├── requirements.txt ├── README.md ├── LICENSE ├── app.py ├── .gitignore └── 🐭_a_human_can_create_a_DPO_dataset_with_smaller_models_by_looking_at_it.ipynb /requirements.txt: -------------------------------------------------------------------------------- 1 | openai 2 | python-dotenv -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # Synthetic-Data-Generation-using-LLM 2 | Synthetic Data Generation using LLM via Argilla, Distilabel, ChatGPT, etc. 3 | -------------------------------------------------------------------------------- /LICENSE: -------------------------------------------------------------------------------- 1 | MIT License 2 | 3 | Copyright (c) 2024 AI Anytime 4 | 5 | Permission is hereby granted, free of charge, to any person obtaining a copy 6 | of this software and associated documentation files (the "Software"), to deal 7 | in the Software without restriction, including without limitation the rights 8 | to use, copy, modify, merge, publish, distribute, sublicense, and/or sell 9 | copies of the Software, and to permit persons to whom the Software is 10 | furnished to do so, subject to the following conditions: 11 | 12 | The above copyright notice and this permission notice shall be included in all 13 | copies or substantial portions of the Software. 14 | 15 | THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR 16 | IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, 17 | FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE 18 | AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER 19 | LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, 20 | OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE 21 | SOFTWARE. 22 | -------------------------------------------------------------------------------- /app.py: -------------------------------------------------------------------------------- 1 | import os 2 | import openai 3 | 4 | def generate_reviews(prompt, count=1): 5 | reviews = [] 6 | 7 | for i in range(count): 8 | review_generated = False 9 | while not review_generated: 10 | 11 | # Generate a response using the ChatCompletion method 12 | response = openai.ChatCompletion.create( 13 | model="gpt-3.5-turbo", 14 | messages=[ 15 | {"role": "system", "content": "You are a helpful assistant."}, 16 | {"role": "user", "content": prompt} 17 | ] 18 | ) 19 | 20 | review = response.choices[0].message['content'].strip() 21 | word_count = len(review.split()) 22 | print("word count:", word_count) 23 | 24 | # Check if the word count is within the desired range 25 | if 15 <= word_count <= 70: 26 | print("counted") 27 | reviews.append(review) 28 | review_generated = True 29 | 30 | # Optional: Add a slight variation to the prompt for next iteration 31 | prompt += " Provide another perspective." 32 | 33 | return reviews 34 | 35 | prompt_text = "Write a 25 word positive review for a wireless earbud highlighting its battery life." 36 | num_datapoints = 5 37 | generated_reviews = generate_reviews(prompt_text, num_datapoints) 38 | 39 | for idx, review in enumerate(generated_reviews): 40 | print(f"Review {idx + 1}: {review}") -------------------------------------------------------------------------------- /.gitignore: -------------------------------------------------------------------------------- 1 | # Byte-compiled / optimized / DLL files 2 | __pycache__/ 3 | *.py[cod] 4 | *$py.class 5 | 6 | # C extensions 7 | *.so 8 | 9 | # Distribution / packaging 10 | .Python 11 | build/ 12 | develop-eggs/ 13 | dist/ 14 | downloads/ 15 | eggs/ 16 | .eggs/ 17 | lib/ 18 | lib64/ 19 | parts/ 20 | sdist/ 21 | var/ 22 | wheels/ 23 | share/python-wheels/ 24 | *.egg-info/ 25 | .installed.cfg 26 | *.egg 27 | MANIFEST 28 | 29 | # PyInstaller 30 | # Usually these files are written by a python script from a template 31 | # before PyInstaller builds the exe, so as to inject date/other infos into it. 32 | *.manifest 33 | *.spec 34 | 35 | # Installer logs 36 | pip-log.txt 37 | pip-delete-this-directory.txt 38 | 39 | # Unit test / coverage reports 40 | htmlcov/ 41 | .tox/ 42 | .nox/ 43 | .coverage 44 | .coverage.