meta-llama-3-8b.ipynb

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   "source": [
    "**Meta introduced and launched the Meta Llama 3** series of large language models (LLMs), comprising pretrained and instruction-tuned generative text models in sizes of 8B and 70B. The instruction-tuned Llama 3 models are specifically tailored for dialogue applications, showcasing superior performance compared to numerous open-source chat models across prevalent industry benchmarks."
   ]
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   "source": [
    "##### huggingface token \n",
    "from kaggle_secrets import UserSecretsClient\n",
    "user_secrets = UserSecretsClient()\n",
    "hf_token = user_secrets.get_secret(\"hf_token\")"
   ]
  },
  {
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    {
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     "text": [
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      "\u001b[?25hInstalling collected packages: tokenizers, bitsandbytes, accelerate, transformers\r\n",
      "  Attempting uninstall: tokenizers\r\n",
      "    Found existing installation: tokenizers 0.15.2\r\n",
      "    Uninstalling tokenizers-0.15.2:\r\n",
      "      Successfully uninstalled tokenizers-0.15.2\r\n",
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      "    Found existing installation: accelerate 0.28.0\r\n",
      "    Uninstalling accelerate-0.28.0:\r\n",
      "      Successfully uninstalled accelerate-0.28.0\r\n",
      "  Attempting uninstall: transformers\r\n",
      "    Found existing installation: transformers 4.39.3\r\n",
      "    Uninstalling transformers-4.39.3:\r\n",
      "      Successfully uninstalled transformers-4.39.3\r\n",
      "Successfully installed accelerate-0.29.3 bitsandbytes-0.43.1 tokenizers-0.19.1 transformers-4.40.0\r\n"
     ]
    }
   ],
   "source": [
    "#installing library\n",
    "!pip install -U transformers accelerate bitsandbytes "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ad4f1551",
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    "tags": []
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   "source": [
    "# ***### Kernal restart recommended after installing library ***"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "4e4d6b60",
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2024-04-19 03:58:06.894902: E external/local_xla/xla/stream_executor/cuda/cuda_dnn.cc:9261] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n",
      "2024-04-19 03:58:06.895032: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:607] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n",
      "2024-04-19 03:58:07.027511: E external/local_xla/xla/stream_executor/cuda/cuda_blas.cc:1515] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n"
     ]
    }
   ],
   "source": [
    "##importing library\n",
    "from transformers import (AutoTokenizer,AutoModelForCausalLM,\n",
    "                          TextStreamer,pipeline,BitsAndBytesConfig)\n",
    "import transformers\n",
    "import torch\n",
    "model_id = \"meta-llama/Meta-Llama-3-8B-Instruct\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "d89e4942",
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     "exception": false,
     "start_time": "2024-04-19T03:58:17.949091",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
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     "metadata": {},
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    {
     "data": {
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    {
     "data": {
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     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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     "metadata": {},
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    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "9724225cff684702a3561b17f14ddcc8",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained.\n"
     ]
    }
   ],
   "source": [
    "pipeline = transformers.pipeline(\n",
    "    \"text-generation\",\n",
    "    model=model_id,token=hf_token,\n",
    "    model_kwargs={\n",
    "        \"torch_dtype\": torch.float16,\n",
    "        \"quantization_config\": {\"load_in_4bit\": True},\n",
    "        \"low_cpu_mem_usage\": True,\n",
    "    },\n",
    ")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "6df86f94",
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     "exception": false,
     "start_time": "2024-04-19T04:00:03.521389",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "terminators = [\n",
    "    pipeline.tokenizer.eos_token_id,\n",
    "    pipeline.tokenizer.convert_tokens_to_ids(\"<|eot_id|>\")\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "8760e63b",
   "metadata": {
    "execution": {
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    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "####### prompt template\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "e1a778e9",
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    "_cell_guid": "b1076dfc-b9ad-4769-8c92-a6c4dae69d19",
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   "source": [
    "def get_inference(prompt=\"\",max_new_tokens=256,temperature=0.7,top_p=0.9):\n",
    "    sys_pmt = \"Your task is to generate a brief summary of a given text, providing the main points and key details while maintaining coherence and clarity. The summary should be concise and capture the essence of the original text accurately.\"\n",
    "    messages = [\n",
    "    {\"role\": \"system\", \"content\": sys_pmt},\n",
    "    {\"role\": \"user\", \"content\": prompt},\n",
    "    ]\n",
    "    prompt = pipeline.tokenizer.apply_chat_template(\n",
    "        messages, \n",
    "        tokenize=False, \n",
    "        add_generation_prompt=True\n",
    ")\n",
    "    outputs = pipeline(\n",
    "        prompt,\n",
    "        max_new_tokens=max_new_tokens,\n",
    "        eos_token_id=terminators,\n",
    "        do_sample=True,\n",
    "        temperature=temperature,\n",
    "        top_p=top_p,\n",
    "    )\n",
    "    return outputs[0][\"generated_text\"][len(prompt):]"
   ]
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     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Setting `pad_token_id` to `eos_token_id`:128001 for open-end generation.\n",
      "/opt/conda/lib/python3.10/site-packages/bitsandbytes/nn/modules.py:426: UserWarning: Input type into Linear4bit is torch.float16, but bnb_4bit_compute_dtype=torch.float32 (default). This will lead to slow inference or training speed.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Photosynthesis is the process by which plants, algae, and some bacteria convert light energy from the sun into chemical energy in the form of organic compounds, such as glucose. This process occurs in specialized organelles called chloroplasts, which contain the pigment chlorophyll. Chlorophyll absorbs light energy, which is then used to power the conversion of carbon dioxide and water into glucose and oxygen. Oxygen is released as a byproduct, while glucose is used by the plant to fuel its metabolic processes. Photosynthesis is essential for life on Earth, as it provides the energy and organic compounds necessary to support the food chain.\n"
     ]
    }
   ],
   "source": [
    "result = get_inference(\"Write a short summary about photosynthesis.\")\n",
    "print(result.strip())"
   ]
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