---
language:
- pt
license: apache-2.0
library_name: transformers
tags:
- portuguese
- brasil
- gemma
- portugues
- instrucao
base_model: google/gemma-2b-it
datasets:
- rhaymison/superset
pipeline_tag: text-generation
model-index:
- name: gemma-portuguese-tom-cat-2b-it
  results:
  - task:
      type: text-generation
      name: Text Generation
    dataset:
      name: ENEM Challenge (No Images)
      type: eduagarcia/enem_challenge
      split: train
      args:
        num_few_shot: 3
    metrics:
    - type: acc
      value: 27.71
      name: accuracy
    source:
      url: https://huggingface.co./spaces/eduagarcia/open_pt_llm_leaderboard?query=rhaymison/gemma-portuguese-tom-cat-2b-it
      name: Open Portuguese LLM Leaderboard
  - task:
      type: text-generation
      name: Text Generation
    dataset:
      name: BLUEX (No Images)
      type: eduagarcia-temp/BLUEX_without_images
      split: train
      args:
        num_few_shot: 3
    metrics:
    - type: acc
      value: 29.07
      name: accuracy
    source:
      url: https://huggingface.co./spaces/eduagarcia/open_pt_llm_leaderboard?query=rhaymison/gemma-portuguese-tom-cat-2b-it
      name: Open Portuguese LLM Leaderboard
  - task:
      type: text-generation
      name: Text Generation
    dataset:
      name: OAB Exams
      type: eduagarcia/oab_exams
      split: train
      args:
        num_few_shot: 3
    metrics:
    - type: acc
      value: 27.97
      name: accuracy
    source:
      url: https://huggingface.co./spaces/eduagarcia/open_pt_llm_leaderboard?query=rhaymison/gemma-portuguese-tom-cat-2b-it
      name: Open Portuguese LLM Leaderboard
  - task:
      type: text-generation
      name: Text Generation
    dataset:
      name: Assin2 RTE
      type: assin2
      split: test
      args:
        num_few_shot: 15
    metrics:
    - type: f1_macro
      value: 46.84
      name: f1-macro
    source:
      url: https://huggingface.co./spaces/eduagarcia/open_pt_llm_leaderboard?query=rhaymison/gemma-portuguese-tom-cat-2b-it
      name: Open Portuguese LLM Leaderboard
  - task:
      type: text-generation
      name: Text Generation
    dataset:
      name: Assin2 STS
      type: eduagarcia/portuguese_benchmark
      split: test
      args:
        num_few_shot: 15
    metrics:
    - type: pearson
      value: 14.06
      name: pearson
    source:
      url: https://huggingface.co./spaces/eduagarcia/open_pt_llm_leaderboard?query=rhaymison/gemma-portuguese-tom-cat-2b-it
      name: Open Portuguese LLM Leaderboard
  - task:
      type: text-generation
      name: Text Generation
    dataset:
      name: FaQuAD NLI
      type: ruanchaves/faquad-nli
      split: test
      args:
        num_few_shot: 15
    metrics:
    - type: f1_macro
      value: 29.39
      name: f1-macro
    source:
      url: https://huggingface.co./spaces/eduagarcia/open_pt_llm_leaderboard?query=rhaymison/gemma-portuguese-tom-cat-2b-it
      name: Open Portuguese LLM Leaderboard
  - task:
      type: text-generation
      name: Text Generation
    dataset:
      name: HateBR Binary
      type: ruanchaves/hatebr
      split: test
      args:
        num_few_shot: 25
    metrics:
    - type: f1_macro
      value: 46.59
      name: f1-macro
    source:
      url: https://huggingface.co./spaces/eduagarcia/open_pt_llm_leaderboard?query=rhaymison/gemma-portuguese-tom-cat-2b-it
      name: Open Portuguese LLM Leaderboard
  - task:
      type: text-generation
      name: Text Generation
    dataset:
      name: PT Hate Speech Binary
      type: hate_speech_portuguese
      split: test
      args:
        num_few_shot: 25
    metrics:
    - type: f1_macro
      value: 45.36
      name: f1-macro
    source:
      url: https://huggingface.co./spaces/eduagarcia/open_pt_llm_leaderboard?query=rhaymison/gemma-portuguese-tom-cat-2b-it
      name: Open Portuguese LLM Leaderboard
  - task:
      type: text-generation
      name: Text Generation
    dataset:
      name: tweetSentBR
      type: eduagarcia/tweetsentbr_fewshot
      split: test
      args:
        num_few_shot: 25
    metrics:
    - type: f1_macro
      value: 18.86
      name: f1-macro
    source:
      url: https://huggingface.co./spaces/eduagarcia/open_pt_llm_leaderboard?query=rhaymison/gemma-portuguese-tom-cat-2b-it
      name: Open Portuguese LLM Leaderboard
---