* 45 | .cache 46 | nosetests.xml 47 | coverage.xml 48 | *.cover 49 | *.py,cover 50 | .hypothesis/ 51 | .pytest_cache/ 52 | cover/ 53 | 54 | # Translations 55 | *.mo 56 | *.pot 57 | 58 | # Django stuff: 59 | *.log 60 | local_settings.py 61 | db.sqlite3 62 | db.sqlite3-journal 63 | 64 | # Flask stuff: 65 | instance/ 66 | .webassets-cache 67 | 68 | # Scrapy stuff: 69 | .scrapy 70 | 71 | # Sphinx documentation 72 | docs/_build/ 73 | 74 | # PyBuilder 75 | .pybuilder/ 76 | target/ 77 | 78 | # Jupyter Notebook 79 | .ipynb_checkpoints 80 | 81 | # IPython 82 | profile_default/ 83 | ipython_config.py 84 | 85 | # pyenv 86 | # For a library or package, you might want to ignore these files since the code is 87 | # intended to run in multiple environments; otherwise, check them in: 88 | # .python-version 89 | 90 | # pipenv 91 | # According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control. 92 | # However, in case of collaboration, if having platform-specific dependencies or dependencies 93 | # having no cross-platform support, pipenv may install dependencies that don't work, or not 94 | # install all needed dependencies. 95 | #Pipfile.lock 96 | 97 | # poetry 98 | # Similar to Pipfile.lock, it is generally recommended to include poetry.lock in version control. 99 | # This is especially recommended for binary packages to ensure reproducibility, and is more 100 | # commonly ignored for libraries. 101 | # https://python-poetry.org/docs/basic-usage/#commit-your-poetrylock-file-to-version-control 102 | #poetry.lock 103 | 104 | # pdm 105 | # Similar to Pipfile.lock, it is generally recommended to include pdm.lock in version control. 106 | #pdm.lock 107 | # pdm stores project-wide configurations in .pdm.toml, but it is recommended to not include it 108 | # in version control. 109 | # https://pdm.fming.dev/latest/usage/project/#working-with-version-control 110 | .pdm.toml 111 | .pdm-python 112 | .pdm-build/ 113 | 114 | # PEP 582; used by e.g. github.com/David-OConnor/pyflow and github.com/pdm-project/pdm 115 | __pypackages__/ 116 | 117 | # Celery stuff 118 | celerybeat-schedule 119 | celerybeat.pid 120 | 121 | # SageMath parsed files 122 | *.sage.py 123 | 124 | # Environments 125 | .env 126 | .venv 127 | env/ 128 | venv/ 129 | ENV/ 130 | env.bak/ 131 | venv.bak/ 132 | 133 | # Spyder project settings 134 | .spyderproject 135 | .spyproject 136 | 137 | # Rope project settings 138 | .ropeproject 139 | 140 | # mkdocs documentation 141 | /site 142 | 143 | # mypy 144 | .mypy_cache/ 145 | .dmypy.json 146 | dmypy.json 147 | 148 | # Pyre type checker 149 | .pyre/ 150 | 151 | # pytype static type analyzer 152 | .pytype/ 153 | 154 | # Cython debug symbols 155 | cython_debug/ 156 | 157 | # PyCharm 158 | # JetBrains specific template is maintained in a separate JetBrains.gitignore that can 159 | # be found at https://github.com/github/gitignore/blob/main/Global/JetBrains.gitignore 160 | # and can be added to the global gitignore or merged into this file. For a more nuclear 161 | # option (not recommended) you can uncomment the following to ignore the entire idea folder. 162 | #.idea/ 163 | -------------------------------------------------------------------------------- /🐭_a_human_can_create_a_DPO_dataset_with_smaller_models_by_looking_at_it.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "markdown", 5 | "source": [ 6 | "

\"drawing\" 🐭 a-human-can-create-a-DPO-dataset-with-smaller-models-by-looking-at-it

