Final.ipynb

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      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
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        "outputId": "9cc79455-374a-4bdc-b456-f493c2e79e6d"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "  Installing build dependencies ... \u001b[?25l\u001b[?25hdone\n",
            "  Getting requirements to build wheel ... \u001b[?25l\u001b[?25hdone\n",
            "  Preparing metadata (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
            "  Building wheel for transformers (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m119.8/119.8 MB\u001b[0m \u001b[31m8.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m309.4/309.4 kB\u001b[0m \u001b[31m30.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m222.7/222.7 MB\u001b[0m \u001b[31m6.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m43.2/43.2 kB\u001b[0m \u001b[31m5.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m21.3/21.3 MB\u001b[0m \u001b[31m75.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h"
          ]
        }
      ],
      "source": [
        " !pip -q install git+https://github.com/huggingface/transformers\n",
        "!pip -q install bitsandbytes accelerate\n",
        "# all of these libraries are used for speeding up the inference process"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 70
        },
        "id": "uzDsRhajth_X",
        "outputId": "48bfe0a0-09ae-4286-e6f2-c6f6a6693685"
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      "outputs": [
        {
          "data": {
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              "type": "string"
            },
            "text/plain": [
              "'\\n1. When loading the model normally with a 16-bit quantization, I encountered a two errors- a. CPU RAM consumed error and b. GPU shoots up to 15 gbs and then it says cuda out of memory\\n   To tackle this problem, firstly I loaded the model in 4-bit quantization and we also used device_map = \"auto\"\\n\\n2. We could have further sped up the inference process by using flash-attention, but currently it doesn\\'t support T4 gpus\\n'"
            ]
          },
          "execution_count": 2,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "\"\"\"\n",
        "1. When loading the model normally with a 16-bit quantization, I encountered a two errors- a. CPU RAM consumed error and b. GPU shoots up to 15 gbs and then it says cuda out of memory\n",
        "   To tackle this problem, firstly I loaded the model in 4-bit quantization and we also used device_map = \"auto\"\n",
        "\n",
        "2. We could have further sped up the inference process by using flash-attention, but currently it doesn't support T4 gpus\n",
        "\"\"\""
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 145,
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        },
        "id": "UxqEfCoQkmYj",
        "outputId": "0cf54aa4-df52-4f53-875b-fb79eab2a13b"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "099b2cbfdfc4431b8ff32f39d0d36eb3",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "VBox(children=(HTML(value='<center> <img\\nsrc=https://huggingface.co/front/assets/huggingface_logo-noborder.sv…"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "from huggingface_hub import notebook_login\n",
        "notebook_login()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "id": "7eSRiX4alQe3"
      },
      "outputs": [],
      "source": [
        "import torch\n",
        "import transformers\n",
        "from transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig\n",
        "import nest_asyncio\n",
        "import time\n",
        "import asyncio\n",
        "import textwrap"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "id": "ndxOknOKlTWk"
      },
      "outputs": [],
      "source": [
        "nest_asyncio.apply() # this is used to allow the notebook to run multiple event loops simultaneously"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "id": "_El7Oc2tlUf8"
      },
      "outputs": [],
      "source": [
        "quantization_config = BitsAndBytesConfig(\n",
        "        load_in_4bit=True,\n",
        "        bnb_4bit_quant_type=\"nf4\",\n",
        "        bnb_4bit_compute_dtype=torch.float16,\n",
        ")\n",
        "# the weights of the model are stored in 4-bit precision\n",
        "# but the computations are performed using 16-bit fp precision. This reduces the gpu ram required to store the model weights"
      ]
    },
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      "outputs": [
        {
          "name": "stderr",
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          "text": [
            "/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_token.py:89: UserWarning: \n",
            "The secret `HF_TOKEN` does not exist in your Colab secrets.\n",
