notebooks/camoscio-lora.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "6ExAe1UCWn5E"
   },
   "source": [
    "# Parla con Camoscio\n",
    "\n",
    "This notebook contains minimal code for running [Alpaca-LoRA](https://github.com/tloen/alpaca-lora/) for demonstration purposes. Please check the repo for more details.\n",
    "The notebook is based in this another [notebook](https://colab.research.google.com/drive/1eWAmesrW99p7e1nah5bipn0zikMb8XYC)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "X_pz8MuY84Qh",
    "outputId": "e94db934-452c-4b1b-f61d-e4b3f0af1e88"
   },
   "outputs": [],
   "source": [
    "!pip install bitsandbytes==0.41.1\n",
    "!pip install -q datasets loralib sentencepiece\n",
    "!pip install -q transformers==4.28.0\n",
    "!pip install -q peft==0.5.0"
   ]
  },
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    "id": "VucO3HSMoJkz",
    "outputId": "03db26a9-c078-4a53-c129-f54c5f96adfa"
   },
   "outputs": [],
   "source": [
    "from peft import PeftModel\n",
    "from transformers import LlamaTokenizer, LlamaForCausalLM, GenerationConfig\n",
    "\n",
    "tokenizer = LlamaTokenizer.from_pretrained(\"linhvu/decapoda-research-llama-7b-hf\")\n",
    "model = LlamaForCausalLM.from_pretrained(\n",
    "    \"linhvu/decapoda-research-llama-7b-hf\",\n",
    "    load_in_8bit=True,\n",
    "    device_map=\"auto\",\n",
    ")\n",
    "model = PeftModel.from_pretrained(model, \"teelinsan/camoscio-7b-llama\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "w3_lzwcqermJ"
   },
   "outputs": [],
   "source": [
    "def generate_prompt(instruction, input=None):\n",
    "    if input:\n",
    "        return f\"\"\"Di seguito è riportata un'istruzione che descrive un task, insieme ad un input che fornisce un contesto più ampio. Scrivete una risposta che completi adeguatamente la richiesta.\n",
    "\n",
    "### Istruzione:\n",
    "{instruction}\n",
    "\n",
    "### Input:\n",
    "{input}\n",
    "\n",
    "### Risposta:\"\"\"\n",
    "    else:\n",
    "        return f\"\"\"Di seguito è riportata un'istruzione che descrive un task. Scrivete una risposta che completi adeguatamente la richiesta.\n",
    "\n",
    "### Istruzione:\n",
    "{instruction}\n",
    "\n",
    "### Risposta:\"\"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "Egh3beCVRpW5"
   },
   "outputs": [],
   "source": [
    "generation_config = GenerationConfig(\n",
    "    temperature=0.2,\n",
    "    top_p=0.75,\n",
    "    num_beams=4,\n",
    ")\n",
    "\n",
    "def evaluate(instruction, input=None):\n",
    "    prompt = generate_prompt(instruction, input)\n",
    "    inputs = tokenizer(prompt, return_tensors=\"pt\")\n",
    "    input_ids = inputs[\"input_ids\"].cuda()\n",
    "    generation_output = model.generate(\n",
    "        input_ids=input_ids,\n",
    "        generation_config=generation_config,\n",
    "        return_dict_in_generate=True,\n",
    "        output_scores=True,\n",
    "        max_new_tokens=256\n",
    "    )\n",
    "    for s in generation_output.sequences:\n",
    "        output = tokenizer.decode(s)\n",
    "        print(\"Risposta:\", output.split(\"### Risposta:\")[1].strip())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "evaluate(input(\"Istruzione: \"))"
   ]
  }
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