Update README.md

README.md

@@ -47,3 +47,108 @@ parameters:
 dtype: bfloat16
 
 ```
+
+#### Usage
+
+You can use Amadeus-Verbo--MI-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:
+
+KeyError: 'qwen2'
+
+Below, we have provided a simple example of how to load the model and generate text:
+
+#### Quickstart 
+The following code snippet uses `pipeline`, `AutoTokenizer`, `AutoModelForCausalLM` and 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/AV-MI-Qwen2.5-7B-PT-BR-Instruct")
+pipe(messages)
+```
+OR
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+model_name = "amadeusai/AV-MI-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]
+```
+OR
+```python
+from transformers import GenerationConfig, TextGenerationPipeline, AutoTokenizer, AutoModelForCausalLM
+import torch
+
+# Specify the model and tokenizer
+model_id = "amadeusai/AV-MI-Qwen2.5-7B-PT-BR-Instruct"
+tokenizer = AutoTokenizer.from_pretrained(model_id)
+model = AutoModelForCausalLM.from_pretrained(model_id)
+
+# Specify the generation parameters as you like
+generation_config = GenerationConfig(
+    **{
+    "do_sample": True,
+    "max_new_tokens": 512,
+    "renormalize_logits": True,
+    "repetition_penalty": 1.2,
+    "temperature": 0.1,
+    "top_k": 50,
+    "top_p": 1.0,
+    "use_cache": True, 
+  }
+)
+
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+generator = TextGenerationPipeline(model=model, task="text-generation", tokenizer=tokenizer, device=device)
+
+# Generate text
+prompt = "Faça uma planilha nutricional para uma alimentação fitness e mediterrânea com todos os dias da semana"
+completion = generator(prompt, generation_config=generation_config)
+print(completion[0]['generated_text'])
+```
+
+
+#### Citation
+
+If you find our work helpful, feel free to cite it.
+```
+@misc{Amadeus AI,
+    title = {Amadeus Verbo: A Brazilian Portuguese large language model.},
+    url = {https://amadeus-ai.com},
+    author = {Amadeus AI},
+    month = {November},
+    year = {2024}
+}
+```