            "To authenticate with the Hugging Face Hub, create a token in your settings tab (https://huggingface.co/settings/tokens), set it as secret in your Google Colab and restart your session.\n",
            "You will be able to reuse this secret in all of your notebooks.\n",
            "Please note that authentication is recommended but still optional to access public models or datasets.\n",
            "  warnings.warn(\n"
          ]
        },
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        {
          "data": {
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              "model_id": "3e693fdcabce4efaa3aeb68aa5b45078",
              "version_major": 2,
              "version_minor": 0
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          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "daa17564cfd54d2fa3048f9d82f09ed7",
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          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "9f4fc45c66e34ef2b89100700336f3e3",
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            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "5d78610480f8458baeb0c53377b61687",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
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            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "model = AutoModelForCausalLM.from_pretrained(\"mistralai/Mistral-7B-Instruct-v0.2\", quantization_config=quantization_config, device_map=\"auto\")\n",
        "# device_map param allows the transformers library to automatically distribute the model's layers across the avilable hardware to improve performance\n",
        "tokenizer = AutoTokenizer.from_pretrained(\"mistralai/Mistral-7B-Instruct-v0.2\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "b5BIyfZ6rQs1",
        "outputId": "187a24dd-8419-4d40-f38c-e6a104e2f7f0"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "' uncomment and run this cell to include lora adaptation. After running the module with lora, the model weigths took ~2.4 gbs of gpu ram.\\n    And when we load the model without lora, it takes around 6 gbs '"
            ]
          },
          "execution_count": 9,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "\"\"\" uncomment and run this cell to include lora adaptation. After running the module with lora, the model weigths took ~2.4 gbs of gpu ram.\n",
        "    And when we load the model without lora, it takes around ~6 gbs \"\"\"\n",
        "# from peft import LoraConfig, get_peft_model, PeftModel\n",
        "# lora_config = LoraConfig(\n",
        "#     r=8, # rank of the low-rank matrices introduced by LoRA \n",
        "#     lora_alpha=16, # scaling factor for the low-rank matrices \n",
        "#     target_modules=[\"q_proj\", \"v_proj\"], # adapter targets query and value projection matrices\n",
        "#     lora_dropout=0.05,\n",
        "#     bias=\"none\",\n",
        "#     task_type=\"CAUSAL_LM\"\n",
        "# )\n",
        "# model = get_peft_model(model, lora_config)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "id": "oAq4vGizlat4"
      },
      "outputs": [],
      "source": [
        "tokenizer.pad_token = tokenizer.eos_token\n",
        "# idk why is this necessary but the model doesn't give any output without this step. It might be because the tokenizer is configured to use the EOS token as the padding token\n",
        "device = 'cuda'"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "id": "qmbD-bqKmoB-"
      },
      "outputs": [],
      "source": [
        "# a basic function to show the output in a more readable format\n",
        "def wrap_text(text, width=90):\n",
        "    lines = text.split('\\n')\n",
        "    wrapped_lines = [textwrap.fill(line, width=width) for line in lines]\n",
        "    wrapped_text = '\\n'.join(wrapped_lines)\n",
        "    return wrapped_text"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "id": "lVZPLUmAmpyT"
      },
      "outputs": [],
      "source": [
        "async def generate(input_texts, max_length=128):\n",
        "    inputs = tokenizer(input_texts, return_tensors=\"pt\", padding=True, truncation=True).to(device)\n",
        "    outputs = model.generate(**inputs, max_length=max_length, temperature=0.1, do_sample=True)\n",
        "    texts = tokenizer.batch_decode(outputs, skip_special_tokens=True)\n",
        "    wrapped_texts = [wrap_text(text) for text in texts]\n",
        "    for text in wrapped_texts:\n",
        "        print(text)\n",
        "    return wrapped_texts"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "id": "ZRrUDqRYmrfp"
      },
      "outputs": [],
      "source": [
        "# we initialize the model with a few iterations of generating text based on a seed text, this helps the model to generate accurate responses\n",
