Improve language tag

README.md

@@ -1,135 +1,147 @@
----
-language:
-- pt
-metrics:
-- accuracy
-- f1
-- pearsonr
-base_model:
-- Qwen/Qwen2.5-7B-Instruct
-pipeline_tag: text-generation
-library_name: transformers
-tags:
-- text-generation-inference
-license: apache-2.0
----
-
-###  Amadeus-Verbo-FI-Qwen2.5-7B-PT-BR-Instruct
-#### Introduction
-Amadeus-Verbo-FI-Qwen2.5-7B-PT-BR-Instruct is a Brazilian-Portuguese language model (PT-BR-LLM) developed from the base model Qwen2.5-7B-Instruct through fine-tuning, for 2 epochs, with 600k instructions 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:** 7.61B 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 Amadeus-Verbo-FI-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-FI-Qwen2.5-7B-PT-BR-Instruct")
-pipe(messages)
-```
-OR
-```python
-from transformers import AutoModelForCausalLM, AutoTokenizer
-
-model_name = "amadeusai/AV-FI-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-FI-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}
-}
+---
+language:
+- zho
+- eng
+- fra
+- spa
+- por
+- deu
+- ita
+- rus
+- jpn
+- kor
+- vie
+- tha
+- ara
+metrics:
+- accuracy
+- f1
+- pearsonr
+base_model:
+- Qwen/Qwen2.5-7B-Instruct
+pipeline_tag: text-generation
+library_name: transformers
+tags:
+- text-generation-inference
+license: apache-2.0
+---
+
+###  Amadeus-Verbo-FI-Qwen2.5-7B-PT-BR-Instruct
+#### Introduction
+Amadeus-Verbo-FI-Qwen2.5-7B-PT-BR-Instruct is a Brazilian-Portuguese language model (PT-BR-LLM) developed from the base model Qwen2.5-7B-Instruct through fine-tuning, for 2 epochs, with 600k instructions 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:** 7.61B 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 Amadeus-Verbo-FI-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-FI-Qwen2.5-7B-PT-BR-Instruct")
+pipe(messages)
+```
+OR
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+model_name = "amadeusai/AV-FI-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-FI-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}
+}
 ```