Upload README.md

README.md

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+---
+
+datasets:
+- dominguesm/alpaca-data-pt-br
+library_name: adapter-transformers
+pipeline_tag: text-generation
+language:
+- pt
+- en
+thumbnail: https://huggingface.co/Bruno/Harpia-7b-guanacoLora/blob/main/har.png
+---
+https://huggingface.co/Bruno/Harpia-7b-guanacoLora/blob/main/har.png
+
+<div style="text-align:center;width:250;height:250;">
+    <img src="https://huggingface.co/Bruno/Harpia-7b-guanacoLora/blob/main/har.png" alt="Harpia logo"">
+</div>
+
+<!-- This model card has been generated automatically according to the information the Trainer had access to. You
+should probably proofread and complete it, then remove this comment. -->
+
+# Harpia
+
+## Adapter Description
+This adapter was created with the [PEFT](https://github.com/huggingface/peft) library and allowed the base model **Falcon-7b** to be fine-tuned on the **https://huggingface.co/datasets/dominguesm/alpaca-data-pt-br** by using the method **QLoRA**.
+
+## Model description
+
+[Falcon 7B](https://huggingface.co/tiiuae/falcon-7b)
+
+## Intended uses & limitations
+
+TBA
+
+## Training and evaluation data
+
+TBA
+
+
+### Training results
+
+
+### How to use
+```py
+from peft import PeftModel, PeftConfig
+from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig, GenerationConfig
+
+peft_model_id = "Bruno/Caramelo_7B"
+
+config = PeftConfig.from_pretrained(peft_model_id)
+bnb_config = BitsAndBytesConfig(
+    load_in_4bit=True,
+    bnb_4bit_quant_type="nf4",
+    bnb_4bit_compute_dtype=torch.float16,
+)
+
+tokenizer = AutoTokenizer.from_pretrained(peft_model_id)
+
+model = AutoModelForCausalLM.from_pretrained(config.base_model_name_or_path,
+                                             return_dict=True,
+                                             quantization_config=bnb_config, 
+                                             trust_remote_code=True, 
+                                             device_map={"": 0})
+prompt_input = "Abaixo está uma declaração que descreve uma tarefa, juntamente com uma entrada que fornece mais contexto. Escreva uma resposta que conclua corretamente a solicitação.\n\n ### Instrução:\n{instruction}\n\n### Entrada:\n{input}\n\n### Resposta:\n"
+prompt_no_input = "Abaixo está uma instrução que descreve uma tarefa. Escreva uma resposta que conclua corretamente a solicitação.\n\n### Instrução:\n{instruction}\n\n### Resposta:\n"
+
+def create_prompt(instruction, input=None):
+    if input:
+        return prompt_input.format(instruction=instruction, input=input)
+    else:
+        return prompt_no_input.format(instruction=instruction)
+
+def generate(
+        instruction,
+        input=None,
+        max_new_tokens=128,
+        temperature=0.1,
+        top_p=0.75,
+        top_k=40,
+        num_beams=4,
+        repetition_penalty=1.5,
+        max_length=512
+):
+    prompt = create_prompt(instruction, input)
+    inputs = tokenizer.encode_plus(prompt, return_tensors="pt", truncation=True, max_length=max_length, padding="longest")
+    input_ids = inputs["input_ids"].to("cuda")
+    attention_mask = inputs["attention_mask"].to("cuda")
+
+    generation_output = model.generate(
+        input_ids=input_ids,
+        attention_mask=attention_mask,
+        max_length=max_length,
+        pad_token_id=tokenizer.pad_token_id,
+        eos_token_id=tokenizer.eos_token_id,
+        temperature=temperature,
+        top_p=top_p,
+        top_k=top_k,
+        num_beams=num_beams,
+        repetition_penalty=repetition_penalty,
+        length_penalty=0.8,
+        early_stopping=True,
+        output_scores=True,
+        return_dict_in_generate=True
+    )
+
+    output = tokenizer.decode(generation_output.sequences[0], skip_special_tokens=True)
+    return output.split("### Resposta:")[1]
+    
+instruction = "como faço um bolo de cenoura?"
+print("instruction:", instruction)
+print("Resposta:", generate(instruction))
+
+
+### Framework versions
+
+- Transformers 4.30.0.dev0
+- Pytorch 2.0.1+cu118
+- Datasets 2.12.0
+- Tokenizers 0.13.3