        "# https://medium.com/better-ml/model-warmup-8e9681ef4d41\n",
        "def warmup_model(model, tokenizer, text, num_iterations=5):\n",
        "    for _ in range(num_iterations):\n",
        "        inputs = tokenizer(text, return_tensors=\"pt\", add_special_tokens=False).to(device)\n",
        "        model.generate(**inputs, max_new_tokens=100)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "id": "M-MOyc5emt4X"
      },
      "outputs": [],
      "source": [
        "# this function calculates the throughput of our model for a specific set of constraints\n",
        "async def benchmark_model(model, tokenizer, prompts, input_tokens, output_tokens):\n",
        "    inputs = tokenizer(prompts, return_tensors=\"pt\", max_length=input_tokens, padding=True, truncation=True).to(device)\n",
        "\n",
        "    start_time = time.time()\n",
        "    outputs = model.generate(**inputs, max_length=output_tokens)\n",
        "    end_time = time.time()\n",
        "\n",
        "    total_time = end_time - start_time\n",
        "    total_tokens = (input_tokens + output_tokens) * len(prompts)\n",
        "    throughput = total_tokens / total_time\n",
        "\n",
        "    print(f'Average time per inference: {total_time/len(prompts):.4f} seconds')\n",
        "    print(f'Throughput: {throughput:.2f} tokens/sec')\n",
        "    return throughput"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "id": "K45CZlkYmvng"
      },
      "outputs": [],
      "source": [
        "async def main_concurrent(model, tokenizer, concurrency=32):\n",
        "    warmup_prompt = \"<s>[INST] Warm-up prompt to prepare the model. [/INST]\" # seed text to warm up the model\n",
        "    benchmark_prompt = \"<s>[INST] Please provide a summary of the latest news. [/INST]\" # we use this text to test all the functions and check the throughput on a single prompt\n",
        "\n",
        "    warmup_model(model, tokenizer, warmup_prompt)\n",
        "\n",
        "    print(f'Benchmarking on predefined prompt:\\n{benchmark_prompt}\\n')\n",
        "    await benchmark_model(model, tokenizer, [benchmark_prompt]*concurrency, input_tokens=128, output_tokens=128)\n",
        "\n",
        "    # then we ask user for prompts; number of prompts here is equal to the number of concurrent tasks that we want out model to do\n",
        "    input_prompts = []\n",
        "    for i in range(concurrency):\n",
        "        prompt = input(f\"Enter prompt {i+1}/{concurrency}: \")\n",
        "        input_prompts.append(prompt)\n",
        "\n",
        "    print('Benchmarking on user provided prompts:')\n",
        "    await benchmark_model(model, tokenizer, input_prompts, input_tokens=128, output_tokens=128)\n",
        "\n",
        "    print('Generating responses for user provided prompts:')\n",
        "    await generate(input_prompts)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RSZyy2mFmxzL",
        "outputId": "15e5e184-6edf-4f79-e7c8-777f6089ad29"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n",
            "Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n",
            "Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n",
            "Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n",
            "Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n",
            "Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Benchmarking on predefined prompt:\n",
            "<s>[INST] Please provide a summary of the latest news. [/INST]\n",
            "\n",
            "Average time per inference: 0.8335 seconds\n",
            "Throughput: 307.12 tokens/sec\n",
            "Enter prompt 1/32: what is cv? \n",
            "Enter prompt 2/32: what is ml? \n",
            "Enter prompt 3/32: what is dl?\n",
            "Enter prompt 4/32: how do we make french toast? \n",
            "Enter prompt 5/32: how to make lasagna? \n",
            "Enter prompt 6/32: what is lasgana made of? \n",
            "Enter prompt 7/32: what is rl?\n",
            "Enter prompt 8/32: how to use rl in self-driving cars?\n",
            "Enter prompt 9/32: what is policy learning?\n",
            "Enter prompt 10/32: is rl tough?\n",
            "Enter prompt 11/32: what are some use cases of rl?\n",
            "Enter prompt 12/32: how can we combine rl with llms?\n",
            "Enter prompt 13/32: what are ViTs?\n",
            "Enter prompt 14/32: what is the use of ViTs?\n",
            "Enter prompt 15/32: explain me the working of ViTs?\n",
            "Enter prompt 16/32: can we use ViT in a self-driving car?\n",
            "Enter prompt 17/32: what is the difference between ViT and CV?\n",
            "Enter prompt 18/32: do you know about tinygrad?\n",
            "Enter prompt 19/32: what is the use of tinygrad?\n",
            "Enter prompt 20/32: what is cuda?\n",
