Update app.py

app.py

@@ -1,64 +1,64 @@
 import gradio as gr
-from huggingface_hub import InferenceClient
-
-"""
-For more information on `huggingface_hub` Inference API support, please check the docs: https://huggingface.co/docs/huggingface_hub/v0.22.2/en/guides/inference
-"""
-# client = InferenceClient("HuggingFaceTB/SmolLM2-1.7B-Instruct")
-client = InferenceClient("nvidia/Hymba-1.5B-Instruct")
-
-def respond(
-    message,
-    history: list[tuple[str, str]],
-    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})
-
-    response = ""
-
-    for message in client.chat_completion(
-        messages,
-        max_tokens=max_tokens,
-        stream=True,
-        temperature=temperature,
-        top_p=top_p,
-    ):
-        token = message.choices[0].delta.content
-
-        response += token
-        yield response
-
-
-"""
-For information on how to customize the ChatInterface, peruse the gradio docs: https://www.gradio.app/docs/chatinterface
-"""
-demo = gr.ChatInterface(
-    respond,
-    additional_inputs=[
-        gr.Textbox(value="You are a friendly Chatbot.", label="System message"),
-        gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens"),
-        gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature"),
-        gr.Slider(
-            minimum=0.1,
-            maximum=1.0,
-            value=0.95,
-            step=0.05,
-            label="Top-p (nucleus sampling)",
-        ),
-    ],
+
+from transformers import AutoModelForCausalLM, AutoTokenizer, StopStringCriteria, StoppingCriteriaList
+import torch
+
+import torch._dynamo
+torch._dynamo.config.suppress_errors = True
+
+import os
+# os.system("nvidia-smi")
+# print("TORCH_CUDA", torch.cuda.is_available())
+
+
+print("loading model")
+# Load the tokenizer and model
+repo_name = "nvidia/Hymba-1.5B-Instruct"
+# repo_name = "HuggingFaceTB/SmolLM2-1.7B-Instruct"
+
+tokenizer = AutoTokenizer.from_pretrained(repo_name, trust_remote_code=True)
+model = AutoModelForCausalLM.from_pretrained(repo_name, trust_remote_code=True)
+
+model = model.cuda().to(torch.bfloat16)
+
+print("model is loaded")
+
+
+# Chat with Hymba
+# prompt = input()
+prompt = "Who are you?"
+
+messages = [
+    {"role": "system", "content": "You are a helpful assistant."}
+]
+messages.append({"role": "user", "content": prompt})
+
+# Apply chat template
+tokenized_chat = tokenizer.apply_chat_template(messages, tokenize=True, add_generation_prompt=True, return_tensors="pt").to('cuda')
+stopping_criteria = StoppingCriteriaList([StopStringCriteria(tokenizer=tokenizer, stop_strings="</s>")])
+
+print("generating prompt")
+
+
+outputs = model.generate(
+    tokenized_chat, 
+    max_new_tokens=256,
+    do_sample=False,
+    temperature=0.7,
+    use_cache=True,
+    stopping_criteria=stopping_criteria
 )
+input_length = tokenized_chat.shape[1]
+response = tokenizer.decode(outputs[0][input_length:], skip_special_tokens=True)
+
+print(f"Model response: {response}")
+
+
 
+def greet(name):
+    print(f"User: prompt")
+    print(f"Model response: {response}")
+    # return "Hello " + name + "!!"
 
-if __name__ == "__main__":
-    demo.launch()
+demo = gr.Interface(fn=greet, inputs="text", outputs="text")
+demo.launch()