Update README.md

README.md

@@ -13,13 +13,25 @@ tags:
 - text-generation-inference
 ---
 
-###  Qwen2.5-7B-PT-BR-Instruct
+###  Amadeus-Verbo-Qwen2.5-7B-PT-BR-Instruct
 #### Introduction
-Qwen2.5-7B-PT-BR-Instruct is a Brazilian-Portuguese language model (PT-BR-LLM) developed from the base model Qwen2.5-7B by fine-tuning, for 2 epochs, with the superset instruction dataset.
+Amadeus-Verbo-Qwen2.5-7B-PT-BR-Instruct is a Brazilian-Portuguese language model (PT-BR-LLM) developed from the base model Qwen2.5-7B through fine-tuning, for 2 epochs, with the superset instruction dataset.
+Read our article [here](https://www.).
+
+## Details
+
+- **Architecture:** a Transformer-based model with RoPE, SwiGLU, RMSNorm, and Attention QKV bias pre-trained via Causal Language Modeling
+- **Parameters:** 7.62B parameters
+- **Number of Parameters (Non-Embedding):** 6.53B
+- **Number of Layers:** 28
+- **Number of Attention Heads (GQA):** 28 for Q and 4 for KV
+- **Context length:** 131,072 tokens
+- **Number of steps:** 78838
+- **Language:** Brazilian Portuguese
 
 #### Usage
 
-You can use Qwen2.5-7B-PT-BR-Instruct with the latest HuggingFace Transformers library and we advise you to use the latest version of transformers.
+You can use Amadeus-Verbo-Qwen2.5-7B-PT-BR-Instruct with the latest HuggingFace Transformers library and we advise you to use the latest version of transformers.
 
 With transformers<4.37.0, you will encounter the following error:
 
@@ -28,6 +40,55 @@ KeyError: 'qwen2'
 Below, we have provided a simple example of how to load the model and generate text:
 
 #### Quickstart 
+The following code snippet uses apply_chat_template to show how to load the tokenizer, the model, and how to generate content.
+
+Using the pipeline:
+```python
+from transformers import pipeline
+
+messages = [
+    {"role": "user", "content": "Faça uma planilha nutricional para uma alimentação fitness e mediterrânea com todos os dias da semana"},
+]
+pipe = pipeline("text-generation", model="amadeusai/qwen2.5-7B-PT-BR-Instruct")
+pipe(messages)
+```
+
+Using the `AutoTokenizer` and `AutoModelForCausalLM`:
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+model_name = "amadeusai/qwen2.5-7B-PT-BR-Instruct"
+
+model = AutoModelForCausalLM.from_pretrained(
+    model_name,
+    torch_dtype="auto",
+    device_map="auto"
+)
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+
+prompt = "Faça uma planilha nutricional para uma alimentação fitness e mediterrânea com todos os dias da semana."
+messages = [
+    {"role": "system", "content": "Você é um assistente útil."},
+    {"role": "user", "content": prompt}
+]
+text = tokenizer.apply_chat_template(
+    messages,
+    tokenize=False,
+    add_generation_prompt=True
+)
+model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
+
+generated_ids = model.generate(
+    **model_inputs,
+    max_new_tokens=512
+)
+generated_ids = [
+    output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
+]
+
+response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
+
+```
 
 #### Citation