rhaymison
/

portuguese-tom-cat-13b

@@ -1,199 +1,106 @@
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 library_name: transformers
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 ---
+language:
+- pt
+license: apache-2.0
 library_name: transformers
+tags:
+- portugues
+- portuguese
+- QA
+- instruct
+- phi
+base_model: meta-llama/Llama-2-13b-chat-hf
+datasets:
+- rhaymison/superset
+pipeline_tag: text-generation
 ---
+# portuguese-tom-cat-13b
+<p align="center">
+  <img src="https://raw.githubusercontent.com/rhaymisonbetini/huggphotos/main/13b.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 Llama-2-13b-chat-hf
+# 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/portuguese-tom-cat-13b", device_map= {"": 0})
+tokenizer = AutoTokenizer.from_pretrained("rhaymison/portuguese-tom-cat-13b")
+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,
+                )
+```
+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}
+)
+```
+### 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>