\n", 7 | "\n", 8 | "## Distilabel & Prometheus on **DPO** datasets\n", 9 | "\n", 10 | " This is a notebook for creating a DPO dataset from an SFT dataset with open source models. It uses a quantized version of [Prometheus 2](https://huggingface.co/prometheus-eval/prometheus-7b-v2.0), [distilabel](https://distilabel.argilla.io/latest/), and [Argilla](https://argilla.io/).\n", 11 | "\n", 12 | " Here's a handy blogpost on [DPO](https://argilla.io/blog/mantisnlp-rlhf-part-3/). The difference between a DPO dataset and an SFT one dataset is that a DPO dataset contains both 'chosen' and 'rejected' and rejected responses. Where as the SFT dataset contains a single response.\n", 13 | "\n", 14 | "\n", 15 | " ## 0. Install dependencies\n", 16 | "\n", 17 | " First, we need to install dependencies for this notebook: `distilabel`, `argilla`, and `llama-cpp`" 18 | ], 19 | "metadata": { 20 | "id": "A9UE1A3gBlty" 21 | } 22 | }, 23 | { 24 | "cell_type": "code", 25 | "execution_count": 1, 26 | "metadata": { 27 | "id": "h1HQI2fyMWeJ", 28 | "outputId": "cdc70336-a54f-42e0-bb27-45584f9f9fb8", 29 | "colab": { 30 | "base_uri": "https://localhost:8080/" 31 | } 32 | }, 33 | "outputs": [ 34 | { 35 | "output_type": "stream", 36 | "name": "stdout", 37 | "text": [ 38 | "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m49.4/49.4 MB\u001b[0m \u001b[31m34.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", 39 | "\u001b[?25h Installing build dependencies ... \u001b[?25l\u001b[?25hdone\n", 40 | " Getting requirements to build wheel ... \u001b[?25l\u001b[?25hdone\n", 41 | " Installing backend dependencies ... \u001b[?25l\u001b[?25hdone\n", 42 | " Preparing metadata (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n", 43 | "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m45.5/45.5 kB\u001b[0m \u001b[31m7.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", 44 | "\u001b[?25h Building wheel for llama-cpp-python (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n", 45 | "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m53.1/53.1 kB\u001b[0m \u001b[31m1.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", 46 | "\u001b[?25h Installing build dependencies ... \u001b[?25l\u001b[?25hdone\n", 47 | " Getting requirements to build wheel ... \u001b[?25l\u001b[?25hdone\n", 48 | " Preparing metadata (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n", 49 | "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m542.0/542.0 kB\u001b[0m \u001b[31m11.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", 50 | "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m75.6/75.6 kB\u001b[0m \u001b[31m13.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", 51 | "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m134.8/134.8 kB\u001b[0m \u001b[31m23.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", 52 | "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m142.5/142.5 kB\u001b[0m \u001b[31m21.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", 53 | "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m46.7/46.7 kB\u001b[0m \u001b[31m7.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", 54 | "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m116.3/116.3 kB\u001b[0m \u001b[31m18.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", 55 | "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m194.1/194.1 kB\u001b[0m \u001b[31m26.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", 56 | "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m77.9/77.9 kB\u001b[0m \u001b[31m12.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", 57 | "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m58.3/58.3 kB\u001b[0m \u001b[31m9.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", 58 | "\u001b[?25h Building wheel for distilabel (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n", 59 | "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m421.2/421.2 kB\u001b[0m \u001b[31m10.