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eramax/Nous-Capybara-limarpv3-34B-3.0bpw-h6-exl2-2.ipynb

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},
"cells": [
{
"cell_type": "markdown",
"metadata": {
"id": "view-in-github",
"colab_type": "text"
},
"source": [
"<a href=\"https://colab.research.google.com/gist/eramax/732ab060b8c3adddd12a2ad6f0741d5a/exl2-2.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"id": "UKnZDcsnITG3",
"outputId": "2c4e3dfb-1dae-49a9-934a-d27a82ed926a",
"colab": {
"base_uri": "https://localhost:8080/"
}
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Cloning into 'exllamav2'...\n",
"remote: Enumerating objects: 145, done.\u001b[K\n",
"remote: Counting objects: 100% (145/145), done.\u001b[K\n",
"remote: Compressing objects: 100% (140/140), done.\u001b[K\n",
"remote: Total 145 (delta 13), reused 60 (delta 3), pack-reused 0\u001b[K\n",
"Receiving objects: 100% (145/145), 1.64 MiB | 24.02 MiB/s, done.\n",
"Resolving deltas: 100% (13/13), done.\n",
"/content/exllamav2\n",
"Requirement already satisfied: pandas in /usr/local/lib/python3.10/dist-packages (from -r requirements.txt (line 1)) (1.5.3)\n",
"Collecting ninja (from -r requirements.txt (line 2))\n",
" Downloading ninja-1.11.1.1-py2.py3-none-manylinux1_x86_64.manylinux_2_5_x86_64.whl (307 kB)\n",
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m307.2/307.2 kB\u001b[0m \u001b[31m4.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
"\u001b[?25hCollecting fastparquet (from -r requirements.txt (line 3))\n",
" Downloading fastparquet-2023.10.1-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (1.7 MB)\n",
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.7/1.7 MB\u001b[0m \u001b[31m65.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
"\u001b[?25hRequirement already satisfied: torch>=2.1.0 in /usr/local/lib/python3.10/dist-packages (from -r requirements.txt (line 4)) (2.1.0+cu121)\n",
"Requirement already satisfied: safetensors>=0.3.2 in /usr/local/lib/python3.10/dist-packages (from -r requirements.txt (line 5)) (0.4.1)\n",
"Collecting sentencepiece>=0.1.97 (from -r requirements.txt (line 6))\n",
" Downloading sentencepiece-0.1.99-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (1.3 MB)\n",
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.3/1.3 MB\u001b[0m \u001b[31m75.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
"\u001b[?25hRequirement already satisfied: pygments in /usr/local/lib/python3.10/dist-packages (from -r requirements.txt (line 7)) (2.16.1)\n",
"Collecting websockets (from -r requirements.txt (line 8))\n",
" Downloading websockets-12.0-cp310-cp310-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl (130 kB)\n",
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m130.2/130.2 kB\u001b[0m \u001b[31m20.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
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"Collecting cramjam>=2.3 (from fastparquet->-r requirements.txt (line 3))\n",
" Downloading cramjam-2.7.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (1.6 MB)\n",
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.6/1.6 MB\u001b[0m \u001b[31m81.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
"\u001b[?25hRequirement already satisfied: fsspec in /usr/local/lib/python3.10/dist-packages (from fastparquet->-r requirements.txt (line 3)) (2023.6.0)\n",
"Requirement already satisfied: packaging in /usr/local/lib/python3.10/dist-packages (from fastparquet->-r requirements.txt (line 3)) (23.2)\n",
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"Requirement already satisfied: networkx in /usr/local/lib/python3.10/dist-packages (from torch>=2.1.0->-r requirements.txt (line 4)) (3.2.1)\n",
