Mamba.ipynb

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  },
  "cells": [
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      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/TrelisResearch/install-guides/blob/main/Mamba.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Mamba Inference Script\n",
        "by [Trelis.com](https://Trelis.com).\n",
        "\n",
        "Find us on [YouTube](https://Youtube.com/@TrelisResarch).\n",
        "\n",
        "Built from: [State spaces, Mamba on GitHub](https://github.com/state-spaces/mamba/blob/main/benchmarks/benchmark_generation_mamba_simple.py)\n",
        "\n",
        "## Getting started\n",
        "Select a GPU from Runtime -> Change Runtime type."
      ],
      "metadata": {
        "id": "S8UDlHsa1ilJ"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ZCTReYFZz6LN"
      },
      "outputs": [],
      "source": [
        "!pip install causal-conv1d>=1.1.0 -q -U"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install mamba-ssm -q -U"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
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      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "\u001b[?25l     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/44.6 kB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0m\r\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m44.6/44.6 kB\u001b[0m \u001b[31m2.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h  Building wheel for mamba-ssm (setup.py) ... \u001b[?25l\u001b[?25hdone\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!git clone https://github.com/state-spaces/mamba.git"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YhfaPaHSz_7Z",
        "outputId": "73d794ce-fa8c-4b3a-d902-4c84dfc436c3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Cloning into 'mamba'...\n",
            "remote: Enumerating objects: 237, done.\u001b[K\n",
            "remote: Counting objects: 100% (128/128), done.\u001b[K\n",
            "remote: Compressing objects: 100% (65/65), done.\u001b[K\n",
            "remote: Total 237 (delta 76), reused 84 (delta 57), pack-reused 109\u001b[K\n",
            "Receiving objects: 100% (237/237), 506.54 KiB | 4.73 MiB/s, done.\n",
            "Resolving deltas: 100% (105/105), done.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "%cd mamba"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "q2FeujCy0Ry5",
        "outputId": "8c907100-5ca0-4894-dec4-ed1bf218588a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/content/mamba\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!echo \"/usr/lib64-nvidia/\" | sudo tee -a /etc/ld.so.conf.d/libcuda.conf\n",
        "!ldconfig"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "aFps7u-D4fH1",
        "outputId": "ce194842-ed10-4202-f039-9e7e2f1611f8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/usr/lib64-nvidia/\n",
            "/sbin/ldconfig.real: /usr/local/lib/libtbbmalloc_proxy.so.2 is not a symbolic link\n",
            "\n",
            "/sbin/ldconfig.real: /usr/local/lib/libtbb.so.12 is not a symbolic link\n",
            "\n",
            "/sbin/ldconfig.real: /usr/local/lib/libtbbbind.so.3 is not a symbolic link\n",
            "\n",
            "/sbin/ldconfig.real: /usr/local/lib/libtbbmalloc.so.2 is not a symbolic link\n",
            "\n",
            "/sbin/ldconfig.real: /usr/local/lib/libtbbbind_2_5.so.3 is not a symbolic link\n",
            "\n",
            "/sbin/ldconfig.real: /usr/local/lib/libtbbbind_2_0.so.3 is not a symbolic link\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## One line example"
      ],
      "metadata": {
        "id": "QjCVbhrqK40D"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!python benchmarks/benchmark_generation_mamba_simple.py --model-name \"Trelis/mamba-2.8b-slimpj-bf16\" --prompt \"Here is a python program to add the first five fibonnacci numbers:\\n\" --topp 0.9 --temperature 0.7 --repetition-penalty 1.2"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "EaeESrKK0Ec4",
        "outputId": "18d3a7d7-29a9-411e-eab9-bcd329a469b9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Loading model state-spaces/mamba-2.8b\n",
            "Number of parameters: 2768345600\n",
