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marcoorasch
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llama-3.2-3B-instruct-hatespeech-offensive-classification

Text Generation
Transformers
Safetensors
English
llama
hatespeech
offensive
classification
conversational
text-generation-inference
Model card Files Community
llama-3.2-3B-instruct-hatespeech-offensive-classification
File size: 3,165 Bytes 
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{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "b368a208-7b0f-4928-aad6-94030a47d573",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "6d72bc7458d64ec7af180321e7d9d7aa",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Loading checkpoint shards:   0%|          | 0/2 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "###load models\n",
    "base_model = \"meta-llama/Llama-3.2-3B-Instruct\"\n",
    "fine_tuned_model = \"/home/marco/llama-3.2-instruct-offensive-classification-1.0.0\"\n",
    "\n",
    "from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline\n",
    "from peft import PeftModel\n",
    "import torch\n",
    "\n",
    "\n",
    "# Reload tokenizer and model\n",
    "tokenizer = AutoTokenizer.from_pretrained(fine_tuned_model)\n",
    "\n",
    "model = AutoModelForCausalLM.from_pretrained(\n",
    "        fine_tuned_model,\n",
    "        return_dict=True,\n",
    "        low_cpu_mem_usage=True,\n",
    "        torch_dtype=torch.float16,\n",
    "        device_map=\"auto\",\n",
    "        trust_remote_code=True,\n",
    "        offload_buffers=True\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "54e39123-1ed6-4990-8295-6df1e0563fc5",
   "metadata": {},
   "outputs": [],
   "source": [
    "text = \"You are a pig!\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "1b68121f-3215-46f6-901b-406be4e05a06",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Device set to use cpu\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Offensive\n"
     ]
    }
   ],
   "source": [
    "###Start Prompt\n",
    "prompt = f\"\"\"Classify the text into Hatespeech, Offensive, Normal and return the answer as the corresponding label.\n",
    "text: {text}\n",
    "label: \"\"\".strip()\n",
    "\n",
    "pipe = pipeline(\n",
    "    \"text-generation\",\n",
    "    model=model,\n",
    "    tokenizer=tokenizer,\n",
    "    torch_dtype=torch.float16,\n",
    "    device_map=\"auto\"\n",
    ")\n",
    "\n",
    "outputs = pipe(prompt, max_new_tokens=2, do_sample=True, temperature=0.1, pad_token_id=tokenizer.eos_token_id)\n",
    "print(outputs[0][\"generated_text\"].split(\"label: \")[-1].strip())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d709317d-b9cf-4590-9caf-ac74842f6be2",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.12.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}