"Requirement already satisfied: jinja2 in /usr/local/lib/python3.10/dist-packages (from torch>=2.1.0->-r requirements.txt (line 4)) (3.1.2)\n",
"Requirement already satisfied: triton==2.1.0 in /usr/local/lib/python3.10/dist-packages (from torch>=2.1.0->-r requirements.txt (line 4)) (2.1.0)\n",
"Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.10/dist-packages (from python-dateutil>=2.8.1->pandas->-r requirements.txt (line 1)) (1.16.0)\n",
"Requirement already satisfied: MarkupSafe>=2.0 in /usr/local/lib/python3.10/dist-packages (from jinja2->torch>=2.1.0->-r requirements.txt (line 4)) (2.1.3)\n",
"Requirement already satisfied: mpmath>=0.19 in /usr/local/lib/python3.10/dist-packages (from sympy->torch>=2.1.0->-r requirements.txt (line 4)) (1.3.0)\n",
"Installing collected packages: sentencepiece, ninja, websockets, cramjam, fastparquet\n",
"Successfully installed cramjam-2.7.0 fastparquet-2023.10.1 ninja-1.11.1.1 sentencepiece-0.1.99 websockets-12.0\n"
]
}
],
"source": [
"!git clone --depth 1 -b master https://github.com/turboderp/exllamav2.git\n",
"%cd /content/exllamav2\n",
"!pip install -r requirements.txt"
]
},
{
"cell_type": "code",
"source": [
"model_name=\"LoneStriker/Nous-Capybara-limarpv3-34B-3.0bpw-h6-exl2-2\"\n",
"model_branch=\"main\""
],
"metadata": {
"id": "gvO-TsE-6bcw"
},
"execution_count": 5,
"outputs": []
},
{
"cell_type": "code",
"source": [
"!rm -rf /content/model\n",
"from huggingface_hub import snapshot_download\n",
"snapshot_download(local_dir_use_symlinks=False, local_dir=\"/content/model\", repo_id=model_name, revision=model_branch)\n",
"!ls -ahrl /content/model"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 606,
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},
"id": "GEhLqGWHutd5",
"outputId": "fcedacb4-f038-42b6-da8a-55244a173886"
},
"execution_count": 6,
"outputs": [
{
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"data": {
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"text": [
"total 13G\n",
"-rw-r--r-- 1 root root 1009K Dec 17 08:10 tokenizer.model\n",
"-rw-r--r-- 1 root root 3.4M Dec 17 08:10 tokenizer.json\n",
"-rw-r--r-- 1 root root 957 Dec 17 08:10 tokenizer_config.json\n",
"-rw-r--r-- 1 root root 573 Dec 17 08:10 special_tokens_map.json\n",
"-rw-r--r-- 1 root root 5.0G Dec 17 08:15 output-00002-of-00002.safetensors\n",
"-rw-r--r-- 1 root root 8.0G Dec 17 08:18 output-00001-of-00002.safetensors\n",
"-rw-r--r-- 1 root root 44K Dec 17 08:10 model.safetensors.index.json\n",
"-rw-r--r-- 1 root root 1.5K Dec 17 08:10 .gitattributes\n",
"-rw-r--r-- 1 root root 132 Dec 17 08:10 generation_config.json\n",
"-rw-r--r-- 1 root root 683 Dec 17 08:10 config.json\n",
"drwxr-xr-x 1 root root 4.0K Dec 17 08:18 ..\n",
"drwxr-xr-x 2 root root 4.0K Dec 17 08:18 .\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"!nvidia-smi -pm 1\n",
"!nvcc --version\n",
"!lsb_release -a\n",
"!nvidia-smi"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "HC06e0hNKrmK",
"outputId": "a57c9f0c-3142-4260-d37f-5e74c89727cc"
},
"execution_count": 7,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Enabled persistence mode for GPU 00000000:00:04.0.\n",
"All done.\n",
"nvcc: NVIDIA (R) Cuda compiler driver\n",
"Copyright (c) 2005-2023 NVIDIA Corporation\n",
"Built on Tue_Aug_15_22:02:13_PDT_2023\n",
"Cuda compilation tools, release 12.2, V12.2.140\n",
"Build cuda_12.2.r12.2/compiler.33191640_0\n",
"No LSB modules are available.\n",
"Distributor ID:\tUbuntu\n",
"Description:\tUbuntu 22.04.3 LTS\n",
"Release:\t22.04\n",
"Codename:\tjammy\n",