            "2024-01-29 11:06:59.030622: 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",
            "2024-01-29 11:06:59.030678: 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",
            "2024-01-29 11:06:59.032250: 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",
            "2024-01-29 11:07:00.149092: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT\n",
            "['Here is a python program to add the first five fibonnacci numbers:\\\\n\",\\n    \"\\\\n\",\\n    \"def f(x):\\\\n\",\\n    \"  if x == 0 or 1: return [0,1]\\\\n\",\\n    \" else : \\\\n\",\\n    \"   return[f([i+j for i in range (2) if j==0] ) + list ([fibo_helper]())]\", \"list of lists\"] \\n     print(\"The sum = \",sum((map(lambda lst:[']\n",
            "Prompt length: 17, generation length: 100\n",
            "state-spaces/mamba-2.8b prompt processing + decoding time: 671ms\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Explicit Example"
      ],
      "metadata": {
        "id": "DLAR5OJV5z_2"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import torch\n",
        "from transformers import AutoTokenizer, AutoModelForCausalLM\n",
        "from mamba_ssm.models.mixer_seq_simple import MambaLMHeadModel"
      ],
      "metadata": {
        "id": "8JhLWgBc51f7"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Set parameters\n",
        "model_name = \"Trelis/mamba-2.8b-slimpj-bf16\" # or \"clibrain/mamba-2.8b-instruct-openhermes\"\n",
        "# model_name = \"clibrain/mamba-2.8b-instruct-openhermes\"\n",
        "promptlen = 100\n",
        "genlen = 100\n",
        "temperature = 0.01\n",
        "topk = 1\n",
        "topp = 1.0\n",
        "repetition_penalty = 1.0\n",
        "batch = 1\n",
        "repeats = 3\n",
        "device = \"cuda\"\n",
        "dtype = torch.float16 #or use torch.bfloat16 for an A100 GPU\n",
        "\n",
        "# Load model\n",
        "print(f\"Loading model {model_name}\")\n",
        "tokenizer = AutoTokenizer.from_pretrained(\"EleutherAI/gpt-neox-20b\")\n",
        "model = MambaLMHeadModel.from_pretrained(\n",
        "    model_name,\n",
        "    device=device,\n",
        "    dtype=dtype)\n",
        "\n",
        "model.eval()\n",
        "print(f\"Number of parameters: {sum(p.numel() for p in model.parameters() if p.requires_grad)}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 402,
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          ]
        },
        "id": "BzpPbmwR6Hhe",
        "outputId": "b131ff8a-4a45-427b-9430-541fc9201b1a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Loading model Trelis/mamba-2.8b-slimpj-bf16\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_token.py:88: 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"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "tokenizer_config.json:   0%|          | 0.00/156 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "1839e27519004de5965089d58f91c341"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "vocab.json:   0%|          | 0.00/1.08M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "3f0bd36e608b4500abc1750f093b8b54"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "merges.txt:   0%|          | 0.00/457k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "c5feb2c7944245c4b925d01ab38e9058"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "tokenizer.json:   0%|          | 0.00/2.11M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "2eadb31cf0df4f3994c01140a60b51c2"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "special_tokens_map.json:   0%|          | 0.00/90.0 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "0f64b9a30e5c45af8be0ad785330c17f"
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          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "config.json:   0%|          | 0.00/184 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "e7336aa3f2f748ef85074773eb206925"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "pytorch_model.bin:   0%|          | 0.00/5.55G [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "b5e218b67c644186a6e0bc793b2c01f3"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Number of parameters: 2768345600\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(model.config)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SixQ_-kxXPJn",
