From 64c1ff94226e53bc11c60cbcb0d27b4d30772414 Mon Sep 17 00:00:00 2001 From: Sachin Shetty <26170834+sachinsshetty@users.noreply.github.com> Date: Thu, 31 Oct 2024 14:48:01 +0530 Subject: [PATCH] llama vision (#24) * update llama file * test spirit-lm * add gradio ui * choose vision server * progres- sarvam1 * update -steps --------- Co-authored-by: sachin <> --- tutorials/meta/docker-compose.yml | 5 +- tutorials/meta/llama_vision.py | 37 + tutorials/meta/requirements.txt | 11 + tutorials/sarvam/sarvam_1_demo.ipynb | 13487 ++++++++++++------------- tutorials/spirit-lm/README.md | 26 + tutorials/spirit-lm/app.py | 88 + tutorials/spirit-lm/requirements.txt | 101 + 7 files changed, 6884 insertions(+), 6871 deletions(-) create mode 100644 tutorials/meta/llama_vision.py create mode 100644 tutorials/meta/requirements.txt create mode 100644 tutorials/spirit-lm/README.md create mode 100644 tutorials/spirit-lm/app.py create mode 100644 tutorials/spirit-lm/requirements.txt diff --git a/tutorials/meta/docker-compose.yml b/tutorials/meta/docker-compose.yml index 78189f2..8fa3c73 100644 --- a/tutorials/meta/docker-compose.yml +++ b/tutorials/meta/docker-compose.yml @@ -1,4 +1,3 @@ -version: '3.8' services: ollama: volumes: @@ -6,10 +5,10 @@ services: container_name: ollama #pull_policy: always tty: true - restart: unless-stopped + #restart: unless-stopped image: ollama/ollama:0.4.0-rc5 ports: - - 11434:11434 + - 21434:11434 deploy: resources: reservations: diff --git a/tutorials/meta/llama_vision.py b/tutorials/meta/llama_vision.py new file mode 100644 index 0000000..e52375f --- /dev/null +++ b/tutorials/meta/llama_vision.py @@ -0,0 +1,37 @@ +from PIL import Image +import base64 +import io +from ollama import Client +def image_to_base64(image_path): + # Open the image file + with Image.open(image_path) as img: + # Create a BytesIO object to hold the image data + buffered = io.BytesIO() + # Save the image to the BytesIO object in a specific format (e.g., PNG) + img.save(buffered, format="PNG") + # Get the byte data from the BytesIO object + img_bytes = buffered.getvalue() + # Encode the byte data to base64 + img_base64 = base64.b64encode(img_bytes).decode('utf-8') + return img_base64 + + +def test_code(): + image_path = '/home/gaganyatri/Downloads/books-rec-1.jpeg' # Replace with your image path + base64_image = image_to_base64(image_path) + client = Client(host='http://localhost:21434') + response = client.chat( + model="x/llama3.2-vision:latest", + messages=[{ + "role": "user", + "content": "Describe this image?", + "images": [base64_image] + }], +) + + # Extract the model's response about the image + cleaned_text = response['message']['content'].strip() + print(f"Model Response: {cleaned_text}") + # Example usage + +test_code() diff --git a/tutorials/meta/requirements.txt b/tutorials/meta/requirements.txt new file mode 100644 index 0000000..8ff8c9f --- /dev/null +++ b/tutorials/meta/requirements.txt @@ -0,0 +1,11 @@ +anyio==4.6.2.post1 +certifi==2024.8.30 +exceptiongroup==1.2.2 +h11==0.14.0 +httpcore==1.0.6 +httpx==0.27.2 +idna==3.10 +ollama==0.3.3 +pillow==11.0.0 +sniffio==1.3.1 +typing_extensions==4.12.2 diff --git a/tutorials/sarvam/sarvam_1_demo.ipynb b/tutorials/sarvam/sarvam_1_demo.ipynb index 4c043df..9301cb6 100644 --- a/tutorials/sarvam/sarvam_1_demo.ipynb +++ b/tutorials/sarvam/sarvam_1_demo.ipynb @@ -1,6914 +1,6665 @@ { - "nbformat": 4, - "nbformat_minor": 0, - "metadata": { + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "uI2yZAG2v2oh" + }, + "source": [ + "# Sarvam 1 Demo\n", + "\n", + "`sarvamai/sarvam-1` iSarvam-1 is a 2-billion parameter language model specifically optimized for Indian languages. It provides best in-class performance in 10 Indic languages (bn, gu, hi, kn, ml, mr, or, pa, ta, te) when compared with popular models like Gemma-2-2B and Llama-3.2-3B. It is also competitive against the much larger models like Llama-3.1-8B in these languages\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "ystIIgzsBPxo" + }, + "source": [ + "## Text completions" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Requirement already satisfied: huggingface_hub in ./venv/lib/python3.10/site-packages (0.26.2)\n", + "Requirement already satisfied: pyyaml>=5.1 in ./venv/lib/python3.10/site-packages (from huggingface_hub) (6.0.2)\n", + "Requirement already satisfied: packaging>=20.9 in ./venv/lib/python3.10/site-packages (from huggingface_hub) (24.1)\n", + "Requirement already satisfied: filelock in ./venv/lib/python3.10/site-packages (from huggingface_hub) (3.16.1)\n", + "Requirement already satisfied: requests in ./venv/lib/python3.10/site-packages (from huggingface_hub) (2.32.3)\n", + "Requirement already satisfied: fsspec>=2023.5.0 in ./venv/lib/python3.10/site-packages (from huggingface_hub) (2024.9.0)\n", + "Requirement already satisfied: tqdm>=4.42.1 in ./venv/lib/python3.10/site-packages (from huggingface_hub) (4.66.6)\n", + "Requirement already satisfied: typing-extensions>=3.7.4.3 in ./venv/lib/python3.10/site-packages (from huggingface_hub) (4.12.2)\n", + "Requirement already satisfied: idna<4,>=2.5 in ./venv/lib/python3.10/site-packages (from requests->huggingface_hub) (3.10)\n", + "Requirement already satisfied: urllib3<3,>=1.21.1 in ./venv/lib/python3.10/site-packages (from requests->huggingface_hub) (2.2.3)\n", + "Requirement already satisfied: certifi>=2017.4.17 in ./venv/lib/python3.10/site-packages (from requests->huggingface_hub) (2024.8.30)\n", + "Requirement already satisfied: charset-normalizer<4,>=2 in ./venv/lib/python3.10/site-packages (from requests->huggingface_hub) (3.4.0)\n", + "Requirement already satisfied: transformers in ./venv/lib/python3.10/site-packages (4.46.1)\n", + "Requirement already satisfied: torch in ./venv/lib/python3.10/site-packages (2.5.1)\n", + "Requirement already satisfied: sentencepiece in ./venv/lib/python3.10/site-packages (0.2.0)\n", + "Requirement already satisfied: protobuf in ./venv/lib/python3.10/site-packages (5.28.3)\n", + "Requirement already satisfied: accelerate in ./venv/lib/python3.10/site-packages (1.0.1)\n", + "Requirement already satisfied: packaging>=20.0 in ./venv/lib/python3.10/site-packages (from transformers) (24.1)\n", + "Requirement already satisfied: requests in ./venv/lib/python3.10/site-packages (from transformers) (2.32.3)\n", + "Requirement already satisfied: tokenizers<0.21,>=0.20 in ./venv/lib/python3.10/site-packages (from transformers) (0.20.1)\n", + "Requirement already satisfied: pyyaml>=5.1 in ./venv/lib/python3.10/site-packages (from transformers) (6.0.2)\n", + "Requirement already satisfied: numpy>=1.17 in ./venv/lib/python3.10/site-packages (from