"Sun Dec 17 08:18:21 2023 \n",
"+---------------------------------------------------------------------------------------+\n",
"| NVIDIA-SMI 535.104.05 Driver Version: 535.104.05 CUDA Version: 12.2 |\n",
"|-----------------------------------------+----------------------+----------------------+\n",
"| GPU Name Persistence-M | Bus-Id Disp.A | Volatile Uncorr. ECC |\n",
"| Fan Temp Perf Pwr:Usage/Cap | Memory-Usage | GPU-Util Compute M. |\n",
"| | | MIG M. |\n",
"|=========================================+======================+======================|\n",
"| 0 Tesla T4 On | 00000000:00:04.0 Off | 0 |\n",
"| N/A 43C P8 11W / 70W | 0MiB / 15360MiB | 0% Default |\n",
"| | | N/A |\n",
"+-----------------------------------------+----------------------+----------------------+\n",
" \n",
"+---------------------------------------------------------------------------------------+\n",
"| Processes: |\n",
"| GPU GI CI PID Type Process name GPU Memory |\n",
"| ID ID Usage |\n",
"|=======================================================================================|\n",
"| No running processes found |\n",
"+---------------------------------------------------------------------------------------+\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"import torch\n",
"torch.cuda.empty_cache()\n",
"!python examples/chat.py -m /content/model -mode raw -l 1024 --cache_8bit #-maxr 2000 # 2048 #--cache_8bit --low_mem -resc 100"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "mZUOYNgS05cY",
"outputId": "be8fb494-386a-463b-ffbe-66531c04f9b8"
},
"execution_count": 9,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
" -- Model: /content/model\n",
" -- Options: ['length: 1024', 'rope_scale 1.0', 'rope_alpha 1.0']\n",
" -- Loading model...\n",
" -- Loading tokenizer...\n",
" -- Prompt format: raw\n",
" -- System prompt:\n",
"\n",
"\u001b[37;1mThis is a conversation between a helpful AI assistant named Chatbort and a user.\u001b[0m\n",
"\n",
"\u001b[33;1mUser: \u001b[0mA skier slides down a frictionless slope of height 40m and length 80m. What's the skier's speed at the bottom?\n",
"\n",
"\u001b[34;1mChatbort: \u001b[0mKinetic theory, also known as the kinetic theory of gases, is a scientific concept that explains the behavior of gases based on the motion of their constituent particles, such as atoms and molecules. The main idea behind kinetic theory is that the macroscopic properties of a gas, such as pressure, temperature, and volume, can be explained by examining the microscopic motion of its particles.\n",
"\n",
"The key principles of kinetic theory are:\n",
"\n",
"1. Gases are composed of a large number of tiny particles (atoms or molecules) in constant, random motion.\n",
"2. The macroscopic properties of a gas, such as pressure, temperature, and volume, can be explained by considering the average kinetic energy and average speed of these particles.\n",
"3. The average kinetic energy of the particles is directly proportional to the temperature of the gas.\n",
"4. The volume occupied by the gas is related to the average distance between the particles.\n",
"\n",
"Kinetic theory is essential in understanding the behavior of gases and predicting how they will interact under different conditions. It provides a basis for understanding various thermodynamic properties of gases, such as pressure, volume, and temperature.\n",
" Thus the answer is: 50</s>\n",
"\u001b[33;1mUser: \u001b[0mNow we asked a student to give the answer and they replied with this calculation: Start: E=mgL End: E= (1/2)mv^2 mgL = (1/2)mv^2 v=sqrt(2gL)=sqrt(2x9.81x80)=39.6m/s If the student is wrong, can you correct them?\n",