        "outputId": "03ba944e-64d4-410d-c102-23da896a8a1f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "MambaConfig(d_model=2560, n_layer=64, vocab_size=50277, ssm_cfg={}, rms_norm=True, residual_in_fp32=True, fused_add_norm=True, pad_vocab_size_multiple=8)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(model)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6PUdxkBaXo4w",
        "outputId": "72641b96-6722-4a41-bb92-b245bb897cfa"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "MambaLMHeadModel(\n",
            "  (backbone): MixerModel(\n",
            "    (embedding): Embedding(50280, 2560)\n",
            "    (layers): ModuleList(\n",
            "      (0-63): 64 x Block(\n",
            "        (mixer): Mamba(\n",
            "          (in_proj): Linear(in_features=2560, out_features=10240, bias=False)\n",
            "          (conv1d): Conv1d(5120, 5120, kernel_size=(4,), stride=(1,), padding=(3,), groups=5120)\n",
            "          (act): SiLU()\n",
            "          (x_proj): Linear(in_features=5120, out_features=192, bias=False)\n",
            "          (dt_proj): Linear(in_features=160, out_features=5120, bias=True)\n",
            "          (out_proj): Linear(in_features=5120, out_features=2560, bias=False)\n",
            "        )\n",
            "        (norm): RMSNorm()\n",
            "      )\n",
            "    )\n",
            "    (norm_f): RMSNorm()\n",
            "  )\n",
            "  (lm_head): Linear(in_features=2560, out_features=50280, bias=False)\n",
            ")\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# prompt = \"Question: Write a short python program that adds together the first five fib numbers.\\n\\nAnswer: Here is the answer:\\n\"  # Replace with your prompt if needed\n",
        "prompt = \"What planets are in our solar system?\\n\\nAnswer: Here is the answer:\\n\"  # Replace with your prompt if needed\n",
        "\n",
        "torch.random.manual_seed(0)\n",
        "if prompt is None:\n",
        "    input_ids = torch.randint(1, 1000, (batch, promptlen), dtype=torch.long, device=device)\n",
        "    attn_mask = torch.ones_like(input_ids, dtype=torch.long, device=device)\n",
        "else:\n",
        "    tokens = tokenizer(prompt, return_tensors=\"pt\")\n",
        "    input_ids = tokens.input_ids.to(device=device)\n",
        "    attn_mask = tokens.attention_mask.to(device=device)\n",
        "max_length = input_ids.shape[1] + genlen\n",
        "\n",
        "fn = lambda: model.generate(\n",
        "    input_ids=input_ids,\n",
        "    max_length=max_length,\n",
        "    cg=True,\n",
        "    return_dict_in_generate=True,\n",
        "    output_scores=True,\n",
        "    enable_timing=False,\n",
        "    temperature=temperature,\n",
        "    top_k=topk,\n",
        "    top_p=topp,\n",
        "    eos_token_id=tokenizer.eos_token_id,\n",
        "    repetition_penalty=repetition_penalty,\n",
        ")"
      ],
      "metadata": {
        "id": "Df9RlZDd6cZv"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(tokenizer.eos_token_id)\n",
        "eos_token = tokenizer.convert_ids_to_tokens(tokenizer.eos_token_id)\n",
        "print(eos_token)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UNiQldcQIcrx",
        "outputId": "b6f08a22-fbc8-41c1-b9ba-2e23b39da9c5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0\n",
            "<|endoftext|>\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import time\n",
        "\n",
        "out = fn()\n",
        "if prompt is not None:\n",
        "    print(prompt)\n",
        "    print(\"\\n\")  # Print new lines\n",
        "    # Decode and print the generated text\n",
        "    decoded_sequences = tokenizer.batch_decode(out.sequences.tolist())\n",
        "    for sequence in decoded_sequences:\n",
        "        generated_text = sequence[len(tokenizer.decode(input_ids[0], skip_special_tokens=True)):]\n",
        "        print(generated_text)\n",
        "\n",
        "torch.cuda.synchronize()\n",
        "start = time.time()\n",
        "for _ in range(repeats):\n",
        "    fn()\n",
        "torch.cuda.synchronize()\n",