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", 60 | "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m75.9/75.9 kB\u001b[0m \u001b[31m12.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", 61 | "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m17.1/17.1 MB\u001b[0m \u001b[31m38.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", 62 | "\u001b[?25h\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n", 63 | "chex 0.1.86 requires numpy>=1.24.1, but you have numpy 1.23.5 which is incompatible.\n", 64 | "pandas-stubs 2.0.3.230814 requires numpy>=1.25.0; python_version >= \"3.9\", but you have numpy 1.23.5 which is incompatible.\u001b[0m\u001b[31m\n", 65 | "\u001b[0m" 66 | ] 67 | } 68 | ], 69 | "source": [ 70 | "!CMAKE_ARGS=\"-DLLAMA_CUDA=on\" pip install -qqq llama-cpp-python\n", 71 | "!pip install -qqq pynvml\n", 72 | "!pip install -qqq --upgrade \"distilabel[llama-cpp] @ git+https://github.com/argilla-io/distilabel.git@develop\"\n", 73 | "!pip install -qqq huggingface_hub argilla" 74 | ] 75 | }, 76 | { 77 | "cell_type": "markdown", 78 | "source": [ 79 | "## 0. Autheticate\n", 80 | "\n", 81 | "Next, we'll need to login into huggingface because we're going to create a space and pull some models." 82 | ], 83 | "metadata": { 84 | "id": "1fK4xmL4KJL2" 85 | } 86 | }, 87 | { 88 | "cell_type": "code", 89 | "source": [ 90 | "from huggingface_hub import notebook_login, hf_hub_download, duplicate_space, whoami\n", 91 | "\n", 92 | "notebook_login()\n", 93 | "\n", 94 | "HF_USERNAME = whoami()[\"name\"]" 95 | ], 96 | "metadata": { 97 | "id": "KQ0Cb4Y8FsGe", 98 | "outputId": "0b155974-f79b-44aa-d37e-61183811ec24", 99 | "colab": { 100 | "base_uri": "https://localhost:8080/", 101 | "height": 145, 102 | "referenced_widgets": [ 103 | "e4d9805eb6f24907bc733e3f2f767246", 104 | "c8a80891ec4b4170b2aa13a01e4083f0", 105 | "721f360d4c114fde8c653eb556e49ca8", 106 | "44a869e66a16403aa0afdc1b4f1c7cb1", 107 | "4f95510802c84f6fba0b70343ef2d3d4", 108 | "75e591e1a9ba4cbc97cb7c5f5487b690", 109 | "9c7b9c7b390340a59fe091d837b58725", 110 | "a1eb05d046a44d1390cf9a9d07ff424a", 111 | "454824dc47884005a988132b68db50c7", 112 | "6631c5904cc34bdcbfba337cc7adcfd5", 113 | "48ead913aaed42468df1d2de2447036f", 114 | "aedae1c750f44e039626c2e1306728e4", 115 | "82a9ffe888f9466fb0cd3465555df187", 116 | "438b939577554e039d8a7a0d355f2921", 117 | "b99b8e793e20427cbd1533ede726cbe3", 118 | "764e6c6586ab49aba717d18c6ac25a1a", 119 | "35751d39de294ac088c67e5c547ba388", 120 | "0d6a569abec040fea9b83f777b47eb1e", 121 | "dd44c362014e4b9e8ec0efcf3d424701", 122 | "a31247571b5b4ecc8b5832d893083627", 123 | "02c0de3c9ccd44ac80420f70055b13a5", 124 | "3ed5b651922d43b1bd8e829f273b65e3", 125 | "f2300c433d2848c2b8e2620d1ccda9fb", 126 | "07c9ef86722142659c31edc23ab4a4e3", 127 | "0785f89002724084bef32bf4fca9a627", 128 | "af630d3259e140ca8335f204cfd6bab5", 129 | "ca98c022721d4fd98ab0fa9c2881d687", 130 | "32685f46223042519cbb5f8575d7c603", 131 | "b5ea8a8d735b430e89f302770e538a6d", 132 | "519e24ca644a4a839ed54f8f46717d19", 133 | "69f947f7a7414e40955b97e1d6a57c77", 134 | "7386d6ec9f2a4c5ead98693ee69361fd" 135 | ] 136 | } 137 | }, 138 | "execution_count": 2, 139 | "outputs": [ 140 | { 141 | "output_type": "display_data", 142 | "data": { 143 | "text/plain": [ 144 | "VBox(children=(HTML(value='
