rhaymison
/

portuguese-Phi3-Tom-Cat-128k-instruct

@@ -150,200 +150,97 @@ model-index:
       name: Open Portuguese LLM Leaderboard
 ---
-# Model Card for Model ID
-<!-- Provide a quick summary of what the model is/does. -->
-## Model Details
-### Model Description
-<!-- Provide a longer summary of what this model is. -->
-This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
-- **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]
-### Model Sources [optional]
-<!-- Provide the basic links for the model. -->
-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
-## Uses
-<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
-### Direct Use
-<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
-[More Information Needed]
-### Downstream Use [optional]
-<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
-[More Information Needed]
-### Out-of-Scope Use
-<!-- 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]
 # Open Portuguese LLM Leaderboard Evaluation Results
@@ -363,3 +260,17 @@ Detailed results can be found [here](https://huggingface.co/datasets/eduagarcia-
 |PT Hate Speech Binary     |    65.76|
 |tweetSentBR               |    53.32|

       name: Open Portuguese LLM Leaderboard
 ---
+# Phi-3-portuguese-tom-cat-128k-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
+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
 |PT Hate Speech Binary     |    65.76|
 |tweetSentBR               |    53.32|
+### Comments
+Any idea, help or report will always be welcome.
+email: [email protected]
+ <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>