        "print(f\"Prompt length: {len(input_ids[0])}, generation length: {len(out.sequences[0]) - len(input_ids[0])}\")\n",
        "print(f\"{model_name} prompt processing + decoding time: {(time.time() - start) / repeats * 1000:.0f}ms\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-yTbli5aFrma",
        "outputId": "50bc747c-0ee6-4286-a038-9f109f137c5a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "What planets are in our solar system?\n",
            "\n",
            "Answer: Here is the answer:\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "The planets in our solar system are: Mercury, Venus, Earth, Mars, Jupiter, Saturn, Uranus, Neptune, and Pluto.\n",
            "\n",
            "What is the name of the planet that is closest to the sun?\n",
            "\n",
            "Answer: The planet that is closest to the sun is Mercury.\n",
            "\n",
            "What is the name of the planet that is farthest from the sun?\n",
            "\n",
            "Answer: The planet that is farthest from the sun is Neptune.\n",
            "\n",
            "Prompt length: 18, generation length: 100\n",
            "Trelis/mamba-2.8b-slimpj-bf16 prompt processing + decoding time: 1065ms\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def fibonacci(n):\n",
        "    a, b = 0, 1\n",
        "    for i in range(n):\n",
        "        print(a)\n",
        "        a, b = b, a + b\n",
        "\n",
        "print(fibonacci(5))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "a4YutqwoTrDa",
        "outputId": "4355aaf8-6747-494e-afac-6212f55430b8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0\n",
            "1\n",
            "1\n",
            "2\n",
            "3\n",
            "None\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Passkey Retrieval (you must first have loaded the model above in the explicit example)"
      ],
      "metadata": {
        "id": "Tf0gMIs6KZ_C"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Define the passkey and text file\n",
        "passkey = \"(the passkey is 'u89dsnakj8')\"\n",
        "text_file = '../berkshire23.txt'\n",
        "\n",
        "# Define the length limit for the text\n",
        "len_limit = int(16000 * 0.8)  # 16k characters is about 4k tokens of context.\n",
        "\n",
        "# Calculate the position to insert the passkey\n",
        "n = int(len_limit / 5 * 0.5)  # Placement of the passkey as a % of total text.\n",
        "\n",
        "# Read the text from the file\n",
        "with open(text_file, 'r') as file:\n",
        "    text = file.read()\n",
        "\n",
        "# Split the text into words\n",
        "words = text.split()\n",
        "\n",
        "# Insert the passkey after the nth word\n",
        "words.insert(n, passkey)\n",
        "\n",
        "# Join back into a string and truncate to 'len_limit' characters\n",
        "modified_text = ' '.join(words)[:len_limit]\n",
        "\n",
        "# Define the prompt for the model\n",
        "prompt = f'Respond with the passkey contained within the below text.\\n\\n{modified_text}\\n\\nRespond with the passkey contained within the above text.\\n\\nThe passkey is:'\n",
        "\n",
        "# Prepare the input for the model\n",
        "tokens = tokenizer(prompt, return_tensors=\"pt\")\n",
        "input_ids = tokens.input_ids.to(device=device)\n",
        "attn_mask = tokens.attention_mask.to(device=device)\n",
        "max_length = input_ids.shape[1] + 100  # Assuming you want to generate 100 tokens\n",
        "\n",
        "# Generate function for Mamba model\n",
        "def generate_mamba(input_ids, max_length):\n",
        "    return model.generate(\n",
        "        input_ids=input_ids,\n",
        "        max_length=max_length,\n",
        "        cg=True,\n",
        "        return_dict_in_generate=True,\n",
        "        output_scores=True,\n",
        "        enable_timing=False,\n",
        "        temperature=0.01,\n",
        "        top_k=1,\n",
        "        top_p=1.0,\n",
        "        eos_token_id=tokenizer.eos_token_id,\n",
        "        repetition_penalty=1.0,\n",
        "    )\n",
        "\n",
        "# Run the generation\n",
        "out = generate_mamba(input_ids, max_length)\n",
        "\n",