            "Enter prompt 21/32: what is the difference between cuda and metal ?\n",
            "Enter prompt 22/32: is cuda better than metal?\n",
            "Enter prompt 23/32: how tinygrad converts python to cuda or metal?\n",
            "Enter prompt 24/32: what are lazy computations?\n",
            "Enter prompt 25/32: what graph is generated by tinygrad to convert python to cuda code?\n",
            "Enter prompt 26/32: what is ONNX?\n",
            "Enter prompt 27/32: can we achieve AGI with LLMs?\n",
            "Enter prompt 28/32: what is currently most hyped topic in ML community?\n",
            "Enter prompt 29/32: What is GNN?\n",
            "Enter prompt 30/32: can we use gnn in recommendation systems?\n",
            "Enter prompt 31/32: tell me use cases of gnn?\n",
            "Enter prompt 32/32: are transformers a special case of a GNN?\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Benchmarking on user provided prompts:\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "Asking to truncate to max_length but no maximum length is provided and the model has no predefined maximum length. Default to no truncation.\n",
            "Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Average time per inference: 0.8798 seconds\n",
            "Throughput: 290.97 tokens/sec\n",
            "Generating responses for user provided prompts:\n",
            "what is cv?\n",
            "\n",
            "CV stands for Curriculum Vitae, which is a Latin term meaning \"course of life.\" It is a\n",
            "document used to provide detailed information about an individual's education, work\n",
            "experience, skills, and other qualifications. A CV is often used when applying for jobs,\n",
            "academic positions, or research grants. It is a more detailed and comprehensive version of\n",
            "a resume, and is typically longer than one page. The purpose of a CV is to showcase an\n",
            "individual's accomplishments and qualifications in a clear and concise manner, making\n",
            "what is ml?\n",
            "\n",
            "Machine Learning (ML) is a subfield of artificial intelligence (AI) that provides systems\n",
            "the ability to automatically learn and improve from experience without being explicitly\n",
            "programmed. It focuses on the development of computer programs that can access data and\n",
            "use it to learn for themselves. The process of learning begins with observations or data,\n",
            "such as examples, direct experience, or instruction, in order to look for patterns in data\n",
            "and make better decisions in the future based on the examples that we have provided. The\n",
            "primary aim is to allow the computers to learn automatically without human\n",
            "what is dl?\n",
            "\n",
            "DL stands for \"Deep Learning,\" which is a subset of machine learning and artificial\n",
            "intelligence (AI) that uses neural networks with multiple layers to learn and make\n",
            "decisions from data. Deep learning models can automatically learn features from large\n",
            "amounts of data, making them particularly effective for tasks such as image and speech\n",
            "recognition, natural language processing, and predictive modeling.\n",
            "\n",
            "Deep learning models are inspired by the structure and function of the human brain, with\n",
            "each layer of the network representing a different level of abstraction or feature\n",
            "extraction. The model learns\n",
            "how do we make french toast? 1. In a shallow dish, whisk together 1 cup milk, 1/2 cup\n",
            "heavy cream, 4 eggs, 2 tbsp sugar, 1 tsp vanilla extract and a pinch of cinnamon. 2. Heat\n",
            "a non-stick skillet or griddle over medium heat. 3. Dip each slice of bread into the egg\n",
            "mixture, allowing it to soak for about 1 minute on each side. 4. Place the soaked bread\n",
            "slices onto the heated skillet or griddle and\n",
            "how to make lasagna? 1. Preheat oven to 375°F (190°C).\n",
            "2. Cook lasagna noodles according to package instructions. Drain and rinse with cold\n",
            "water.\n",
            "3. In a large bowl, mix together ricotta cheese, eggs, garlic, and parsley.\n",
            "4. Spread a thin layer of tomato sauce in the bottom of a 9x13 inch baking dish.\n",
            "5. Place a layer of cooked lasagna noodles on top of the sauce.\n",
            "what is lasgana made of?\n",
            "\n",
            "Lasagna is a traditional Italian dish made of flat pasta sheets called lasagna, which are\n",
            "usually cooked and then layered with various ingredients. The most common ingredients used\n",
            "in lasagna are:\n",
