Update README.md

README.md

@@ -4,23 +4,116 @@ datasets:
 - timdettmers/openassistant-guanaco
 library_name: adapter-transformers
 pipeline_tag: text-generation
+language:
+- pt
+- en
 ---
 
-metadata
-tags:
-  - generated_from_trainer
-model-index:
-  - name: Harpia
-    results: []
-license: apache-2.0
-language:
-  - PT
-thumbnail: >-
-  https://huggingface.co/Bruno/Harpia-7b-guanacoLora/blob/main/download.jpeg
-Harpia logo
-Harpia
-
-Model description
-Falcon 7B
-Adapter Description
-This adapter was created with the PEFT library and allowed the base model Falcon-7b to be fine-tuned on the Guanaco dataset by using the method QLoRA.
+<div style="text-align:center;width:250px;height:250px;">
+    <img src="https://huggingface.co/Bruno/Harpia-7b-guanacoLora/blob/main/download.jpeg" alt="pomeranian 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 **timdettmers/openassistant-guanaco** 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
+import torch
+from peft import PeftModel, PeftConfig
+from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig, AutoTokenizer, GenerationConfig
+
+peft_model_id = "Bruno/Harpia-7b-guanacoLora"
+
+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 = ""
+prompt_no_input = ""
+
+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,
+        **kwargs,
+):
+    prompt = create_prompt(instruction, input)
+    inputs = tokenizer(prompt, return_tensors="pt")
+    input_ids = inputs["input_ids"].to("cuda")
+    attention_mask = inputs["attention_mask"].to("cuda")
+    generation_config = GenerationConfig(
+        temperature=temperature,
+        top_p=top_p,
+        top_k=top_k,
+        num_beams=num_beams,
+        **kwargs,
+    )
+    with torch.no_grad():
+        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_new_tokens=max_new_tokens
+        )
+    s = generation_output.sequences[0]
+    output = tokenizer.decode(s)
+    return output.split("### Respuesta:")[1]
+
+instruction = "Dime algo sobre los halcones"
+
+print("Instrucción:", instruction)
+print("Respuesta:", generate(instruction))
+```
+
+
+### Framework versions
+
+- Transformers 4.30.0.dev0
+- Pytorch 2.0.1+cu118
+- Datasets 2.12.0
+- Tokenizers 0.13.3