I recomend starting from the default configuration and experiment from there.\n", 164 | "\n", 165 | "---\n", 166 | "\n" 167 | ], 168 | "metadata": { 169 | "id": "q1ZwXW3TLCna" 170 | } 171 | }, 172 | { 173 | "cell_type": "code", 174 | "source": [ 175 | "# @markdown ---\n", 176 | "# @markdown ### 🧹 Define the dataset to cleanup:\n", 177 | "\n", 178 | "# @markdown What's the name of your project? This will be used to create datasets and spaces so it should be unique.\n", 179 | "PROJECT_NAME = \"prometheus-text-generation-project\" # @param {type:\"string\"}\n", 180 | "\n", 181 | "# @markdown What SFT dataset on the hub should we start from:\n", 182 | "INPUT_DATASET_REPO_ID = \"openbmb/UltraInteract_sft\" # @param {type:\"string\"}\n", 183 | "\n", 184 | "# @markdown What are the instruction and response columns named in the dataset?\n", 185 | "RESPONSE_COLUMN_NAME = \"response\" # @param {type:\"string\"}\n", 186 | "INSTRUCTION_COLUMN_NAME = \"instruction\" # @param {type:\"string\"}\n", 187 | "EVALUATION_RUBRIC = \"factual-validity\" # @param [\"helpfulness\", \"harmlessness\", \"honesty\", \"factual-validity\", \"reasoning\"]\n", 188 | "\n", 189 | "# @markdown ---\n", 190 | "\n", 191 | "# @markdown 🤗 Model Selection\n", 192 | "\n", 193 | "# @markdown Define the quantized models that you want to use for generation and evaluation. Note this notebook is based on Llama-cpp so you will need gguf files, or to adapt the implementation.\n", 194 | "\n", 195 | "# @markdown **Prometheus Evaluation Model**\n", 196 | "PROMETHEUS_MODEL_REPO = \"AlekseiPravdin/prometheus-7b-v2_0-gguf\" # @param {type:\"string\"}\n", 197 | "PROMETHEUS_MODEL_PATH = \"prometheus-7b-v2_0.q2_k.gguf\" # @param {type:\"string\"}\n", 198 | "\n", 199 | "# @markdown **Text Generation Model**\n", 200 | "GENERATION_MODEL_REPO = \"microsoft/Phi-3-mini-4k-instruct-gguf\"# @param {type:\"string\"}\n", 201 | "GENERATION_MODEL_PATH = \"Phi-3-mini-4k-instruct-q4.gguf\" # @param {type:\"string\"}\n", 202 | "\n", 203 | "# @markdown You could advantage of different compute options by using other GGUF files in https://huggingface.co/AlekseiPravdin/prometheus-7b-v2_0-gguf\n", 204 | "\n", 205 | "# @markdown ---\n", 206 | "\n", 207 | "# @markdown 📖 Prometheus Model Configuration\n", 208 | "\n", 209 | "# @markdown Refine the promethus configuration based on feedback. Start from the defaults.\n", 210 | "TEMPERATURE = 0.7 # @param {type:\"slider\", min:0, max:1, step:0.1}\n", 211 | "MAX_TOKENS = 512 # @param {type:\"slider\", min:64, max:2048, step:64}\n", 212 | "NUM_SAMPLES = 10 # @param {type:\"slider\", min:5, max:500, step:10}\n", 213 | "# @markdown ---\n", 214 | "\n", 215 | "DATASET_REPO_ID = f\"{HF_USERNAME}/{PROJECT_NAME}\"\n", 216 | "SPACE_REPO_ID = f\"{HF_USERNAME}/{PROJECT_NAME}-argilla\"\n", 217 | "ARGILLA_URL = f\"https://{HF_USERNAME}-{PROJECT_NAME}-argilla.hf.space\"\n", 218 | "\n", 219 | "prometheus_path = hf_hub_download(\n", 220 | " repo_id=PROMETHEUS_MODEL_REPO, filename=PROMETHEUS_MODEL_PATH, repo_type=\"model\"\n", 221 | ")\n", 222 | "phi3_path = hf_hub_download(\n", 223 | " repo_id=GENERATION_MODEL_REPO, filename=GENERATION_MODEL_PATH, repo_type=\"model\"\n", 224 | ")\n", 225 | "duplicate_space(\n", 226 | " from_id=\"argilla/argilla-template-space\",\n", 227 | " to_id=SPACE_REPO_ID,\n", 228 | " private=False,\n", 229 | " exist_ok=True,\n", 230 | ")" 231 | ], 232 | "metadata": { 233 | "id": "UXUQGa_KGjkK", 234 | "outputId": "c911df47-635f-43fb-c2a6-43be7bece972", 235 | "colab": { 236 | "base_uri": "https://localhost:8080/", 237 | "height": 116, 238 | "referenced_widgets": [ 239 | "d8b33bb641c945ee85bb94cfe0ff58cf", 240 | "264e3e750bd143f09d822ee8ac39200a", 241 | "ac9dbf10d9224fa58671a8c3fe381c0c", 242 | "1f89b563ab7a4734876d12bd1adfbd82", 243 | "85d28ff296bf4fac905e894d5cc8191c", 244 | "1173fe912b6f46e694100d84f95ba9c2", 245 | "31c99b8242f2486ba9e7d9790a5c6932", 246 | "fa50c42a7aa04fb385fd91ae1e6b6657", 247 | "239a38f9d1b64ac3b1ef91d87d3a604a", 248 | "07dc27a7a5e646f19c87531dfc904391", 249 | "d9fd216c8fe2415a9f0b47b161bd3345", 250 | "b09ea4131057463fb44f565ac75a0cfa", 251 | "3273f8c539304f70bfd000b3f599bc30", 252 | "5b3edbf8b1394ba99222e1f1ff001817", 