            "\n",
            "1. Lasagna noodles: These are the flat pasta sheets that form the base of the lasagna.\n",
            "They are usually cooked before being layered with the other ingredients.\n",
            "2. Tomato sauce: This is the base sauce that covers the bottom layer of the lasagna and is\n",
            "used to separate the layers of pasta\n",
            "what is rl?\n",
            "\n",
            "RL stands for Reinforcement Learning, which is a type of machine learning (ML) algorithm\n",
            "that allows an agent to learn how to perform a task by interacting with its environment.\n",
            "The agent learns to take actions that maximize a reward signal, which is a numerical value\n",
            "that represents the desirability of a particular state or action.\n",
            "\n",
            "In RL, the agent learns a policy, which is a mapping from states to actions, that\n",
            "maximizes the expected cumulative reward over time. The agent learns this policy through\n",
            "trial\n",
            "how to use rl in self-driving cars?\n",
            "\n",
            "To use Reinforcement Learning (RL) in self-driving cars, you would need to follow these\n",
            "general steps:\n",
            "\n",
            "1. Define the state space: The state space represents the information that the car uses to\n",
            "make decisions. It could include variables such as the car's position, velocity, steering\n",
            "angle, and sensor data (e.g., lidar, radar, camera).\n",
            "2. Define the action space: The action space represents the possible actions that the car\n",
            "can take based on its current state. It could include\n",
            "what is policy learning?\n",
            "\n",
            "Policy learning refers to the process by which an agent, such as an artificial\n",
            "intelligence (AI) system or a human, improves its behavior based on past experiences and\n",
            "feedback. In the context of reinforcement learning, a policy is a mapping from states to\n",
            "actions that specifies the agent's behavior. Policy learning involves finding the optimal\n",
            "policy that maximizes the expected reward over a sequence of actions. This can be achieved\n",
            "through various methods, such as value iteration, policy iteration, or deep learning\n",
            "algorithms like Q-learning and deep Q-learning.\n",
            "is rl tough?\n",
            "\n",
            "Yes, reinforcement learning (RL) can be challenging to implement and master, especially\n",
            "for complex problems. RL involves training an agent to learn how to take actions in an\n",
            "environment to maximize a reward signal. This requires a good understanding of the\n",
            "underlying mathematics and algorithms, as well as the ability to design and implement an\n",
            "effective learning environment.\n",
            "\n",
            "Moreover, RL can be computationally expensive, requiring significant computational\n",
            "resources and time to train an agent to perform well. Additionally, RL algorithms can be\n",
            "sensitive to hyperparameters and\n",
            "what are some use cases of rl?\n",
            "\n",
            "1. Recommender Systems: RL can be used to build recommender systems that suggest items to\n",
            "users based on their past preferences and interactions.\n",
            "2. Autonomous Driving: RL can be used to train self-driving cars to make decisions based\n",
            "on real-time sensor data and learn from experience.\n",
            "3. Robotics: RL can be used to train robots to perform complex tasks in various\n",
            "environments, such as grasping objects, navigating mazes, or assembling products.\n",
            "4\n",
            "how can we combine rl with llms?\n",
            "\n",
            "There are several ways to combine reinforcement learning (RL) with large language models\n",
            "(LLMs). Here are some common approaches:\n",
            "\n",
            "1. Using LLMs as a feature extractor: In this approach, an LLM is used to extract features\n",
            "from raw text data, which are then used as inputs to an RL algorithm. The LLM can be used\n",
            "to extract semantic features, such as named entities, sentiment, or topic, from text data.\n",
            "These features can be used to train an RL agent to learn a policy that\n",
            "what are ViTs?\n",
            "\n",
            "ViTs (Visual Transformers) are a type of deep learning model for computer vision tasks,\n",
            "specifically image classification. They are based on the Transformer architecture, which\n",