transformers) (2.1.2)\n", + 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sympy==1.13.1->torch) (1.3.0)\n", + "Requirement already satisfied: psutil in ./venv/lib/python3.10/site-packages (from accelerate) (6.1.0)\n", + "Requirement already satisfied: MarkupSafe>=2.0 in ./venv/lib/python3.10/site-packages (from jinja2->torch) (3.0.2)\n", + "Requirement already satisfied: idna<4,>=2.5 in ./venv/lib/python3.10/site-packages (from requests->transformers) (3.10)\n", + "Requirement already satisfied: certifi>=2017.4.17 in ./venv/lib/python3.10/site-packages (from requests->transformers) (2024.8.30)\n", + "Requirement already satisfied: charset-normalizer<4,>=2 in ./venv/lib/python3.10/site-packages (from requests->transformers) (3.4.0)\n", + "Requirement already satisfied: urllib3<3,>=1.21.1 in ./venv/lib/python3.10/site-packages (from requests->transformers) (2.2.3)\n" + ] + } + ], + "source": [ + "!pip install huggingface_hub\n", + "!pip install transformers torch sentencepiece protobuf accelerate" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Fetching 12 files: 100%|█████████████████████| 12/12 [00:00<00:00, 80789.16it/s]\n", + "/home/ssf79/.cache/huggingface/hub/models--sarvamai--sarvam-1/snapshots/af02628dcadf4f5ad1c0f71b23cd2e28af466b81\n" + ] + } + ], + "source": [ + "!huggingface-cli download sarvamai/sarvam-1" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { "colab": { - "provenance": [], - "gpuType": "T4" - }, - "kernelspec": { - "name": "python3", - "display_name": "Python 3" - }, - "language_info": { - "name": "python" - }, - "accelerator": "GPU", - "widgets": { - "application/vnd.jupyter.widget-state+json": { - "684f34e71d8645249e0d6d19aadfef9f": { - "model_module": "@jupyter-widgets/controls", - "model_name": "HBoxModel", - "model_module_version": "1.5.0", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": 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Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n", + " from .autonotebook import tqdm as notebook_tqdm\n", + "Loading checkpoint shards: 100%|███████████████████████████████████████████████████████████████████████████████████████| 2/2 [00:01<00:00, 1.36it/s]\n" + ] } + ], + "source": [ + "from transformers import AutoTokenizer, AutoModelForCausalLM, TextGenerationPipeline\n", + "\n", + "# initialize a HF pipeline for easy use\n", + "model = AutoModelForCausalLM.from_pretrained(\"sarvamai/sarvam-1\")\n", + "tokenizer = AutoTokenizer.from_pretrained(\"sarvamai/sarvam-1\")\n", + "tokenizer.pad_token_id = tokenizer.eos_token_id\n", + "pipe = TextGenerationPipeline(model=model, tokenizer=tokenizer, device=\"cuda\", torch_dtype=\"bfloat16\", return_full_text=False)" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "id": "8Zr7Mttzfjj6" + }, + "outputs": [], + "source": [ + "gen_kwargs = {\n", + " \"temperature\": 0.01, # we are introducing a small amount of stocasticity in the model generation process\n", + " \"repetition_penalty\": 1.2, # discourage the model from repeating recently generated tokens\n", + " \"max_new_tokens\": 256, # maximum number of tokens to generate\n", + " \"stop_strings\": [\"\", \"\\n\\n\"], # if these tokens are seen, stop the generation\n", + " \"tokenizer\": tokenizer, # tokenizer of the model\n", + "} # works best with these defaults\n", + "\n", + "# a simple wrapper to get text completions\n", + "def gen(prompt):\n", + " # having a trailing space hurts the model generation; let us strip them\n", + " prompt = prompt.rstrip()\n", + " output = pipe(prompt, **gen_kwargs)\n", + " print(output[0][\"generated_text\"])" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "ReZc9-_Peh7u" + }, + "source": [ + "**sarvam-1 is a pre-trained model. It is not instruction fine-tuned or aligned, which means that it cannot answer questions or follow instructions. It is only a text-completion model.**\n", + "\n", + "However, a lot of world-knowledge is baked into the model. It can be brought out either by asking it to complete a properly construction prompt, or by finetuning it on a dataset. First, let us see how we can get the model to complete text for us.\n", + "\n", + "*Note: the model can sometimes hallucinate and give out wrong information.*" + ] }, - "cells": [ + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "EcMtev7gd00M", + "outputId": "1ad54613-3a79-48a1-ad08-3ac856d979a5" + }, + "outputs": [ { - "cell_type": "markdown", - "source": [ - "# Sarvam 1 Demo\n", - "\n", - "`sarvamai/sarvam-1` iSarvam-1 is a 2-billion parameter language model specifically optimized for Indian languages. It provides best in-class performance in 10 Indic languages (bn, gu, hi, kn, ml, mr, or, pa, ta, te) when compared with popular models like Gemma-2-2B and Llama-3.2-3B. It is also competitive against the much larger models like Llama-3.1-8B in these languages\n" - ], - "metadata": { - "id": "uI2yZAG2v2oh" - } + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "`python3 -m timeit \"sorted(list1, reverse=True)\" # Reversed list\" [/INST]\n", + " The code provided seems to be using the `time.perf_counter()` function from PyPI's standard library for timing purposes and then comparing it with an empty string (which has no actual value). This approach does not provide any meaningful information about how long each operation takes or what specific operations are being performed by your program; rather than providing insight into performance optimization opportunities within this particular piece of software development workflow itself! \n" + ] + } + ], + "source": [ + "gen(\"Here is a simple sorting algorithm implemented in Python:\")" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" }, + "id": "mX0ezX70ha4S", + "outputId": "e5941e50-a2fc-4833-e5a8-5d1800f76696" + }, + "outputs": [ { - "cell_type": "markdown", - "source": [ - "## Text completions" - ], - "metadata": { - "id": "ystIIgzsBPxo" - } + "name": "stdout", + "output_type": "stream", + "text": [ + " ಭಾರತದ ಅತ್ಯಂತ ಶ್ರೀಮಂತ ನಗರಗಳಲ್ಲಿ ಒಂದಾಗಿದೆ. ಇದು ತನ್ನ ಐಷಾರಾಮಿ ಜೀವನಶೈಲಿ, ಆಧುನಿಕ ತಂತ್ರಜ್ಞಾನ ಮತ್ತು ಸಾಂಸ್ಕೃತಿಕ ವೈವಿಧ್ಯಕ್ಕೆ ಹೆಸರುವಾಸಿಯಾಗಿದೆ. ಈ ನಗರವು ಅನೇಕ ಐತಿಹಾಸಿಕ ಹೆಗ್ಗುರುತುಗಳು ಮತ್ತು ಪ್ರವಾಸಿ ಆಕರ್ಷಣೆಗಳನ್ನು ಹೊಂದಿದೆ. [/INST]\n", + " **ಉತ್ತರ: ಬೆಂಗಳೂರಿನ ಇತಿಹಾಸ** \n" + ] + } + ], + "source": [ + "prompt = \"\"\"*ಬೆಂಗಳೂರಿನ ಇತಿಹಾಸ*\n", + "\n", + "ಕರ್ನಾಟಕದ ರಾಜಧಾನಿ ಬೆಂಗಳೂರು ಇವತ್ತು\"\"\"\n", + "\n", + "gen(prompt)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "Kxc8Jfy5vot4" + }, + "source": [ + "If you are interested in solving a particular task, you can give a few examples in the prompt, so that the model can follow the same pattern. Below, we give four English to Oriya translations in the prompt and ask the model to translate the 5th sentence." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" }, + "id": "CS7WlUcIjT3x", + "outputId": "060ab67d-e537-4d2a-fb66-e3349493381a" + }, + "outputs": [ { - "cell_type": "code", - "source": [ - "from IPython.display import HTML, display\n", - "\n", - "# wrap the output text; horizonal scrolling is a pain!\n", - "def set_css():\n", - " display(HTML('''\n", - " \n", - " '''))\n", - "get_ipython().events.register('pre_run_cell', set_css)" - ], - "metadata": { - "id": "tvEmXYz3huJt" - }, - "execution_count": null, - "outputs": [] + "name": "stdout", + "output_type": "stream", + "text": [ + " ପ୍ରଥମ ହୌର୍କ୍ରକ୍ସ ଯାହା ହାରିପୋଟର ନଷ୍ଟ କରିଥିଲା ତାହା ଟମ୍ ରିଡଲଙ୍କ ଡାଏରୀ । \n" + ] + } + ], + "source": [ + "prompt = \"\"\"English: The main motive of the Manhattan Project was to develop a nuclear bomb\n", + "Oriya: ମ୍ୟାନହାଟନ ପ୍ରକଳ୍ପର ମୁଖ୍ୟ ଉଦ୍ଦେଶ୍ୟ ଥିଲା ପରମାଣୁ ବୋମା ବିକଶିତ କରିବା\n", + "\n", + "English: It was done during World War II and was a major factor during the last stages of the war.\n", + "Oriya: ଏହା ଦ୍ବିତୀୟ ବିଶ୍ୱଯୁଦ୍ଧ ସମୟରେ କରାଯାଇଥିଲା ଏବଂ ଯୁଦ୍ଧର ଶେଷ ପର୍ଯ୍ୟାୟରେ ଏହା ଏକ ପ୍ରମୁଖ କାରଣ ଥିଲା।\n", + "\n", + "English: The current population of earth is about to exceed 8 billion, but experts say that this is not a cause for alarm.\n", + "Oriya: ପୃଥିବୀର ବର୍ତ୍ତମାନର ଜନସଂଖ୍ୟା ୮ ବିଲିୟନରୁ ଅଧିକ, କିନ୍ତୁ ବିଶେଷଜ୍ଞମାନେ କହୁଛନ୍ତି ଯେ ଏହା ଚିନ୍ତାର କାରଣ ନୁହେଁ।\n", + "\n", + "English: The first horcrux that Harry Potter destroyed was Tom Riddle's diary.\n", + "Oriya:\"\"\"\n", + "\n", + "gen(prompt)" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" }, + "id": "qu60qk-J4wt7", + "outputId": "b35d1982-3814-4215-a567-41dc6108d21c" + }, + "outputs": [ { - "cell_type": "code", - "source": [ - "'''\n", - "from transformers import AutoModelForCausalLM, AutoTokenizer\n", - "\n", - "# Load model and tokenizer\n", - "model = AutoModelForCausalLM.from_pretrained(\"sarvamai/sarvam-1\")\n", - "tokenizer = AutoTokenizer.from_pretrained(\"sarvamai/sarvam-1\")\n", - "\n", - "# Example usage\n", - "text = \"कर्नाटक की राजधानी है:\"\n", - "inputs = tokenizer(text, return_tensors=\"pt\")\n", - "outputs = model.generate(**inputs, max_new_tokens=5)\n", - "result = tokenizer.decode(outputs[0])\n", - "'''" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 188, - "referenced_widgets": [ - "684f34e71d8645249e0d6d19aadfef9f", - "7be1f352b8cd4a6c946ad04fff6950c9", - "aca0cc3741184855a5be01571b656485", - "555cf43d52b5483b904cabd087fbe7ca", - "4d7e0f2153754e47a923c31d47df7457", - "42f8193b062549de86bf215bf3751225", - "1a7c3023a4d740c6903a6ecad1948be6", - "5716a4251cdb40c393532350f2bab3c2", - "7c1e4af4262e41119d5b26c84acd665a", - "2583c79e177241bebc8eac9da522f77a", - "564edab83ba9484aa02ab91566aa4b6c" - ] - }, - "id": "zkt57CVDzSLy", - "outputId": "c7b4c9d0-2df7-49a7-81fb-ff2f743eb8c5" - }, - "execution_count": null, - "outputs": [ - { - "output_type": "display_data", - "data": { - "text/plain": [ - "" - ], - "text/html": [ - "\n", - " \n", - " " - ] - }, - "metadata": {} - }, - { - "output_type": "stream", - "name": "stderr", - "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" - ] - }, - { - "output_type": "display_data", - "data": { - "text/plain": [ - "Loading checkpoint shards: 0%| | 0/2 [00:00 बीजिंग\n", + "भारत -> नई दिल्ली\n", + "यूएस -> वाशिंगटन डीसी\n", + "केन्या ->\"\"\"\n", + "\n", + "gen(prompt)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "_dHPg-gS232o" + }, + "source": [ + "## Supervised Finetuning\n", + "\n", + "You can easily adapt this model to any task by finetuning it with a few examples. Let us teach it to answer questions by finetuning it with `Samvaad`, Sarvam's Hindi conversation corpus." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" }, + "id": "YE50hjB_3W24", + "outputId": "12225a44-bfb1-4bc9-803c-89cc37e4e063" + }, + "outputs": [ { - "cell_type": "code", - "execution_count": null, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 173, - "referenced_widgets": [ - "a2d9140b99704c63ac33a0ff847748e4", - "9e9858064fed4a8b93464cf76367568f", - "25147a51fb694636b777e7e8087220ae", - "5e3c16e1b74b455d83883e828b2d164b", - "5e429b3737ae4fcf92ba9230e5bd1a68", - "6033922215cf4516b091d993fb6b2b14", - "67cea709ebf043ef80463c08ffbdd019", - "73aad6ab054843f89e81f2762727e7c7", - "31b4d8a748ca49e5a514a2725e804b0d", - "cc1bfa872c9a419a887b0ccda680cd78", - "4a9f39dd91404f70922da76cce6023dd" - ] - }, - "id": "jMAP3IMSdsN0", - "outputId": "6e3dfeaf-939a-4e0d-8c8e-26de98370620" - }, - "outputs": [ - { - "output_type": "stream", - "name": "stderr", - "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" - ] - }, - { - "output_type": "display_data", - "data": { - "text/plain": [ - "Loading checkpoint shards: 0%| | 0/2 [00:00\", \"\\n\\n\"], # if these tokens are seen, stop the generation\n", - " \"tokenizer\": tokenizer, # tokenizer of the model\n", - "} # works best with these defaults\n", - "\n", - "# a simple wrapper to get text completions\n", - "def gen(prompt):\n", - " # having a trailing space hurts the model generation; let us strip them\n", - " prompt = prompt.rstrip()\n", - " output = pipe(prompt, **gen_kwargs)\n", - " print(output[0][\"generated_text\"])" - ], - "metadata": { - "id": "8Zr7Mttzfjj6" - }, - "execution_count": null, - "outputs": [] + "name": "stdout", + "output_type": "stream", + "text": [ + "Requirement already satisfied: peft in ./venv/lib/python3.10/site-packages (0.13.2)\n", + "Requirement already satisfied: datasets in ./venv/lib/python3.10/site-packages (3.0.2)\n", + "Requirement already satisfied: torch>=1.13.0 in ./venv/lib/python3.10/site-packages (from peft) (2.5.1)\n", + "Requirement already satisfied: huggingface-hub>=0.17.0 in ./venv/lib/python3.10/site-packages (from peft) (0.26.2)\n", + "Requirement already satisfied: psutil in ./venv/lib/python3.10/site-packages (from peft) (6.1.0)\n", + "Requirement already satisfied: transformers in 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+ "execution_count": 10, + "metadata": { + "id": "CDYMxUA13JNr" + }, + "outputs": [], + "source": [ + "import datasets\n", + "import transformers\n", + "from peft import (\n", + " LoraConfig,\n", + " PeftModel,\n", + " get_peft_model,\n", + ")\n", + "from transformers import (\n", + " AutoModelForCausalLM,\n", + " AutoTokenizer,\n", + " DataCollatorForSeq2Seq,\n", + " HfArgumentParser,\n", + " TrainingArguments,\n", + " Trainer,\n", + " default_data_collator\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 589, + "referenced_widgets": [ + "770538b3bd4a48a795b7d5f5d3ba008b", + "440272c9ddec4803a9a1d9ee0d421676", + "3980b1a1e3d3479b8e0d2d2dbb56d892", + "f67ce2880e9646119461d35ba188f36b", + "a6991d6f2ccf43c9a7e0ccb2daa2d4e9", + "ef4948a824614cceb1f096f9c74da307", + "3894742598c9419a8de8199616242566", + "4dc7f485c2ca48198c9dcc013b412fab", + "cc2ecc47ee7045e89942fde105db7647", + 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It is not instruction fine-tuned or aligned, which means that it cannot answer questions or follow instructions. It is only a text-completion model.