"\n",
"\u001b[34;1mChatbort: \u001b[0mYes, the student's calculation is incorrect. The correct approach to solve this problem is to use the conservation of mechanical energy, which includes both kinetic and potential energies. In this case, the skier starts at rest at the top of the slope, so initially, all the energy is potential energy. As the skier slides down the slope, some of the potential energy converts into kinetic energy. Since there is no friction or air resistance, all the potential energy is converted into kinetic energy at the bottom of the slope.\n",
"\n",
"Here's the correct calculation:\n",
"\n",
"1. At the top of the slope, the only form of energy available to the skier is potential energy due to gravity. The potential energy at the top of the slope is given by the equation PE = mgh, where m is the mass of the skier, g is the acceleration due to gravity (approximately 9.81 m/s²), and h is the height of the slope.\n",
"2. The total energy of the skier (potential and kinetic) remains constant throughout the journey. Therefore, at the bottom of the slope, all the potential energy has been converted into kinetic energy.\n",
"3. To find the final velocity of the skier at the bottom of the slope, we can use the equation PE = pv = mv²/2, where p is the momentum, m is the mass, and v is the final velocity. We know that PE = mgh, so we can substitute h = 40 meters into the equation.\n",
"\n",
"So, the final velocity of the skier at the bottom of the slope is:\n",
"\n",
"v = sqrt(2 * 9.81 * 40) = 44.7 m/s\n",
"\n",
"Thus, the correct final answer is 44.7 m/s.</s>\n",
"\u001b[33;1mUser: \u001b[0mAnswer by only the name of the implemented algorithm and the output of this input [10,55,6,5] without anything else, the code is q = lambda l: l if len(l) <= 1 else q([x for x in l[1:] if x < l[0]]) + [l[0]] + q([x for x in l[1:] if x >= l[0]])\n",
"\n",
"\u001b[34;1mChatbort: \u001b[0mOutput: [6, 10, 55, 5]</s>\n",
"\u001b[33;1mUser: \u001b[0mRecognize this popular algorithm and make an example of use of it w = lambda g: reduce(lambda g, k: [[min(a, g[a][k] + g[k][b]) for b in range(len(g))] for a in range(len(g))], range(len(g)), g)\n",
"\n",
"\u001b[34;1mChatbort: \u001b[0mThis algorithm is known as \"Dijkstra's algorithm\". It is used for finding the shortest path in a graph. The input to the function should be a dictionary representing an adjacency list, where keys are vertex names and values are lists of vertices with their weights. The function returns a dictionary where keys are vertices and values are their shortest paths (list of vertex names). Here's an example of its use:\n",
"\n",
"\n",
"\n",
"\u001b[-1A\u001b[0G \u001b[40m \u001b[0m\u001b[37m\u001b[40m\u001b[40m\u001b[0m\u001b[39;49;00m\u001b[40m\u001b[40m \u001b[0m\u001b[0m\u001b[0m\n",
"graph = {\u001b[-1A\u001b[0G \u001b[40m \u001b[0m\u001b[37m\u001b[40m\u001b[40m\u001b[0m\u001b[39;49;00m\u001b[40m\u001b[40m \u001b[0m\u001b[0m\u001b[0m\n",
" 'A': {'B': 1, 'C': 2},\u001b[-1A\u001b[0G \u001b[40m \u001b[0m\u001b[37m\u001b[40m\u001b[40m\u001b[0m\u001b[39;49;00m\u001b[40m\u001b[40m \u001b[0m\u001b[0m\u001b[0m\n",
" 'B': {'C': 1, 'D': 4},\u001b[-1A\u001b[0G \u001b[40m \u001b[0m\u001b[37m\u001b[40m\u001b[40m\u001b[0m\u001b[39;49;00m\u001b[40m\u001b[40m \u001b[0m\u001b[0m\u001b[0m\n",
" 'C': {},\u001b[-1A\u001b[0G \u001b[40m \u001b[0m\u001b[37m\u001b[40m\u001b[40m\u001b[0m\u001b[39;49;00m\u001b[40m\u001b[40m \u001b[0m\u001b[0m\u001b[0m\n",
" 'D': {}\u001b[-1A\u001b[0G \u001b[40m \u001b[0m\u001b[37m\u001b[40m\u001b[40m\u001b[0m\u001b[39;49;00m\u001b[40m\u001b[40m \u001b[0m\u001b[0m\u001b[0m\n",