            "was originally designed for natural language processing tasks. ViTs replace the\n",
            "traditional convolutional neural network (CNN) architecture with a Transformer-based\n",
            "architecture, allowing for the direct processing of image pixels as input. This approach\n",
            "enables ViTs to capture long-range dependencies and hierarchical representations in\n",
            "images, leading to state-of-the-art performance on\n",
            "what is the use of ViTs?\n",
            "\n",
            "ViTs ( Vision Transformers) are a type of deep learning model that have been designed\n",
            "specifically for image recognition tasks. They are based on the Transformer architecture,\n",
            "which was originally developed for natural language processing tasks.\n",
            "\n",
            "ViTs use self-attention mechanisms to process local and global dependencies in an image,\n",
            "allowing them to capture long-range dependencies and contextual information in a more\n",
            "effective way than traditional convolutional neural networks (CNNs). This makes them\n",
            "particularly well-suited for large-scale image recognition tasks\n",
            "explain me the working of ViTs?\n",
            "\n",
            "ViTs (Vision Transformers) are a type of deep learning model that uses Transformer\n",
            "architecture for processing visual data, such as images. Transformers were initially\n",
            "designed for natural language processing tasks, but they have since been adapted for\n",
            "computer vision tasks as well.\n",
            "\n",
            "The basic idea behind ViTs is to treat an image as a sequence of patches and apply\n",
            "Transformer-based models to learn the relationships between these patches. Here's a high-\n",
            "level overview of how ViTs work:\n",
            "\n",
            "1. Image\n",
            "can we use ViT in a self-driving car?\n",
            "\n",
            "Yes, Vision Transformer (ViT) models can be used in self-driving cars for object detection\n",
            "and recognition tasks. ViT models are a type of transformer model that can process raw\n",
            "image data without the need for hand-crafted features or convolutional neural networks\n",
            "(CNNs). This makes them computationally efficient and well-suited for real-time\n",
            "applications like self-driving cars.\n",
            "\n",
            "However, it's important to note that using ViT models in self-driving cars requires a\n",
            "large amount\n",
            "what is the difference between ViT and CV?\n",
            "\n",
            "ViT ( Vision Transformer) and CV (Convolutional Neural Networks) are two different types\n",
            "of deep learning models used for image recognition tasks.\n",
            "\n",
            "CV (Convolutional Neural Networks) is a traditional deep learning model that uses\n",
            "convolutional layers to extract features from images. These layers apply filters to the\n",
            "input image to identify patterns and extract features, which are then passed through fully\n",
            "connected layers for classification.\n",
            "\n",
            "ViT ( Vision Transformer), on the other hand, is a more recent deep learning model that\n",
            "do you know about tinygrad?\n",
            "\n",
            "TinyGrad is a lightweight, open-source library for distributed deep learning training,\n",
            "designed to be easy to use and highly scalable. It is built on top of TensorFlow and\n",
            "PyTorch, and provides a simple interface for parallelizing and distributing training\n",
            "across multiple GPUs or machines. TinyGrad is particularly well-suited for research and\n",
            "experimentation, as it allows users to quickly and easily experiment with different\n",
            "architectures, loss functions, and optimization algorithms. It also includes built-in\n",
            "support for popular deep learning frameworks\n",
            "what is the use of tinygrad?\n",
            "\n",
            "TinyGrad is a lightweight and efficient gradient computation library for deep learning\n",
            "models. It is designed to be used in embedded systems and other resource-constrained\n",
            "environments where traditional deep learning frameworks like TensorFlow or PyTorch may be\n",
            "too large or complex. TinyGrad provides basic operations for building and training deep\n",
            "neural networks, such as forward and backward propagation, and supports common activation\n",
            "functions and loss functions. It is often used for on-device machine learning and edge AI\n",
            "applications.\n",
            "what is cuda?\n",