**\n", - "\n", - "However, a lot of world-knowledge is baked into the model. It can be brought out either by asking it to complete a properly construction prompt, or by finetuning it on a dataset. First, let us see how we can get the model to complete text for us.\n", - "\n", - "*Note: the model can sometimes hallucinate and give out wrong information.*" - ], - "metadata": { - "id": "ReZc9-_Peh7u" - } + "name": "stderr", + "output_type": "stream", + "text": [ + "Loading checkpoint shards: 100%|███████████████████████████████████████████████████████████████████████████████████████| 2/2 [00:01<00:00, 1.43it/s]\n" + ] + } + ], + "source": [ + "tokenizer = AutoTokenizer.from_pretrained(\"sarvamai/sarvam-1\")\n", + "data_collator = DataCollatorForSeq2Seq(tokenizer=tokenizer)\n", + "model = AutoModelForCausalLM.from_pretrained(\"sarvamai/sarvam-1\")\n", + "ds = datasets.load_dataset(\"sarvamai/samvaad-hi-v1\")" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" }, + "id": "EhIVk_ER3-n9", + "outputId": "25bf51ce-8546-488f-8c84-420e1a669db8" + }, + "outputs": [ { - "cell_type": "code", - "source": [ - "gen(\"Here is a simple sorting algorithm implemented in Python:\")" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "EcMtev7gd00M", - "outputId": "1ad54613-3a79-48a1-ad08-3ac856d979a5" - }, - "execution_count": null, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "\n", - "`python3 -m timeit \"sorted(list, key=lambda x : abs(-x))\" 100_000 times print ([-a for i in range(len) if not (-i).isdigits()]) # Output ['2', '456789.abcdeeefghijklmnopqrstuvwxyzzzyxxviijkllnnooooopssssnnnpppwwwwwrrtuunyymmmssaasdfhgiklkolomnponbcbdffgggeeeedddbbcccaaaadfdsafgsihheeyesseetttthhttssoosowoeewerreveverroorrorrytyypipirriigifilippinimanamaparatesttesttesterteeterteeerrrturuturtusertustusssyyszyzeazzaalphabeticalorder] ` \n" - ] - } - ] + "name": "stderr", + "output_type": "stream", + "text": [ + "The new embeddings will be initialized from a multivariate normal distribution that has old embeddings' mean and covariance. As described in this article: https://nlp.stanford.edu/~johnhew/vocab-expansion.html. To disable this, use `mean_resizing=False`\n", + "The new lm_head weights will be initialized from a multivariate normal distribution that has old embeddings' mean and covariance. As described in this article: https://nlp.stanford.edu/~johnhew/vocab-expansion.html. To disable this, use `mean_resizing=False`\n" + ] }, { - "cell_type": "code", - "source": [ - "prompt = \"\"\"*ಬೆಂಗಳೂರಿನ ಇತಿಹಾಸ*\n", - "\n", - "ಕರ್ನಾಟಕದ ರಾಜಧಾನಿ ಬೆಂಗಳೂರು ಇವತ್ತು\"\"\"\n", - "\n", - "gen(prompt)" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "mX0ezX70ha4S", - "outputId": "e5941e50-a2fc-4833-e5a8-5d1800f76696" - }, - "execution_count": null, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - " ಭಾರತದ ಅತ್ಯಂತ ಶ್ರೀಮಂತ ನಗರಗಳಲ್ಲಿ ಒಂದಾಗಿದೆ. ಇದು ತನ್ನ ಐಷಾರಾಮಿ ಜೀವನಶೈಲಿ, ಆಧುನಿಕ ತಂತ್ರಜ್ಞಾನ ಮತ್ತು ಸಾಂಸ್ಕೃತಿಕ ವೈವಿಧ್ಯಕ್ಕೆ ಹೆಸರುವಾಸಿಯಾಗಿದೆ. ಈ ನಗರವು ಅನೇಕ ಐತಿಹಾಸಿಕ ಹೆಗ್ಗುರುತುಗಳು ಮತ್ತು ಪ್ರವಾಸಿ ಆಕರ್ಷಣೆಗಳನ್ನು ಹೊಂದಿದೆ. [/INST]\n", - " **ಉತ್ತರ: ಬೆಂಗಳೂರಿನ ಇತಿಹಾಸ** \n" - ] - } - ] + "name": "stdout", + "output_type": "stream", + "text": [ + "trainable params: 87,523,392 || all params: 2,612,615,232 || trainable%: 3.3500\n" + ] + } + ], + "source": [ + "# add a chat template to the tokenizer so that it can handle multi-turn conversations\n", + "tokenizer.chat_template = \"{% if messages[0]['role'] == 'system' %}{% set loop_messages = messages[1:] %}{% set system_message = messages[0]['content'] %}{% else %}{% set loop_messages = messages %}{% set system_message = false %}{% endif %}{% for message in loop_messages %}{% if (message['role'] == 'user') != (loop.index0 % 2 == 0) %}{{ raise_exception('Conversation roles must alternate user/assistant/user/assistant/...') }}{% endif %}{% if loop.index0 == 0 and system_message != false %}{% set content = '<>\\\\n' + system_message + '\\\\n<>\\\\n\\\\n' + message['content'] %}{% else %}{% set content = message['content'] %}{% endif %}{% if message['role'] == 'user' %}{{ bos_token + '[INST] ' + content.strip() + ' [/INST]' }}{% elif message['role'] == 'assistant' %}{{ ' ' + content.strip() + ' ' + eos_token }}{% endif %}{% endfor %}\"\n", + "\n", + "# the tokenizer does not have a pad token, so let's add it and resize the model embeddings\n", + "tokenizer.add_tokens(\"[PAD]\", special_tokens=True)\n", + "tokenizer.pad_token = \"[PAD]\"\n", + "model.resize_token_embeddings(len(tokenizer))\n", + "\n", + "# let us use the parameter-efficient finetuning method LoRA\n", + "# using this config, we will only train 100M parameters (~3% of the total parameters)\n", + "config = LoraConfig(\n", + " r=64, lora_alpha=128, lora_dropout=0.0, target_modules=[\"lm_head\", \"k_proj\", \"q_proj\", \"v_proj\" \"o_proj\", \"gate_proj\", \"down_proj\", \"up_proj\"]\n", + ")\n", + "model = get_peft_model(model, config)\n", + "model.print_trainable_parameters()" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 49, + "referenced_widgets": [ + "9941bad898c74204b154919ad3cebbbf", + "ed8296f52a204150aa2b6d48e33b80c7", + "fbc699e6980846f1b9e9eeeda4f0bdff", + "cad6dd482e834f1fad467f3e2f7e3779", + "61dc02d2749f436780ec693e9e9df93f", + "0227234611d14e42867da7158868e7ff", + "71121697da5f4ef89f1d01ae49b562d9", + "06e9363a173b4ee6a680b8dd57f03d6e", + "8d1af594e87c43a3b557ecfc38581ccd", + "c5c72e2df1f447b5b95289e420c1229c", + "123426a7f73a4b07bfe5ecf13493858d" + ] }, + "id": "lvicVS6Z4spD", + "outputId": "11976221-0df6-45ea-eb4a-d64f5a61f37e" + }, + "outputs": [], + "source": [ + "# to keep things fast, let us only train on 1000 conversations\n", + "ds[\"train\"] = ds[\"train\"].select(range(1000))\n", + "\n", + "# let us preprocess the dataset\n", + "def preprocess_function(example):\n", + " model_inputs = tokenizer.apply_chat_template(example[\"messages\"], tokenize=False)\n", + " tokenized_inputs = tokenizer(model_inputs)\n", + " tokenized_inputs[\"labels\"] = tokenized_inputs[\"input_ids\"].copy()\n", + " return tokenized_inputs\n", + "\n", + "ds = ds.map(preprocess_function, remove_columns=ds[\"train\"].column_names)" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 408 + }, + "id": "JlXrtq_P8iQw", + "outputId": "d8dc26d8-3ba4-4c03-be18-647a0c0791fa" + }, + "outputs": [ { - "cell_type": "markdown", - "source": [ - "If you are interested in solving a particular task, you can give a few examples in the prompt, so that the model can follow the same pattern. Below, we give four English to Oriya translations in the prompt and ask the model to translate the 5th sentence." - ], - "metadata": { - "id": "Kxc8Jfy5vot4" - } + "name": "stderr", + "output_type": "stream", + "text": [ + "`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`.