_sample=True,
+                max_new_tokens=512,
+                num_beams=2,
+                temperature=0.3,
+                top_k=50,
+                top_p=0.95,
+                early_stopping=True,
+                pad_token_id=tokenizer.eos_token_id,
+                )
+
+
+def format_template(question:str):
+    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."
+    return f"""<s><|system|>
+    { system_prompt }
+    <|user|>
+    { question }
+    <|assistant|>
+    """
+
+question = format_template("E possivel ir de Carro dos Estados unidos ate o japão")
+pipe(question)
+```
+
+If you are having a memory problem such as "CUDA Out of memory", you should use 4-bit or 8-bit quantization.
+For the complete model in colab you will need the A100.
+If you want to use 4bits or 8bits, T4 or L4 will already solve the problem.
+
+# 4bits example
+
+```python
+from transformers import BitsAndBytesConfig
+import torch
+nb_4bit_config = BitsAndBytesConfig(
+    load_in_4bit=True,
+    bnb_4bit_quant_type="nf4",
+    bnb_4bit_compute_dtype=torch.bfloat16,
+    bnb_4bit_use_double_quant=True
+)
+
+model = AutoModelForCausalLM.from_pretrained(
+    base_model,
+    quantization_config=bnb_config,
+    device_map={"": 0}
+)
+
+```
+
+# Open Portuguese LLM Leaderboard Evaluation Results  
+
+Detailed results can be found [here](https://huggingface.co/datasets/eduagarcia-temp/llm_pt_leaderboard_raw_results/tree/main/rhaymison/phi-3-portuguese-tom-cat-4k-instruct) and on the [🚀 Open Portuguese LLM Leaderboard](https://huggingface.co/spaces/eduagarcia/open_pt_llm_leaderboard)
+
+|          Metric          |  Value  |
+|--------------------------|---------|
+|Average                   |**64.57**|
+|ENEM Challenge (No Images)|    61.58|
+|BLUEX (No Images)         |    50.63|
+|OAB Exams                 |    43.69|
+|Assin2 RTE                |    91.54|
+|Assin2 STS                |    75.27|
+|FaQuAD NLI                |    47.46|
+|HateBR Binary             |    83.01|
+|PT Hate Speech Binary     |    70.19|
+|tweetSentBR               |    57.78|
+
+
+### Comments
+
+Any idea, help or report will always be welcome.
+
+email: rhaymisoncristian@gmail.com
+
+ <div style="display:flex; flex-direction:row; justify-content:left">
+    <a href="https://www.linkedin.com/in/rhaymison-cristian-betini-2b3016175/" target="_blank">
+    <img src="https://img.shields.io/badge/LinkedIn-0077B5?style=for-the-badge&logo=linkedin&logoColor=white">
+  </a>
+  <a href="https://github.com/rhaymisonbetini" target="_blank">
+    <img src="https://img.shields.io/badge/GitHub-100000?style=for-the-badge&logo=github&logoColor=white">
+  </a>