            "\n",
            "CUDA (Compute Unified Device Architecture) is a parallel computing platform and\n",
            "application programming interface (API) model created by NVIDIA. It allows developers to\n",
            "use NVIDIA GPUs for general purpose processing, in addition to their traditional use for\n",
            "rendering graphics. CUDA enables developers to harness the power of the GPU for a wide\n",
            "range of applications, including scientific simulations, data analytics, machine learning,\n",
            "and more.\n",
            "\n",
            "The CUDA architecture is designed to provide a unified programming model for both the CPU\n",
            "and GPU,\n",
            "what is the difference between cuda and metal ?\n",
            "\n",
            "CUDA and Metal are two different parallel computing frameworks, each designed for specific\n",
            "platforms.\n",
            "\n",
            "CUDA is a parallel computing platform and programming model developed by NVIDIA. It is\n",
            "primarily used for general-purpose computing on NVIDIA GPUs. CUDA provides a C/C++\n",
            "programming interface, along with a large ecosystem of libraries and tools, to enable\n",
            "developers to write and run code on GPUs. CUDA is widely used in scientific computing,\n",
            "machine learning, and other fields where high performance and parallelism\n",
            "is cuda better than metal?\n",
            "\n",
            "Both CUDA and Metal are parallel computing frameworks designed to optimize performance for\n",
            "GPU computing. CUDA is a more established and widely used framework, developed by NVIDIA,\n",
            "while Metal is a framework developed by Apple for use on Apple devices.\n",
            "\n",
            "In terms of raw performance, CUDA has a larger user base and a more extensive set of\n",
            "libraries and tools, which can lead to better optimization and higher performance. CUDA\n",
            "also supports a wider range of GPUs, including those from NVIDIA and other vendors\n",
            "how tinygrad converts python to cuda or metal?\n",
            "\n",
            "I'm trying to understand how tinygrad converts python code to CUDA or Metal code. I've\n",
            "read the paper \"TinyGrad: A Fast and Lightweight Deep Learning Framework\" but I'm still\n",
            "not clear on the conversion process.\n",
            "\n",
            "From what I understand, TinyGrad uses a static analysis approach to identify the\n",
            "computation graph from the Python code. But how does it convert this graph to CUDA or\n",
            "Metal code? Is there a pre-existing library of CUDA or Metal functions that correspond\n",
            "what are lazy computations?\n",
            "\n",
            "Lazy computation is a programming technique where the result of a computation is not\n",
            "produced until it is actually needed. This is in contrast to eager computation, where the\n",
            "result of a computation is produced as soon as it is defined.\n",
            "\n",
            "In lazy computation, the computation is suspended until a value is requested from it. At\n",
            "that point, the computation is evaluated, and the resulting value is produced and\n",
            "returned. This can be useful in situations where the computation is expensive or involves\n",
            "large amounts of data, as it allows the computation to be deferred until it\n",
            "what graph is generated by tinygrad to convert python to cuda code?\n",
            "\n",
            "Tiny-Grad is a Python library for automatic differentiation and it does not generate any\n",
            "specific graph for converting Python code to CUDA code. Instead, it computes gradients\n",
            "symbolically using backpropagation and can then apply various optimization techniques to\n",
            "the resulting expressions.\n",
            "\n",
            "If you want to run your Python code on a GPU using CUDA, you would typically need to write\n",
            "CUDA code yourself or use a higher-level library such as PyTorch or TensorFlow that\n",
            "provides CUDA support. These libraries can\n",
            "what is ONNX?\n",
            "\n",
            "ONNX (Open Neural Network Exchange) is an open format for representing machine learning\n",
            "models. It is a community-driven, industry-standard format for exchanging deep learning\n",
            "models. ONNX enables models to be transferred between various machine learning frameworks,\n",
            "tools, runtimes, and compilers.\n",
            "\n",
            "ONNX provides a common format for representing models, which makes it easier to move\n",