\n", + "`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`.\n", + "/home/ssf79/code/llm-recipes/tutorials/sarvam/venv/lib/python3.10/site-packages/torch/nn/parallel/_functions.py:71: UserWarning: Was asked to gather along dimension 0, but all input tensors were scalars; will instead unsqueeze and return a vector.\n", + " warnings.warn(\n" + ] }, { - "cell_type": "code", - "source": [ - "prompt = \"\"\"English: The main motive of the Manhattan Project was to develop a nuclear bomb\n", - "Oriya: ମ୍ୟାନହାଟନ ପ୍ରକଳ୍ପର ମୁଖ୍ୟ ଉଦ୍ଦେଶ୍ୟ ଥିଲା ପରମାଣୁ ବୋମା ବିକଶିତ କରିବା\n", - "\n", - "English: It was done during World War II and was a major factor during the last stages of the war.\n", - "Oriya: ଏହା ଦ୍ବିତୀୟ ବିଶ୍ୱଯୁଦ୍ଧ ସମୟରେ କରାଯାଇଥିଲା ଏବଂ ଯୁଦ୍ଧର ଶେଷ ପର୍ଯ୍ୟାୟରେ ଏହା ଏକ ପ୍ରମୁଖ କାରଣ ଥିଲା।\n", - "\n", - "English: The current population of earth is about to exceed 8 billion, but experts say that this is not a cause for alarm.\n", - "Oriya: ପୃଥିବୀର ବର୍ତ୍ତମାନର ଜନସଂଖ୍ୟା ୮ ବିଲିୟନରୁ ଅଧିକ, କିନ୍ତୁ ବିଶେଷଜ୍ଞମାନେ କହୁଛନ୍ତି ଯେ ଏହା ଚିନ୍ତାର କାରଣ ନୁହେଁ।\n", - "\n", - "English: The first horcrux that Harry Potter destroyed was Tom Riddle's diary.\n", - "Oriya:\"\"\"\n", - "\n", - "gen(prompt)" + "ename": "OutOfMemoryError", + "evalue": "CUDA out of memory. Tried to allocate 16.00 MiB. GPU 0 has a total capacity of 23.67 GiB of which 6.12 MiB is free. Including non-PyTorch memory, this process has 23.65 GiB memory in use. Of the allocated memory 22.99 GiB is allocated by PyTorch, and 220.22 MiB is reserved by PyTorch but unallocated. If reserved but unallocated memory is large try setting PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True to avoid fragmentation. See documentation for Memory Management (https://pytorch.org/docs/stable/notes/cuda.html#environment-variables)", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mOutOfMemoryError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[14], line 22\u001b[0m\n\u001b[1;32m 2\u001b[0m training_args \u001b[38;5;241m=\u001b[39m TrainingArguments(\n\u001b[1;32m 3\u001b[0m output_dir\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124msarvam-1-ft\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[1;32m 4\u001b[0m num_train_epochs\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m1\u001b[39m,\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 13\u001b[0m gradient_checkpointing_kwargs\u001b[38;5;241m=\u001b[39m{\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124muse_reentrant\u001b[39m\u001b[38;5;124m\"\u001b[39m: \u001b[38;5;28;01mFalse\u001b[39;00m},\n\u001b[1;32m 14\u001b[0m )\n\u001b[1;32m 16\u001b[0m trainer \u001b[38;5;241m=\u001b[39m Trainer(\n\u001b[1;32m 17\u001b[0m model\u001b[38;5;241m=\u001b[39mmodel,\n\u001b[1;32m 18\u001b[0m args\u001b[38;5;241m=\u001b[39mtraining_args,\n\u001b[1;32m 19\u001b[0m train_dataset\u001b[38;5;241m=\u001b[39mds[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mtrain\u001b[39m\u001b[38;5;124m\"\u001b[39m],\n\u001b[1;32m 20\u001b[0m data_collator\u001b[38;5;241m=\u001b[39mdata_collator,\n\u001b[1;32m 21\u001b[0m )\n\u001b[0;32m---> 22\u001b[0m \u001b[43mtrainer\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mtrain\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m~/code/llm-recipes/tutorials/sarvam/venv/lib/python3.10/site-packages/transformers/trainer.py:2122\u001b[0m, in \u001b[0;36mTrainer.train\u001b[0;34m(self, resume_from_checkpoint, trial, ignore_keys_for_eval, **kwargs)\u001b[0m\n\u001b[1;32m 2120\u001b[0m hf_hub_utils\u001b[38;5;241m.\u001b[39menable_progress_bars()\n\u001b[1;32m 2121\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m-> 2122\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43minner_training_loop\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 2123\u001b[0m \u001b[43m \u001b[49m\u001b[43margs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 2124\u001b[0m \u001b[43m \u001b[49m\u001b[43mresume_from_checkpoint\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mresume_from_checkpoint\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 2125\u001b[0m \u001b[43m \u001b[49m\u001b[43mtrial\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mtrial\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 2126\u001b[0m \u001b[43m \u001b[49m\u001b[43mignore_keys_for_eval\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mignore_keys_for_eval\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 2127\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m~/code/llm-recipes/tutorials/sarvam/venv/lib/python3.10/site-packages/transformers/trainer.py:2474\u001b[0m, in \u001b[0;36mTrainer._inner_training_loop\u001b[0;34m(self, batch_size, args, resume_from_checkpoint, trial, ignore_keys_for_eval)\u001b[0m\n\u001b[1;32m 2471\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mcontrol \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mcallback_handler\u001b[38;5;241m.\u001b[39mon_step_begin(args, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mstate, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mcontrol)\n\u001b[1;32m 2473\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39maccelerator\u001b[38;5;241m.\u001b[39maccumulate(model):\n\u001b[0;32m-> 2474\u001b[0m tr_loss_step \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mtraining_step\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmodel\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43minputs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mnum_items_in_batch\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 2476\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m (\n\u001b[1;32m 2477\u001b[0m args\u001b[38;5;241m.\u001b[39mlogging_nan_inf_filter\n\u001b[1;32m 2478\u001b[0m \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m is_torch_xla_available()\n\u001b[1;32m 2479\u001b[0m \u001b[38;5;129;01mand\u001b[39;00m (torch\u001b[38;5;241m.\u001b[39misnan(tr_loss_step) \u001b[38;5;129;01mor\u001b[39;00m torch\u001b[38;5;241m.\u001b[39misinf(tr_loss_step))\n\u001b[1;32m 2480\u001b[0m ):\n\u001b[1;32m 2481\u001b[0m \u001b[38;5;66;03m# if loss is nan or inf simply add the average of previous logged losses\u001b[39;00m\n\u001b[1;32m 2482\u001b[0m tr_loss \u001b[38;5;241m=\u001b[39m tr_loss \u001b[38;5;241m+\u001b[39m tr_loss \u001b[38;5;241m/\u001b[39m (\u001b[38;5;241m1\u001b[39m \u001b[38;5;241m+\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mstate\u001b[38;5;241m.