        "# Decode and print the generated text\n",
        "decoded_sequences = tokenizer.batch_decode(out.sequences.tolist())\n",
        "for sequence in decoded_sequences:\n",
        "    generated_text = sequence[len(tokenizer.decode(input_ids[0], skip_special_tokens=True)):]\n",
        "    print(generated_text)\n",
        "\n",
        "# Optionally, measure the time taken for generation\n",
        "torch.cuda.synchronize()\n",
        "start = time.time()\n",
        "for _ in range(3):  # Number of repeats\n",
        "    generate_mamba(input_ids, max_length)\n",
        "torch.cuda.synchronize()\n",
        "end = time.time()\n",
        "print(f\"{model_name} prompt processing + decoding time: {(end - start) / 3 * 1000:.0f}ms\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UcoyTxtDKbzQ",
        "outputId": "6cc6e8ea-cd1e-473b-997a-d640211b3e06"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            " u89dsnakj8\n",
            "\n",
            "we'll see you at the end of the day\n",
            "\n",
            "# Transcript of the Berkshire Hathaway Annual Meeting\n",
            "\n",
            "## Transcript of the Berkshire Hathaway Annual Meeting\n",
            "\n",
            "## May 1, 2022\n",
            "\n",
            "## CNBC\n",
            "\n",
            "## CNBC is a trademark of NBCUniversal, Inc.\n",
            "\n",
            "## Copyright © 2022 NBCUniversal Media, LLC. All rights reserved.\n",
            "\n",
            "## CNBC is a trademark of NBC\n",
            "Trelis/mamba-2.8b-slimpj-bf16 prompt processing + decoding time: 1431ms\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## OpenHermes Inference"
      ],
      "metadata": {
        "id": "EOUwK_aWJf1F"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import torch\n",
        "from transformers import AutoTokenizer, AutoModelForCausalLM\n",
        "from mamba_ssm.models.mixer_seq_simple import MambaLMHeadModel\n",
        "\n",
        "CHAT_TEMPLATE_ID = \"HuggingFaceH4/zephyr-7b-beta\"\n",
        "\n",
        "device = \"cuda:0\" if torch.cuda.is_available() else \"cpu\"\n",
        "model_name = \"clibrain/mamba-2.8b-instruct-openhermes\"\n",
        "\n",
        "eos_token = \"<|endoftext|>\"\n",
        "tokenizer = AutoTokenizer.from_pretrained(model_name)\n",
        "tokenizer.eos_token = eos_token\n",
        "tokenizer.pad_token = tokenizer.eos_token\n",
        "tokenizer.chat_template = AutoTokenizer.from_pretrained(CHAT_TEMPLATE_ID).chat_template\n",
        "\n",
        "# Uncomment the below if you haven't loaded a model above.\n",
        "# model = MambaLMHeadModel.from_pretrained(\n",
        "#         model_name, device=device, dtype=torch.float16)\n",
        "\n",
        "messages = []\n",
        "# prompt = \"Tell me 5 sites to visit in Spain\"\n",
        "# prompt = \"What are the planets in our solar system\"\n",
        "prompt = \"Write a piece of python code to add the first five fibonacci numbers\"\n",
        "messages.append(dict(role=\"user\", content=prompt))\n",
        "\n",
        "input_ids = tokenizer.apply_chat_template(\n",
        "            messages, return_tensors=\"pt\", add_generation_prompt=True\n",
        ").to(device)\n",
        "\n",
        "out = model.generate(\n",
        "    input_ids=input_ids,\n",
        "    max_length=250,\n",
        "    temperature=0.9,\n",
        "    top_p=0.7,\n",
        "    eos_token_id=tokenizer.eos_token_id,\n",
        ")\n",
        "\n",
        "decoded = tokenizer.batch_decode(out)\n",
        "assistant_message = (\n",
        "    decoded[0].split(\"<|assistant|>\\n\")[-1].replace(eos_token, \"\")\n",
        ")\n",
        "\n",
        "print(assistant_message)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_Ym-4At1JhzE",
        "outputId": "9c68e463-6fcf-4936-fb8a-1393e3e746cb"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "fibonacci_sequence = [0, 1]\n",
            "for i in range(2, 6):\n",
            "    fibonacci_sequence.append(fibonacci_sequence[i-1] + fibonacci_sequence[i-2])\n",
            "print(fibonacci_sequence)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "fibonacci_sequence = [0, 1]\n",
        "for i in range(2, 6):\n",