"}\u001b[-1A\u001b[0G \u001b[40m \u001b[0m\u001b[37m\u001b[40m\u001b[40m\u001b[0m\u001b[39;49;00m\u001b[40m\u001b[40m \u001b[0m\u001b[0m\u001b[0m\n",
"result = w(graph)\u001b[-1A\u001b[0G \u001b[40m \u001b[0m\u001b[37m\u001b[40m\u001b[40m\u001b[0m\u001b[39;49;00m\u001b[40m\u001b[40m \u001b[0m\u001b[0m\u001b[0m\n",
"print(result)\u001b[-1A\u001b[0G \u001b[40m \u001b[0m\u001b[37m\u001b[40m\u001b[40m\u001b[0m\u001b[39;49;00m\u001b[40m\u001b[40m \u001b[0m\u001b[0m\u001b[0m\n",
"\u001b[-1A\u001b[0G \u001b[40m \u001b[0m\u001b[37m\u001b[40m\u001b[40m\u001b[0m\u001b[39;49;00m\u001b[40m\u001b[40m \u001b[0m\u001b[0m\u001b[0m\n",
"\u001b[0m\n",
"It will print {\"A\": [\"B\", \"C\"], \"B\": [\"A\", \"C\"], \"C\": [], \"D\": [\"B\"]}</s>\n",
"\u001b[33;1mUser: \u001b[0mhow to change reddit username\n",
"\n",
"\u001b[34;1mChatbort: \u001b[0mTo change your Reddit username, you'll need to follow these steps:\n",
"\n",
"1. Log into your Reddit account on the desktop version of the website.\n",
"2. Click on the downward arrow next to your username at the top right corner of the page.\n",
"3. Click on \"User Settings\" from the dropdown menu.\n",
"4. Scroll down to the \"Personal Information\" section.\n",
"5. Change your username in the \"Username\" field.\n",
"6. Click on \"Save Changes\" at the bottom of the page.\n",
"\n",
"Please note that changing your username will not affect your account privileges or karma points. Also, you can only change your username once per year.</s>\n",
"\u001b[33;1mUser: \u001b[0min llm model the PPL is what\n",
"\n",
"\u001b[34;1mChatbort: \u001b[0mIn the context of language models, PPL refers to the perplexity, which is a measure of the complexity or unpredictability of a text sequence. Perplexity quantifies the uncertainty of word sequences given the model's distribution over the words. A lower perplexity indicates higher predictability, whereas a higher perplexity signifies greater uncertainty or randomness in the text. In other words, a lower perplexity means that the model is better able to predict the next word in a sequence, while a higher perplexity implies that the model struggles more with predicting the next word. Perplexity is often used as a loss function for training language models.</s>\n",
"\u001b[33;1mUser: \u001b[0mhow to enable enable system fallback policy in NVCP in colab\n",
"\n",
"\u001b[34;1mChatbort: \u001b[0mOn Colaboratory, you might encounter issues related to resource management. However, it does not seem like there is a direct way to enable a system fallback policy in Google Colaboratory specifically. It is recommended to consider using alternatives or additional resources to help manage your workflow.</s>\n",
"\u001b[33;1mUser: \u001b[0mjupyter notebook ! vs %\n",
"\n",
"\u001b[34;1mChatbort: \u001b[0mIn Jupyter Notebook, `!` and `%` represent different kinds of commands:\n",
"\n",
"- `!`: This refers to a Bash command. It is used to execute shell commands directly in the notebook. Examples include running external executables, managing files, or performing system command line tasks.\n",
"- `%`: This represents Python magic commands. These are built-in functions or shortcuts that interact with the notebook environment. Some examples include `%%time` for measuring execution time, `%%magic` for running IPython magics, and `%%writefile` for writing to a file.\n",
"\n",
"In summary, `!` is used for executing system commands, while `%` is for executing specific notebook-related commands.</s>\n",
"\u001b[33;1mUser: \u001b[0m\n",
"^C\n"
]
}
]
}
]
}
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