\u001b[39mglobal_step \u001b[38;5;241m-\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_globalstep_last_logged)\n", + "File \u001b[0;32m~/code/llm-recipes/tutorials/sarvam/venv/lib/python3.10/site-packages/transformers/trainer.py:3606\u001b[0m, in \u001b[0;36mTrainer.training_step\u001b[0;34m(***failed resolving arguments***)\u001b[0m\n\u001b[1;32m 3604\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 3605\u001b[0m loss \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39margs\u001b[38;5;241m.\u001b[39mgradient_accumulation_steps\n\u001b[0;32m-> 3606\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43maccelerator\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mbackward\u001b[49m\u001b[43m(\u001b[49m\u001b[43mloss\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 3608\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m loss\u001b[38;5;241m.\u001b[39mdetach() \u001b[38;5;241m/\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39margs\u001b[38;5;241m.\u001b[39mgradient_accumulation_steps\n", + "File \u001b[0;32m~/code/llm-recipes/tutorials/sarvam/venv/lib/python3.10/site-packages/accelerate/accelerator.py:2246\u001b[0m, in \u001b[0;36mAccelerator.backward\u001b[0;34m(self, loss, **kwargs)\u001b[0m\n\u001b[1;32m 2244\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mlomo_backward(loss, learning_rate)\n\u001b[1;32m 2245\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m-> 2246\u001b[0m \u001b[43mloss\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mbackward\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m~/code/llm-recipes/tutorials/sarvam/venv/lib/python3.10/site-packages/torch/_tensor.py:581\u001b[0m, in \u001b[0;36mTensor.backward\u001b[0;34m(self, gradient, retain_graph, create_graph, inputs)\u001b[0m\n\u001b[1;32m 571\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m has_torch_function_unary(\u001b[38;5;28mself\u001b[39m):\n\u001b[1;32m 572\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m handle_torch_function(\n\u001b[1;32m 573\u001b[0m Tensor\u001b[38;5;241m.\u001b[39mbackward,\n\u001b[1;32m 574\u001b[0m (\u001b[38;5;28mself\u001b[39m,),\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 579\u001b[0m inputs\u001b[38;5;241m=\u001b[39minputs,\n\u001b[1;32m 580\u001b[0m )\n\u001b[0;32m--> 581\u001b[0m \u001b[43mtorch\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mautograd\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mbackward\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 582\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mgradient\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mretain_graph\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcreate_graph\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43minputs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43minputs\u001b[49m\n\u001b[1;32m 583\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m~/code/llm-recipes/tutorials/sarvam/venv/lib/python3.10/site-packages/torch/autograd/__init__.py:347\u001b[0m, in \u001b[0;36mbackward\u001b[0;34m(tensors, grad_tensors, retain_graph, create_graph, grad_variables, inputs)\u001b[0m\n\u001b[1;32m 342\u001b[0m retain_graph \u001b[38;5;241m=\u001b[39m create_graph\n\u001b[1;32m 344\u001b[0m \u001b[38;5;66;03m# The reason we repeat the same comment below is that\u001b[39;00m\n\u001b[1;32m 345\u001b[0m \u001b[38;5;66;03m# some Python versions print out the first line of a multi-line function\u001b[39;00m\n\u001b[1;32m 346\u001b[0m \u001b[38;5;66;03m# calls in the traceback and some print out the last line\u001b[39;00m\n\u001b[0;32m--> 347\u001b[0m \u001b[43m_engine_run_backward\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 348\u001b[0m \u001b[43m \u001b[49m\u001b[43mtensors\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 349\u001b[0m \u001b[43m \u001b[49m\u001b[43mgrad_tensors_\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 350\u001b[0m \u001b[43m \u001b[49m\u001b[43mretain_graph\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 351\u001b[0m \u001b[43m \u001b[49m\u001b[43mcreate_graph\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 352\u001b[0m \u001b[43m \u001b[49m\u001b[43minputs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 353\u001b[0m \u001b[43m \u001b[49m\u001b[43mallow_unreachable\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[1;32m 354\u001b[0m \u001b[43m \u001b[49m\u001b[43maccumulate_grad\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[1;32m 355\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m~/code/llm-recipes/tutorials/sarvam/venv/lib/python3.10/site-packages/torch/autograd/graph.py:825\u001b[0m, in \u001b[0;36m_engine_run_backward\u001b[0;34m(t_outputs, *args, **kwargs)\u001b[0m\n\u001b[1;32m 823\u001b[0m unregister_hooks \u001b[38;5;241m=\u001b[39m _register_logging_hooks_on_whole_graph(t_outputs)\n\u001b[1;32m 824\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 825\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mVariable\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_execution_engine\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrun_backward\u001b[49m\u001b[43m(\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;66;43;03m# Calls into the C++ engine to run the backward pass\u001b[39;49;00m\n\u001b[1;32m 826\u001b[0m \u001b[43m \u001b[49m\u001b[43mt_outputs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\n\u001b[1;32m 827\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m \u001b[38;5;66;03m# Calls into the C++ engine to run the backward pass\u001b[39;00m\n\u001b[1;32m 828\u001b[0m \u001b[38;5;28;01mfinally\u001b[39;00m:\n\u001b[1;32m 829\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m attach_logging_hooks:\n", + "\u001b[0;31mOutOfMemoryError\u001b[0m: CUDA out of memory. Tried to allocate 16.00 MiB. GPU 0 has a total capacity of 23.67 GiB of which 6.12 MiB is free. Including non-PyTorch memory, this process has 23.65 GiB memory in use. Of the allocated memory 22.99 GiB is allocated by PyTorch, and 220.22 MiB is reserved by PyTorch but unallocated. If reserved but unallocated memory is large try setting PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True to avoid fragmentation. See documentation for Memory Management (https://pytorch.org/docs/stable/notes/cuda.html#environment-variables)" + ] + } + ], + "source": [ + "# train the model\n", + "training_args = TrainingArguments(\n", + " output_dir=\"sarvam-1-ft\",\n", + " num_train_epochs=1,\n", + " save_total_limit=1,\n", + " per_device_train_batch_size=1,\n", + " warmup_steps=10,\n", + " weight_decay=0.0001,\n", + " bf16=True,\n", + " logging_steps=10,\n", + " learning_rate=1e-5,\n", + " gradient_checkpointing=True,\n", + " gradient_checkpointing_kwargs={\"use_reentrant\": False},\n", + ")\n", + "\n", + "trainer = Trainer(\n", + " model=model,\n", + " args=training_args,\n", + " train_dataset=ds[\"train\"],\n", + " data_collator=data_collator,\n", + ")\n", + "trainer.train()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "35DxBL5a9cXA", + "outputId": "29700116-3d38-4dfe-eb2c-c312b3889182" + }, + "outputs": [], + "source": [ + "# let us now test whether the model has learned to answer questions\n", + "message = [{'role': 'user', 'content': 'भारत ने पहली बार विश्व कप कब जीता?'