# gemma-portuguese-tom-cat-2b-it


<p align="center">
  <img src="https://raw.githubusercontent.com/rhaymisonbetini/huggphotos/main/tom-cat-2b.webp"  width="50%" style="margin-left:'auto' margin-right:'auto' display:'block'"/>
</p>



## Model description

updated: 2024-04-10 20:06

The gemma-portuguese-tom-cat-2b-it model is a portuguese model trained with the superset dataset with 250,000 instructions. 
The model is mainly focused on text generation and instruction.
The model was not trained on math and code tasks.
The model is generalist with focus on understand portuguese inferences. 
With this fine tuning for portuguese, you can adjust the model for a specific field.

## How to Use


```python
from transformers import AutoTokenizer, pipeline
import torch

model = "rhaymison/gemma-portuguese-tom-cat-2b-it"

tokenizer = AutoTokenizer.from_pretrained(model)
pipeline = pipeline(
    "text-generation",
    model=model,
    model_kwargs={"torch_dtype": torch.bfloat16},
    device="cuda",
)

messages = [
   {
      "role": "system",
      "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."
    },
    {"role": "user", "content": "Me conte sobre a ida do homem a Lua."},
]
prompt = pipeline.tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
outputs = pipeline(
    prompt,
    max_new_tokens=256,
    do_sample=True,
    temperature=0.2,
    top_k=50,
    top_p=0.95
)
```


```python
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer2 = AutoTokenizer.from_pretrained("rhaymison/gemma-portuguese-tom-cat-2b-it")
model2 = AutoModelForCausalLM.from_pretrained("rhaymison/gemma-portuguese-tom-cat-2b-it", device_map={"":0})
tokenizer2.pad_token = tokenizer2.eos_token
tokenizer2.add_eos_token = True
tokenizer2.add_bos_token, tokenizer2.add_eos_token
tokenizer2.padding_side = "right"
```

```python
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."
    text = f"""<bos>system
    {system_prompt}<end_of_turn>
    <start_of_turn>user
    ###instrução: {question} <end_of_turn>
    <start_of_turn>model"""
    return text

question = format_template("Me conte sobre a ida do homem a Lua")

device = "cuda:0"

inputs = tokenizer2(text, return_tensors="pt").to(device)

outputs = model2.generate(**inputs, max_new_tokens=256, do_sample=False)

output = tokenizer2.decode(outputs[0], skip_special_tokens=True, skip_prompt=True)
print(output.replace("model"," "))
```

### 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/heleno-betini-2b3016175/" target="_blank">
    <img src="https://img.shields.io/badge/LinkedIn-0077B5?style=for-the-badge&logo=linkedin&logoColor=white">
  </a>
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  </a>
 </div>


# 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/gemma-portuguese-tom-cat-2b-it) and on the [🚀 Open Portuguese LLM Leaderboard](https://huggingface.co./spaces/eduagarcia/open_pt_llm_leaderboard)

|          Metric          |  Value  |
|--------------------------|---------|
|Average                   |**31.76**|
|ENEM Challenge (No Images)|    27.71|
|BLUEX (No Images)         |    29.07|
|OAB Exams                 |    27.97|
|Assin2 RTE                |    46.84|
|Assin2 STS                |    14.06|
|FaQuAD NLI                |    29.39|
|HateBR Binary             |    46.59|
|PT Hate Speech Binary     |    45.36|
|tweetSentBR               |    18.86|