253 | "79c40c09a18149ce8fc5a4f28012fe55", 254 | "76030354886d4d40aaabdaa71df53534", 255 | "94c66744dcbd422194f15d5bc0c51916", 256 | "069bbd4936ec4039a43b75a43eb92ce7", 257 | "1b83de7e86f2436b974cd3c214961b82", 258 | "231738af3e2341b09e8d9973cd32ad61", 259 | "60a5def5fd41426ba469f287d3f425eb", 260 | "8374678db4124801a9f252e4c65237e3" 261 | ] 262 | } 263 | }, 264 | "execution_count": 3, 265 | "outputs": [ 266 | { 267 | "output_type": "display_data", 268 | "data": { 269 | "text/plain": [ 270 | "prometheus-7b-v2_0.q2_k.gguf: 0%| | 0.00/2.72G [00:00[05/29/24 11:40:40] INFO ['distilabel.pipeline.local'] 📝 Pipeline data will be written to local.py:128\n", 588 | " '/root/.cache/distilabel/pipelines/prometheus/5e039100df81858364b22fa557f \n", 589 | " 58461f827ed58/data' \n", 590 | "\n" 591 | ] 592 | }, 593 | "metadata": {} 594 | }, 595 | { 596 | "output_type": "display_data", 597 | "data": { 598 | "text/plain": [ 599 | "\u001b[2;36m \u001b[0m\u001b[2;36m \u001b[0m\u001b[34mINFO \u001b[0m \u001b[1m[\u001b[0m\u001b[32m'distilabel.pipeline.local'\u001b[0m\u001b[1m]\u001b[0m ⏳ Waiting for all the steps to load\u001b[33m...\u001b[0m \u001b]8;id=74635;file:///usr/local/lib/python3.10/dist-packages/distilabel/pipeline/local.py\u001b\\\u001b[2mlocal.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=151061;file:///usr/local/lib/python3.10/dist-packages/distilabel/pipeline/local.py#467\u001b\\\u001b[2m467\u001b[0m\u001b]8;;\u001b\\\n" 600 | ], 601 | "text/html": [ 602 | "
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 795 |               "                             'combine_columns'                                                                     \n",
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 823 |               "                             output queue                                                                          \n",
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 864 |               "                             to output queue                                                                       \n",
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 892 |               "                             'push_to_argilla'                                                                     \n",
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\n" 894 | ] 895 | }, 896 | "metadata": {} 897 | }, 898 | { 899 | "output_type": "stream", 900 | "name": "stderr", 901 | "text": [ 902 | "/usr/local/lib/python3.10/dist-packages/argilla/client/client.py:178: UserWarning: No workspace configuration was detected. To work with Argilla datasets, specify a valid workspace name on `rg.init` or set it up through the `rg.set_workspace` function.\n", 903 | " warnings.warn(\n" 904 | ] 905 | }, 906 | { 907 | "output_type": "display_data", 908 | "data": { 909 | "text/plain": [ 910 | "\u001b[2;36m[05/29/24 11:41:39]\u001b[0m\u001b[2;36m \u001b[0m\u001b[31mWARNING \u001b[0m \u001b[1m[\u001b[0m\u001b[32m'distilabel.step.push_to_argilla'\u001b[0m\u001b[1m]\u001b[0m ⚠️ Processing batch \u001b[1;36m0\u001b[0m with step \u001b]8;id=116607;file:///usr/local/lib/python3.10/dist-packages/distilabel/pipeline/local.py\u001b\\\u001b[2mlocal.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=135234;file:///usr/local/lib/python3.10/dist-packages/distilabel/pipeline/local.py#975\u001b\\\u001b[2m975\u001b[0m\u001b]8;;\u001b\\\n", 911 | "\u001b[2;36m \u001b[0m \u001b[32m'push_to_argilla'\u001b[0m failed. Sending empty batch filled with `\u001b[3;35mNone\u001b[0m`s\u001b[33m...\u001b[0m \u001b[2m \u001b[0m\n" 912 | ], 913 | "text/html": [ 914 | "
[05/29/24 11:41:39] WARNING  ['distilabel.step.push_to_argilla'] ⚠️ Processing batch 0 with step        local.py:975\n",