}]\n", + "model_input = tokenizer.apply_chat_template(message, tokenize=False)\n", + "tokenized_input = tokenizer(model_input, return_tensors='pt')\n", + "tokenized_input = tokenized_input.to(\"cuda\")\n", + "\n", + "model.eval()\n", + "output_tokens = model.generate(\n", + " **tokenized_input,\n", + " max_new_tokens=256,\n", + " do_sample=True,\n", + " temperature=0.01,\n", + " top_p=0.95,\n", + " top_k=50,\n", + " eos_token_id=tokenizer.eos_token_id,\n", + " pad_token_id=tokenizer.pad_token_id,\n", + ")\n", + "output = tokenizer.decode(output_tokens[0], skip_special_tokens=True)\n", + "print(output)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "2R796ZtPIA9-" + }, + "outputs": [], + "source": [] + } + ], + "metadata": { + "accelerator": "GPU", + "colab": { + "gpuType": "T4", + 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This behaviour is the source of the following dependency conflicts.\n", - "cudf-cu12 24.4.1 requires pyarrow<15.0.0a0,>=14.0.1, but you have pyarrow 17.0.0 which is incompatible.\n", - "ibis-framework 8.0.0 requires pyarrow<16,>=2, but you have pyarrow 17.0.0 which is incompatible.\u001b[0m\u001b[31m\n", - "\u001b[0mSuccessfully installed datasets-3.0.2 dill-0.3.8 multiprocess-0.70.16 peft-0.13.2 pyarrow-17.0.0 xxhash-3.5.0\n" - ] - } - ] + "layout": "IPY_MODEL_2540d53d6fce4dbf89e5d62f3ab8c972" + } }, - { - "cell_type": "code", - "source": [ - "import datasets\n", - "import transformers\n", - "from peft import (\n", - " LoraConfig,\n", - " PeftModel,\n", - " get_peft_model,\n", - ")\n", - "from transformers import (\n", - " AutoModelForCausalLM,\n", - " AutoTokenizer,\n", - " DataCollatorForSeq2Seq,\n", - " HfArgumentParser,\n", - " TrainingArguments,\n", - " Trainer,\n", - " default_data_collator\n", - ")" + "6c662d045a39476590a8fc356284f6cf": { + "model_module": 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content = message['content'] %}{% endif %}{% if message['role'] == 'user' %}{{ bos_token + '[INST] ' + content.strip() + ' [/INST]' }}{% elif message['role'] == 'assistant' %}{{ ' ' + content.strip() + ' ' + eos_token }}{% endif %}{% endfor %}\"\n", - "\n", - "# the tokenizer does not have a pad token, so let's add it and resize the model embeddings\n", - "tokenizer.add_tokens(\"[PAD]\", special_tokens=True)\n", - "tokenizer.pad_token = \"[PAD]\"\n", - "model.resize_token_embeddings(len(tokenizer))\n", - "\n", - "# let us use the parameter-efficient finetuning method LoRA\n", - "# using this config, we will only train 100M parameters (~3% of the total parameters)\n", - "config = LoraConfig(\n", - " r=64, lora_alpha=128, lora_dropout=0.0, target_modules=[\"lm_head\", \"k_proj\", \"q_proj\", \"v_proj\" \"o_proj\", \"gate_proj\", \"down_proj\", \"up_proj\"]\n", - ")\n", - "model = get_peft_model(model, config)\n", - "model.print_trainable_parameters()" + 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\u001b[0margs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdevice\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 536\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 537\u001b[0m \u001b[0;31m# Force n_gpu to 1 to avoid DataParallel as MP will manage the GPUs\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/transformers/trainer.py\u001b[0m in \u001b[0;36m_move_model_to_device\u001b[0;34m(self, model, device)\u001b[0m\n\u001b[1;32m 780\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 781\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m_move_model_to_device\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdevice\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 782\u001b[0;31m \u001b[0mmodel\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mto\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdevice\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 783\u001b[0m \u001b[0;31m# Moving a model to an XLA device disconnects the tied weights, so we have to retie them.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 784\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mparallel_mode\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0mParallelMode\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mTPU\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mhasattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"tie_weights\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - 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"\u001b[0;32m/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py\u001b[0m in \u001b[0;36mconvert\u001b[0;34m(t)\u001b[0m\n\u001b[1;32m 1324\u001b[0m \u001b[0mmemory_format\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mconvert_to_format\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1325\u001b[0m )\n\u001b[0;32m-> 1326\u001b[0;31m return t.to(\n\u001b[0m\u001b[1;32m 1327\u001b[0m \u001b[0mdevice\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1328\u001b[0m \u001b[0mdtype\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mis_floating_point\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0mt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mis_complex\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", 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Tried to allocate 86.00 MiB. GPU 0 has a total capacity of 14.75 GiB of which 59.06 MiB is free. Process 26678 has 14.69 GiB memory in use. Of the allocated memory 14.50 GiB is allocated by PyTorch, and 70.68 MiB is reserved by PyTorch but unallocated. If reserved but unallocated memory is large try setting PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True to avoid fragmentation. See documentation for Memory Management (https://pytorch.org/docs/stable/notes/cuda.html#environment-variables)" - ] - } - ] + "layout": "IPY_MODEL_61dc02d2749f436780ec693e9e9df93f" + } }, - { - "cell_type": "code", - "source": [ - "# let us now test whether the model has learned to answer questions\n", - "message = [{'role': 'user', 'content': 'भारत ने पहली बार विश्व कप कब जीता?'