Update README.md

README.md

@@ -2,16 +2,8 @@
 library_name: transformers
 tags: []
 pipeline_tag: text-generation
-inference: 
-  parameters:
-    do_sample: true
-    repetition_penalty: 1.0
-    top_k: 50 
-    top_p: 0.95
-    temperature: 0.2
-    num_return_sequences: 1
 widget:
-- text: Me conte sobra a ida do homem a Lua.
+- text: Me conte sobre a ida do homem a Lua.
   example_title: Homem na Lua.
 - text: Fale sobre uma curiosidade sobre a história do mundo
   example_title: História.
@@ -19,199 +11,94 @@ widget:
   example_title: Escreva um poema.
 ---
 
-# Model Card for Model ID
 
-<!-- Provide a quick summary of what the model is/does. -->
+## Model description
+The Cuscuz-com-gemma 2b is a model derived from a fine tuning of the google/gemma-2b-it. This model was tuned to be specialized in the Northeast region of Brazil. 
+The model was specialized in a dataset that covered historical, geographical, economic, cultural and culinary issues in the northeast region.
+To make better use of the Cuscuz, the ideal is to use the model without quantization. This model is a small version of Cuscuz-7b whit another arquiteture
 
+## How to Use
 
 
-## Model Details
+```python
+from transformers import AutoTokenizer, pipeline
+import torch
 
-### Model Description
+model = "rhaymison/cuscuz-com-gemma-2b"
 
-<!-- Provide a longer summary of what this model is. -->
+tokenizer = AutoTokenizer.from_pretrained(model)
+pipeline = pipeline(
+    "text-generation",
+    model=model,
+    model_kwargs={"torch_dtype": torch.bfloat16},
+    device="cuda",
+)
 
-This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
+messages = [
+    {"role": "user", "content": "Me conte sobre o estado de Sergipe."},
+]
+prompt = pipeline.tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
+outputs = pipeline(
+    prompt,
+    max_new_tokens=256,
+    do_sample=True,
+    temperature=0.7,
+    top_k=50,
+    top_p=0.95
+)
+print(outputs[0]["generated_text"][len(prompt):].replace("model",""))
 
-- **Developed by:** [More Information Needed]
-- **Funded by [optional]:** [More Information Needed]
-- **Shared by [optional]:** [More Information Needed]
-- **Model type:** [More Information Needed]
-- **Language(s) (NLP):** [More Information Needed]
-- **License:** [More Information Needed]
-- **Finetuned from model [optional]:** [More Information Needed]
+#Sergipe é o menor estado do Nordeste brasileiro em extensão territorial. 
+#O estado de Sergipe é conhecido por suas praias, sua culinária à base de frutos do mar e sua importância histórica na produção de açúcar. 
+#Sergipe teve papel fundamental na produção de açúcar no Brasil colonial, sendo uma das regiões onde se concentraram os engenhos de açúcar.
+```
 
-### Model Sources [optional]
 
-<!-- Provide the basic links for the model. -->
+```python
+from transformers import AutoTokenizer, AutoModelForCausalLM
 
-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
+tokenizer2 = AutoTokenizer.from_pretrained("cuscuz-com-gemma-2b")
+model2 = AutoModelForCausalLM.from_pretrained("cuscuz-com-gemma-2b", 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"
+```
 
-## Uses
+```python
 
-<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
+text = f"""
+Você é um assistente especialista em história do Nordeste Brasileiro.
+Você sempre responde de forma clara e educada e sempre com informações
+verdadeiras. Responda com detalhes e riquesas de informação
+<start_of_turn>Me conte sobre o Folclore Nordestino?<end_of_turn>
+<start_of_turn>model"""
 
-### Direct Use
+device = "cuda:0"
 
-<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
+inputs = tokenizer2(text, return_tensors="pt").to(device)
 
-[More Information Needed]
+outputs = model2.generate(**inputs, max_new_tokens=100, do_sample=False)
 
-### Downstream Use [optional]
+output = tokenizer2.decode(outputs[0], skip_special_tokens=True, skip_prompt=True)
+print(output.replace("model"," "))
 
-<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
+#O Folclore Nordestino é uma parte importante da nossa cultura, com manifestações como o bumba meu boi, o reisado, o maracatu e o repente.
+#Essa história é rica em lendas, contarorias e tradições que são passadas de geração em geração.  
+```
 
-[More Information Needed]
+### Comments
 
-### Out-of-Scope Use
+Any idea, help or report will always be welcome.
 
-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
-
-[More Information Needed]
-
-## Bias, Risks, and Limitations
-
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
-
-[More Information Needed]
-
-### Recommendations
-
-<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
-
-Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
-
-## How to Get Started with the Model
-
-Use the code below to get started with the model.
-
-[More Information Needed]
-
-## Training Details
-
-### Training Data
-
-<!-- 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. -->
-
-[More Information Needed]
-
-### Training Procedure 
-
-<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
-
-#### Preprocessing [optional]
-
-[More Information Needed]
-
-
-#### Training Hyperparameters
-
-- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
-
-#### Speeds, Sizes, Times [optional]
-
-<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
-
-[More Information Needed]
-
-## Evaluation
-
-<!-- This section describes the evaluation protocols and provides the results. -->
-
-### Testing Data, Factors & Metrics
-
-#### Testing Data
-
-<!-- This should link to a Dataset Card if possible. -->
-
-[More Information Needed]
-
-#### Factors
-
-<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
-
-[More Information Needed]
-
-#### Metrics
-
-<!-- These are the evaluation metrics being used, ideally with a description of why. -->
-
-[More Information Needed]
-
-### Results
-
-[More Information Needed]
-
-#### Summary
-
-
-
-## Model Examination [optional]
-
-<!-- Relevant interpretability work for the model goes here -->
-
-[More Information Needed]
-
-## Environmental Impact
-
-<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
-
-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).
-
-- **Hardware Type:** [More Information Needed]
-- **Hours used:** [More Information Needed]
-- **Cloud Provider:** [More Information Needed]
-- **Compute Region:** [More Information Needed]
-- **Carbon Emitted:** [More Information Needed]
-
-## Technical Specifications [optional]
-
-### Model Architecture and Objective
-
-[More Information Needed]
-
-### Compute Infrastructure
-
-[More Information Needed]
-
-#### Hardware
-
-[More Information Needed]
-
-#### Software
-
-[More Information Needed]
-
-## Citation [optional]
-
-<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
-
-**BibTeX:**
-
-[More Information Needed]
-
-**APA:**
-
-[More Information Needed]
-
-## Glossary [optional]
-
-<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
-
-[More Information Needed]
-
-## More Information [optional]
-
-[More Information Needed]
-
-## Model Card Authors [optional]
-
-[More Information Needed]
-
-## Model Card Contact
-
-[More Information Needed]
+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>
+  <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>
+ </div>