waitig for LLAMA aproval .-.

LLAMA-Romantica.ipynb

@@ -0,0 +1,140 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from transformers import AutoTokenizer\n",
+    "import transformers\n",
+    "import torch\n",
+    "\n",
+    "model = \"meta-llama/Llama-2-7b-chat-hf\" # meta-llama/Llama-2-7b-chat-hf\n",
+    "\n",
+    "tokenizer = AutoTokenizer.from_pretrained(model, use_auth_token=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from transformers import pipeline\n",
+    "\n",
+    "llama_pipeline = pipeline(\n",
+    "    \"text-generation\",  # LLM task\n",
+    "    model=model,\n",
+    "    torch_dtype=torch.float16,\n",
+    "    device_map=\"auto\",\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "SYSTEM_PROMPT = \"\"\"<s>[INST] <<SYS>>\n",
+    "You are a helpful bot. Your answers are clear and concise.\n",
+    "<</SYS>>\n",
+    "\n",
+    "\"\"\"\n",
+    "\n",
+    "# Formatting function for message and history\n",
+    "def format_message(message: str, history: list, memory_limit: int = 3) -> str:\n",
+    "    \"\"\"\n",
+    "    Formats the message and history for the Llama model.\n",
+    "\n",
+    "    Parameters:\n",
+    "        message (str): Current message to send.\n",
+    "        history (list): Past conversation history.\n",
+    "        memory_limit (int): Limit on how many past interactions to consider.\n",
+    "\n",
+    "    Returns:\n",
+    "        str: Formatted message string\n",
+    "    \"\"\"\n",
+    "    # always keep len(history) <= memory_limit\n",
+    "    if len(history) > memory_limit:\n",
+    "        history = history[-memory_limit:]\n",
+    "\n",
+    "    if len(history) == 0:\n",
+    "        return SYSTEM_PROMPT + f\"{message} [/INST]\"\n",
+    "\n",
+    "    formatted_message = SYSTEM_PROMPT + f\"{history[0][0]} [/INST] {history[0][1]} </s>\"\n",
+    "\n",
+    "    # Handle conversation history\n",
+    "    for user_msg, model_answer in history[1:]:\n",
+    "        formatted_message += f\"<s>[INST] {user_msg} [/INST] {model_answer} </s>\"\n",
+    "\n",
+    "    # Handle the current message\n",
+    "    formatted_message += f\"<s>[INST] {message} [/INST]\"\n",
+    "\n",
+    "    return formatted_message"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Generate a response from the Llama model\n",
+    "def get_llama_response(message: str, history: list) -> str:\n",
+    "    \"\"\"\n",
+    "    Generates a conversational response from the Llama model.\n",
+    "\n",
+    "    Parameters:\n",
+    "        message (str): User's input message.\n",
+    "        history (list): Past conversation history.\n",
+    "\n",
+    "    Returns:\n",
+    "        str: Generated response from the Llama model.\n",
+    "    \"\"\"\n",
+    "    query = format_message(message, history)\n",
+    "    response = \"\"\n",
+    "\n",
+    "    sequences = llama_pipeline(\n",
+    "        query,\n",
+    "        do_sample=True,\n",
+    "        top_k=10,\n",
+    "        num_return_sequences=1,\n",
+    "        eos_token_id=tokenizer.eos_token_id,\n",
+    "        max_length=1024,\n",
+    "    )\n",
+    "\n",
+    "    generated_text = sequences[0]['generated_text']\n",
+    "    response = generated_text[len(query):]  # Remove the prompt from the output\n",
+    "\n",
+    "    print(\"Chatbot:\", response.strip())\n",
+    "    return response.strip()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import gradio as gr\n",
+    "\n",
+    "gr.ChatInterface(get_llama_response).launch()\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "itam",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "name": "python",
+   "version": "3.11.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

app.py

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+# import gradio as gr
+
+# gr.ChatInterface(get_llama_response).launch()
+
+import gradio as gr
+
+def greet(name):
+    return "Hello " + name + "!!"
+
+iface = gr.Interface(fn=greet, inputs="text", outputs="text")
+iface.launch()