        "    fibonacci_sequence.append(fibonacci_sequence[i-1] + fibonacci_sequence[i-2])\n",
        "print(fibonacci_sequence)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kBVbbZ0MyGYT",
        "outputId": "b9b8bc4a-fea5-459a-8cb2-f3624f017232"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[0, 1, 1, 2, 3, 5]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Uploading Models to Hub"
      ],
      "metadata": {
        "id": "8_zAIFN1AzvE"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "new_repo_name=\"Trelis/mamba-2.8b-slimpj\"\n",
        "\n",
        "model.save_pretrained(new_repo_name)"
      ],
      "metadata": {
        "id": "w_ptts2G6dH-"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install huggingface-hub"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "97ENDppp_7mK",
        "outputId": "305b8e0d-5ad7-4a7e-f584-8ad7600d19e8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: huggingface-hub in /usr/local/lib/python3.10/dist-packages (0.20.3)\n",
            "Requirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from huggingface-hub) (3.13.1)\n",
            "Requirement already satisfied: fsspec>=2023.5.0 in /usr/local/lib/python3.10/dist-packages (from huggingface-hub) (2023.6.0)\n",
            "Requirement already satisfied: requests in /usr/local/lib/python3.10/dist-packages (from huggingface-hub) (2.31.0)\n",
            "Requirement already satisfied: tqdm>=4.42.1 in /usr/local/lib/python3.10/dist-packages (from huggingface-hub) (4.66.1)\n",
            "Requirement already satisfied: pyyaml>=5.1 in /usr/local/lib/python3.10/dist-packages (from huggingface-hub) (6.0.1)\n",
            "Requirement already satisfied: typing-extensions>=3.7.4.3 in /usr/local/lib/python3.10/dist-packages (from huggingface-hub) (4.5.0)\n",
            "Requirement already satisfied: packaging>=20.9 in /usr/local/lib/python3.10/dist-packages (from huggingface-hub) (23.2)\n",
            "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests->huggingface-hub) (3.3.2)\n",
            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests->huggingface-hub) (3.6)\n",
            "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests->huggingface-hub) (2.0.7)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests->huggingface-hub) (2023.11.17)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from huggingface_hub import notebook_login\n",
        "\n",
        "notebook_login()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 145,
          "referenced_widgets": [
            "286beb0636494c4a9f03f1fa9c256c19",
            "bbb9190245aa459cbc846295a1185063",
            "a6cdae6e38324eacbedbe23fda375215",
            "f4a32c783ce747f9bf681c394f26c1d3",
            "4ede720d11c34ae683206632c0c226ca",
            "4715bc159a1744f0857b18c77c0a24c1",
            "cd62d193e5674ad096c92f0e8b247500",
            "7428b26aba8c42dcbc92bdcd5cf5ade5",
            "5ec735aa00794b67bbc7c3026477bdb4",
            "45565b1dc0424783a31c0396919cf406",
            "f1801ee1b8d242dfbe789dff58f89325",
            "3a9eca12efa442688a26ef7a314743c8",
            "9bd54510b5c54a2d86fad0e6e46ec31e",
            "33533c9553aa40059d0ee26a836baa89",
            "8b4e135899634622a4417b6cf2b93a34",
            "e01b54ba28a7490a9f9f527c634edb78",
            "9f056b60caf645758fd62fd55852ef43",
            "89fb9a7913a548149af80ea0ece0690d",
            "5b77a842bd614778a6f4cbe224cb2b8c",
            "724e7b53a1904b9d9e9a038fcb09fc01",
            "fd648e53baff49ee87e389361571513d",
            "fc8d58c0a7464384b8fb1d6901904a50",
            "d42f74793e1b43a2ba6344abfc465784",
            "3d851a889814499b8a13954b9eb0ad54",
            "de78a194c0454b878c1163a3de42441f",
            "43ac4ca9e3954bddb8e2d58098b7a30e",
            "5325f682a0e5416982d4ffa3f99b70cb",
            "d219661c79f44bccbaf040b8c853a85d",
            "ee152816740a486797e147b4ab6bd090",
            "32cae158824a46bc9b6e3efa3ef37931",
            "c476173917014e62abc527493763f052",
            "63e16302402441e1b46c0d50d2fc3a3a"
          ]
        },
        "id": "PSYzwT2F_9nj",
