Update README.md

README.md

@@ -17,6 +17,57 @@ tags:
 - **License:** apache-2.0
 - **Finetuned from model :** unsloth/llama-3-8b-bnb-4bit
 
+**Código para descarga:**
+El siguiente es el código sugerido para descargar el modelo usando Unslot:
+
+```
+import torch
+from unsloth import FastLanguageModel
+model, tokenizer = FastLanguageModel.from_pretrained(
+    model_name = "jjsprockel/Patologia_lora_model1", # YOUR MODEL YOU USED FOR TRAINING
+    max_seq_length = 2048, # Choose any! Llama 3 is up to 8k
+    dtype = None,
+    load_in_4bit = True,
+    )
+
+FastLanguageModel.for_inference(model)
+
+alpaca_prompt = """Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request.
+
+### Instruction:
+{}
+
+### Input:
+{}
+
+### Response:
+{}"""
+
+```
+
+**Código para la inferencia:**
+
+# Get the instruction from the console
+El siguiente codigo demuestra como se puede llevar a cabo la inferencia.
+
+```
+instruction = input("Ingresa la pregunta que tengas de Patología: ")
+
+inputs = tokenizer(
+[
+    alpaca_prompt.format(
+        instruction, # instruction
+        "", # input
+        "", # output - leave this blank for generation!
+    )
+], return_tensors = "pt").to("cuda")
+
+from transformers import TextStreamer
+text_streamer = TextStreamer(tokenizer)
+_ = model.generate(**inputs, streamer = text_streamer, max_new_tokens = 2048)
+
+```
+
 This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
 
 [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)