recogna-nlp
/

Bode-3.1-8B-Instruct-lora

@@ -1,202 +1,72 @@
----
-base_model: meta-llama/Meta-Llama-3.1-8B-Instruct
-library_name: peft
----
-# Model Card for Model ID
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-### Framework versions
-- PEFT 0.12.0

+Recomendamos fortemente que utilizem o Kaggle com GPU. Você pode usar o Bode facilmente com a biblioteca Transformers do HuggingFace. Entretanto, é necessário ter a autorização de acesso ao [Llama-3.1-8B-Instruct](https://huggingface.co/meta-llama/Llama-3.1-8B-Instruct).
+Abaixo, colocamos um exemplo simples de como carregar o modelo e gerar texto:
+```python
+# Downloads necessários
+!pip install transformers
+!pip install einops accelerate bitsandbytes
+!pip install sentence_transformers
+!pip install git+https://github.com/huggingface/peft.git
+from peft import PeftModel
+from transformers import AutoModelForCausalLM, AutoTokenizer, GenerationConfig
+import transformers
+import torch
+from peft import PeftModel, PeftConfig
+llm_model = "recogna-nlp/Bode-3.1-8B-Instruct-lora"
+hf_auth = 'HF_KEY'
+config = PeftConfig.from_pretrained(llm_model)
+model = AutoModelForCausalLM.from_pretrained(config.base_model_name_or_path, trust_remote_code=True, return_dict=True, load_in_4bit=True, device_map='auto', token=hf_auth)
+tokenizer = AutoTokenizer.from_pretrained(config.base_model_name_or_path, token=hf_auth)
+model = PeftModel.from_pretrained(model, llm_model) # Caso ocorra o seguinte erro: "ValueError: We need an `offload_dir`... Você deve acrescentar o parâmetro: offload_folder="./offload_dir".
+model.eval()
+#Testando geração de texto
+def generate_prompt(instruction, input=None):
+    if input:
+        return f"""Abaixo está uma instrução que descreve uma tarefa, juntamente com uma entrada que fornece mais contexto. Escreva uma resposta que complete adequadamente o pedido.
+### Instrução:
+{instruction}
+### Entrada:
+{input}
+### Resposta:"""
+    else:
+        return f"""Abaixo está uma instrução que descreve uma tarefa. Escreva uma resposta que complete adequadamente o pedido.
+### Instrução:
+{instruction}
+### Resposta:"""
+generation_config = GenerationConfig(
+    num_beams=2,
+    do_sample=False,
+    pad_token_id=tokenizer.eos_token_id
+)
+def evaluate(instruction, input=None):
+    prompt = generate_prompt(instruction, input)
+    inputs = tokenizer(prompt, return_tensors="pt")
+    input_ids = inputs["input_ids"].cuda()
+    attention_mask = inputs["attention_mask"].cuda()
+    generation_output = model.generate(
+        input_ids=input_ids,
+        attention_mask=attention_mask,
+        generation_config=generation_config,
+        return_dict_in_generate=True,
+        output_scores=True,
+        max_length=800,
+    )
+    for s in generation_output.sequences:
+        output = tokenizer.decode(s, skip_special_tokens=True)
+        print("Resposta:", output.split("### Resposta:")[1].strip())
+evaluate("Faça uma função em python de multiplicação entre 3 valores")
+```