        "outputId": "05198031-a281-4e15-bec5-70804a73c9b3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "VBox(children=(HTML(value='<center> <img\\nsrc=https://huggingface.co/front/assets/huggingface_logo-noborder.sv…"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "286beb0636494c4a9f03f1fa9c256c19"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from huggingface_hub import HfApi, upload_folder\n",
        "import os\n",
        "\n",
        "# Set the repository name and local folder path\n",
        "repo_id = \"Trelis/mamba-2.8b-slimpj\"\n",
        "folder_path = \"./Trelis/mamba-2.8b-slimpj\"  # Adjust this path to your local folder\n",
        "\n",
        "# Optionally, set your Hugging Face authentication token\n",
        "# You can retrieve your token from your Hugging Face account (https://huggingface.co/settings/tokens)\n",
        "# If you don't set the token here, the function will use the token stored on your machine\n",
        "# token = None  # Replace with your token if needed\n",
        "\n",
        "# Upload the folder to the repository\n",
        "upload_url = upload_folder(\n",
        "    repo_id=repo_id,\n",
        "    folder_path=folder_path,\n",
        "    # token=token  # Include this if you have set a token above\n",
        ")\n",
        "\n",
        "print(f\"Folder uploaded successfully. You can view it at: {upload_url}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 66,
          "referenced_widgets": [
            "56d6297ed7fa403e8728b9251ab94b7d",
            "a144929019b04d9fa3383f93248929e6",
            "c589fe25190c42ed9c1caa3c76e73f63",
            "43735b35d1ab4940a14a209859080f6c",
            "77868c60a6b24c8db4dad1d62e13dbde",
            "d4623de9618e4e78aa232a1e6ab3168d",
            "421bf1581dda4971a009d0def3de586b",
            "8eac3baae1a04094b23d81aeb5a611c2",
            "fe2077d5789042d5b3d66fdfb7923c50",
            "0a74f1dc43504bef949d1f7d5053402e",
            "2c82b58babf54c5fb098ac5f1a91e478"
          ]
        },
        "id": "E6Av3LotAoV1",
        "outputId": "33030828-c94d-42c2-c6e6-be9de04e198b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "pytorch_model.bin:   0%|          | 0.00/5.55G [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "56d6297ed7fa403e8728b9251ab94b7d"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Folder uploaded successfully. You can view it at: https://huggingface.co/Trelis/mamba-2.8b-slimpj-bf16/tree/main/\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "%cd ../../\n",
        "%ls -l -a"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NZdQsDX3-eg7",
        "outputId": "3067baa9-95a6-4677-db61-8ba96e91df0c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/content/mamba\n",
            "total 96\n",
            "drwxr-xr-x 12 root root  4096 Jan 29 11:29 \u001b[0m\u001b[01;34m.\u001b[0m/\n",
            "drwxr-xr-x  1 root root  4096 Jan 29 10:49 \u001b[01;34m..\u001b[0m/\n",
            "drwxr-xr-x  3 root root  4096 Jan 29 10:49 \u001b[01;34m3rdparty\u001b[0m/\n",
            "drwxr-xr-x  2 root root  4096 Jan 29 10:49 \u001b[01;34massets\u001b[0m/\n",
            "-rw-r--r--  1 root root    53 Jan 29 10:49 AUTHORS\n",
            "drwxr-xr-x  2 root root  4096 Jan 29 10:59 \u001b[01;34mbenchmarks\u001b[0m/\n",
            "drwxr-xr-x  3 root root  4096 Jan 29 10:49 \u001b[01;34mcsrc\u001b[0m/\n",
            "drwxr-xr-x  2 root root  4096 Jan 29 10:49 \u001b[01;34mevals\u001b[0m/\n",
            "drwxr-xr-x  8 root root  4096 Jan 29 10:49 \u001b[01;34m.git\u001b[0m/\n",
            "drwxr-xr-x  3 root root  4096 Jan 29 10:49 \u001b[01;34m.github\u001b[0m/\n",
            "-rw-r--r--  1 root root    39 Jan 29 10:49 .gitignore\n",
            "-rw-r--r--  1 root root   144 Jan 29 10:49 .gitmodules\n",
            "-rw-r--r--  1 root root 11348 Jan 29 10:49 LICENSE\n",
            "drwxr-xr-x  7 root root  4096 Jan 29 11:09 \u001b[01;34mmamba_ssm\u001b[0m/\n",
            "-rw-r--r--  1 root root  8314 Jan 29 10:49 README.md\n",
            "-rw-r--r--  1 root root 10450 Jan 29 10:49 setup.py\n",
            "drwxr-xr-x  3 root root  4096 Jan 29 10:49 \u001b[01;34mtests\u001b[0m/\n",
            "drwxr-xr-x  3 root root  4096 Jan 29 11:29 \u001b[01;34mTrelis\u001b[0m/\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "trcLDfsF_YFc"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}