            "models between different deep learning frameworks, such as TensorFlow, PyTorch, and\n",
            "Microsoft Cognitive Toolkit (CNTK).\n",
            "can we achieve AGI with LLMs?\n",
            "\n",
            "I believe that Large Language Models (LLMs) can be a powerful tool in the pursuit of\n",
            "Artificial General Intelligence (AGI), but they are not AGI themselves. AGI refers to a\n",
            "machine intelligence that can understand, learn, and apply knowledge across a broad range\n",
            "of tasks at a level equal to or beyond that of a human being. LLMs, on the other hand, are\n",
            "specifically designed to process and generate human-like text based on input data.\n",
            "\n",
            "However, LLMs can be used as a foundation for AGI\n",
            "what is currently most hyped topic in ML community?\n",
            "\n",
            "There are several topics that are currently generating a lot of buzz in the machine\n",
            "learning community. Here are a few of them:\n",
            "\n",
            "1. Transformers: Transformers are a type of neural network architecture that has gained\n",
            "significant attention in recent years due to their ability to process sequences of data,\n",
            "such as text or speech, with impressive results. Transformers have been used to achieve\n",
            "state-of-the-art performance on a variety of natural language processing tasks, including\n",
            "machine translation, text summarization, and question answering.\n",
            "2\n",
            "What is GNN?\n",
            "\n",
            "Graph Neural Networks (GNNs) are a type of neural network that can process graph data\n",
            "structures. They are designed to learn representations of nodes and edges in a graph,\n",
            "allowing them to make predictions or perform other tasks based on the graph's structure\n",
            "and node features.\n",
            "\n",
            "GNNs have gained popularity in recent years due to their ability to handle complex\n",
            "relationships between data points, such as those found in social networks, chemical\n",
            "compounds, and other real-world systems. They can be used for a variety of tasks,\n",
            "including\n",
            "can we use gnn in recommendation systems?\n",
            "\n",
            "Yes, Graph Neural Networks (GNNs) can be used in recommendation systems. Recommendation\n",
            "systems are a type of information filtering system that suggests items to users based on\n",
            "their past behavior or preferences. In a collaborative filtering approach, recommendations\n",
            "are based on the behavior or preferences of other users. In a content-based approach,\n",
            "recommendations are based on the features of the items.\n",
            "\n",
            "GNNs can be used to model the relationships between users and items in a recommendation\n",
            "system as a graph. Each node in the\n",
            "tell me use cases of gnn?\n",
            "\n",
            "A Graph Neural Network (GNN) is a type of neural network designed to process graph data,\n",
            "where nodes represent entities and edges represent relationships between them. Here are\n",
            "some common use cases of GNNs:\n",
            "\n",
            "1. Social Network Analysis: GNNs can be used to analyze social networks, where nodes\n",
            "represent individuals and edges represent relationships between them. Applications include\n",
            "recommendation systems, community detection, and sentiment analysis.\n",
            "2. Protein Structure Prediction: GNNs can be used to predict the three-dimensional\n",
            "structure of\n",
            "are transformers a special case of a GNN?\n",
            "\n",
            "I've been reading about transformers and graph neural networks (GNNs) and I'm wondering if\n",
            "transformers are a special case of a GNN or if they are fundamentally different.\n",
            "\n",
            "Transformers were originally designed for sequence-to-sequence tasks, such as machine\n",
            "translation, where the input and output sequences have a clear order. However, they have\n",
            "also been applied to other tasks, such as graph-level classification and graph generation,\n",
            "where the input and output graphs may not have a clear order.\n",
            "\n",
            "GNN\n"
          ]
        }
      ],
      "source": [
        "if __name__ == \"__main__\":\n",
        "    loop = asyncio.get_event_loop()\n",
        "    loop.run_until_complete(main_concurrent(model, tokenizer, concurrency=32))"
      ]
    },
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