add application

app.py

@@ -1,7 +1,88 @@
+from transformers import AutoModelForCausalLM, AutoTokenizer
+from huggingface_hub import login
+import torch
 import gradio as gr
+import os
+import spaces
 
-def greet(name):
-    return "Hello " + name + "!!"
+token = os.environ.get("HF_TOKEN_READ")
+login(token)
 
-demo = gr.Interface(fn=greet, inputs="text", outputs="text")
-demo.launch()
+model_id = "meta-llama/Llama-3.2-1B-Instruct"
+model = AutoModelForCausalLM.from_pretrained(
+  model_id,
+  torch_dtype=torch.bfloat16,
+  token=token
+)
+
+tokenizer = AutoTokenizer.from_pretrained(model_id)
+
+if torch.cuda.is_available():
+    device = torch.device("cuda")
+    print(f"Usando GPU: {torch.cuda.get_device_name(device)}")
+else:
+    device = torch.device("cpu")
+    print("Usando CPU")
+
+model = model.to(device)
+
+@spaces.GPU
+def respond(
+        message,
+        history,
+        system_message,
+        max_tokens,
+        temperature,
+        top_p
+):
+    messages = [{"role": "system", "content": system_message}]
+
+    for val in history:
+        if val[0]:
+            messages.append({"role": "user", "content": val[0]})
+        if val[1]:
+            messages.append({"role": "assistant", "content": val[1]})
+
+    messages.append({"role": "user", "content": message})
+
+    input_ids = tokenizer.apply_chat_template(
+        messages,
+        add_generation_prompt=True,
+        return_tensors='pt'
+    ).to(model.device)
+
+    terminators = [
+        tokenizer.eos_token_id,
+        tokenizer.convert_tokens_to_ids("<|eot_id|>")
+    ]
+
+    outputs = model.generate(
+        input_ids,
+        max_new_tokens=max_tokens,
+        eos_token_id=terminators,
+        do_sample=True,
+        temperature=temperature,
+        top_p=top_p
+    )
+
+    response = ""
+
+    for message in tokenizer.decode(
+        outputs[0][input_ids.shape[-1]:],
+        skip_special_tokens=True
+    ):
+        response += message
+        yield response
+
+demo = gr.ChatInterface(
+    respond,
+    additional_inputs=[
+        gr.Textbox(value="Tu eres un asistente amigable", label="System Message"),
+        gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens"),
+        gr.Slider(minimum=0.1, maximum=3, value=0.7, step=0.1, label="Temperature"),
+        gr.Slider(minimum=0.1, maximum=1, value=0.95, step=0.05, label="Top p")
+    ]
+)
+
+if __name__ == "__main__":
+    demo.launch()

requirements.txt

@@ -0,0 +1,5 @@
+torch
+transformers
+gradio
+huggingface_hub
+spaces