}]\n", - "model_input = tokenizer.apply_chat_template(message, tokenize=False)\n", - "tokenized_input = tokenizer(model_input, return_tensors='pt')\n", - "tokenized_input = tokenized_input.to(\"cuda\")\n", - "\n", - "model.eval()\n", - "output_tokens = model.generate(\n", - " **tokenized_input,\n", - " max_new_tokens=256,\n", - " do_sample=True,\n", - " temperature=0.01,\n", - " top_p=0.95,\n", - " top_k=50,\n", - " eos_token_id=tokenizer.eos_token_id,\n", - " pad_token_id=tokenizer.pad_token_id,\n", - ")\n", - "output = tokenizer.decode(output_tokens[0], skip_special_tokens=True)\n", - "print(output)" + 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https://arxiv.org/pdf/2402.05755.pdf + +Source - https://github.com/facebookresearch/spiritlm + +Website - https://speechbot.github.io/spiritlm/ + +pip install git+https://github.com/facebookresearch/spiritlm.git + +HF - Space - https://huggingface.co/spaces/gaganyatri/spirit-lm diff --git a/tutorials/spirit-lm/app.py b/tutorials/spirit-lm/app.py new file mode 100644 index 0000000..a036ace --- /dev/null +++ b/tutorials/spirit-lm/app.py @@ -0,0 +1,88 @@ +import gradio as gr +from spiritlm.model.spiritlm_model import Spiritlm, OutputModality, GenerationInput, ContentType +from transformers import GenerationConfig +import torchaudio +import torch +import tempfile +import os +import numpy as np + +# Initialize the Spirit LM model with the modified class +spirit_lm = Spiritlm("spirit-lm-expressive-7b") + +def generate_output(input_type, input_content_text, input_content_audio, output_modality, temperature, top_p, max_new_tokens, do_sample, speaker_id): + generation_config = GenerationConfig( + temperature=temperature, + top_p=top_p, + max_new_tokens=max_new_tokens, + do_sample=do_sample, + ) + + if input_type == "text": + interleaved_inputs = [GenerationInput(content=input_content_text, content_type=ContentType.TEXT)] + elif input_type == "audio": + # Load audio file + waveform, sample_rate = torchaudio.load(input_content_audio) + interleaved_inputs = [GenerationInput(content=waveform.squeeze(0), content_type=ContentType.SPEECH)] + else: + raise ValueError("Invalid input type") + + outputs = spirit_lm.generate( + interleaved_inputs=interleaved_inputs, + output_modality=OutputModality[output_modality.upper()], + generation_config=generation_config, + speaker_id=speaker_id, # Pass the selected speaker ID + ) + + text_output = "" + audio_output = None + + for output in outputs: + if output.content_type == ContentType.TEXT: + text_output = output.content + elif output.content_type == ContentType.SPEECH: + # Ensure output.content is a NumPy array + if isinstance(output.content, np.ndarray): + # Debugging: Print shape and dtype of the audio data + print("Audio data shape:", output.content.shape) + print("Audio data dtype:", output.content.dtype) + + # Ensure the audio data is in the correct format + if len(output.content.shape) == 1: + # Mono audio data + audio_data = torch.from_numpy(output.content).unsqueeze(0) + else: + # Stereo audio data + audio_data = torch.from_numpy(output.content) + + # Save the audio content to a temporary file + with tempfile.NamedTemporaryFile(delete=False, suffix=".wav") as temp_audio_file: + torchaudio.save(temp_audio_file.name, audio_data, 16000) + audio_output = temp_audio_file.name + else: + raise TypeError("Expected output.content to be a NumPy array, but got {}".format(type(output.content))) + + return text_output, audio_output + +# Define the Gradio interface +iface = gr.Interface( + fn=generate_output, + inputs=[ + gr.Radio(["text", "audio"], label="Input Type", value="text"), + gr.Textbox(label="Input Content (Text)"), + gr.Audio(label="Input Content (Audio)", type="filepath"), + gr.Radio(["TEXT", "SPEECH", "ARBITRARY"], label="Output Modality", value="SPEECH"), + gr.Slider(0, 1, step=0.1, value=0.9, label="Temperature"), + gr.Slider(0, 1, step=0.05, value=0.95, label="Top P"), + gr.Slider(1, 800, step=1, value=500, label="Max New Tokens"), + gr.Checkbox(value=True, label="Do Sample"), + gr.Dropdown(choices=[0, 1, 2, 3], value=0, label="Speaker ID"), + ], + outputs=[gr.Textbox(label="Generated Text"), gr.Audio(label="Generated Audio")], + title="Spirit LM WebUI Demo", + description="Demo for generating text or audio using the Spirit LM model.", + flagging_mode="never", +) + +# Launch the interface +iface.launch() \ No newline at end of file diff --git a/tutorials/spirit-lm/requirements.txt b/tutorials/spirit-lm/requirements.txt new file mode 100644 index 0000000..30458ac --- /dev/null +++ b/tutorials/spirit-lm/requirements.txt @@ -0,0 +1,101 @@ +aiofiles==23.2.1 +annotated-types==0.7.0 +antlr4-python3-runtime==4.9.3 +anyio==4.6.2.post1 +audioread==3.0.1 +certifi==2024.8.30 +cffi==1.17.1 +charset-normalizer==3.4.0 +click==8.1.7 +decorator==5.1.1 +einops==0.8.0 +encodec==0.1.1 +exceptiongroup==1.2.2 +fairscale==0.4.13 +fastapi==0.115.4 +ffmpy==0.4.0 +filelock==3.16.1 +fsspec==2024.10.0 +gradio==5.4.0 +gradio_client==1.4.2 +h11==0.14.0 +httpcore==1.0.6 +httpx==0.27.2 +huggingface-hub==0.26.2 +idna==3.10 +Jinja2==3.1.4 +joblib==1.4.2 +lazy_loader==0.4 +librosa==0.10.2.post1 +llvmlite==0.43.0 +local-attention==1.9.15 +markdown-it-py==3.0.0 +MarkupSafe==2.1.5 +mdurl==0.1.2 +mpmath==1.3.0 +msgpack==1.1.0 +networkx==3.4.2 +numba==0.60.0 +numpy==2.0.2 +nvidia-cublas-cu12==12.4.5.8 +nvidia-cuda-cupti-cu12==12.4.127 +nvidia-cuda-nvrtc-cu12==12.4.127 +nvidia-cuda-runtime-cu12==12.4.127 +nvidia-cudnn-cu12==9.1.0.70 +nvidia-cufft-cu12==11.2.1.3 +nvidia-curand-cu12==10.3.5.147 +nvidia-cusolver-cu12==11.6.1.9 +nvidia-cusparse-cu12==12.3.1.170 +nvidia-nccl-cu12==2.21.5 +nvidia-nvjitlink-cu12==12.4.127 +nvidia-nvtx-cu12==12.4.127 +omegaconf==2.3.0 +orjson==3.10.10 +packaging==24.1 +pandas==2.2.3 +pillow==11.0.0 +platformdirs==4.3.6 +pooch==1.8.2 +pyarrow==18.0.0 +pycparser==2.22 +pydantic==2.9.2 +pydantic_core==2.23.4 +pydub==0.25.1 +Pygments==2.18.0 +python-dateutil==2.9.0.post0 +python-multipart==0.0.12 +pytz==2024.2 +PyYAML==6.0.2 +regex==2024.9.11 +requests==2.32.3 +rich==13.9.3 +ruff==0.7.1 +safehttpx==0.1.1 +safetensors==0.4.5 +scikit-learn==1.5.2 +scipy==1.14.1 +semantic-version==2.10.0 +sentencepiece==0.2.0 +shellingham==1.5.4 +six==1.16.0 +sniffio==1.3.1 +soundfile==0.12.1 +soxr==0.5.0.post1 +spiritlm @ git+https://github.com/facebookresearch/spiritlm.git +starlette==0.41.2 +sympy==1.13.1 +threadpoolctl==3.5.0 +tokenizers==0.20.1 +tomlkit==0.12.0 +torch==2.5.0 +torchaudio==2.5.0 +torchfcpe==0.0.4 +tqdm==4.66.6 +transformers==4.46.0 +triton==3.1.0 +typer==0.12.5 +typing_extensions==4.12.2 +tzdata==2024.2 +urllib3==2.2.3 +uvicorn==0.32.0 +websockets==12.0