 915 |               "                             'push_to_argilla' failed. Sending empty batch filled with `None`s...                  \n",
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                    WARNING  ['distilabel.step.push_to_argilla'] Subprocess traceback:                 local.py:979\n",
 949 |               "                                                                                                                   \n",
 950 |               "                             Traceback (most recent call last):                                                    \n",
 951 |               "                               File                                                                                \n",
 952 |               "                             \"/usr/local/lib/python3.10/dist-packages/distilabel/pipeline/local.py\",               \n",
 953 |               "                             line 959, in _non_generator_process_loop                                              \n",
 954 |               "                                 result = next(self.step.process_applying_mappings(*batch.data))                   \n",
 955 |               "                               File                                                                                \n",
 956 |               "                             \"/usr/local/lib/python3.10/dist-packages/distilabel/steps/base.py\", line              \n",
 957 |               "                             555, in process_applying_mappings                                                     \n",
 958 |               "                                 for output_rows in generator:                                                     \n",
 959 |               "                               File \"<ipython-input-5-a12f50551f63>\", line 47, in DPOToArgilla                     \n",
 960 |               "                                 dataset = rg.FeedbackDataset.from_argilla(name=\"honest_preferences\",              \n",
 961 |               "                             workspace=\"admin\")                                                                    \n",
 962 |               "                               File                                                                                \n",
 963 |               "                             \"/usr/local/lib/python3.10/dist-packages/argilla/client/feedback/dataset/             \n",
 964 |               "                             local/mixins.py\", line 339, in from_argilla                                           \n",
 965 |               "                                 raise ValueError(                                                                 \n",
 966 |               "                             ValueError: Could not find a `FeedbackDataset` in Argilla with                        \n",
 967 |               "                             name='honest_preferences' and workspace='admin'.                                      \n",
 968 |               "                                                                                                                   \n",
 969 |               "
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 983 |               "                             batch 0 to output queue                                                               \n",
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                    INFO     ['distilabel.step.push_to_argilla'] 🏁 Finished running step              local.py:873\n",
 998 |               "                             'push_to_argilla'                                                                     \n",
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\n" 1000 | ] 1001 | }, 1002 | "metadata": {} 1003 | }, 1004 | { 1005 | "output_type": "display_data", 1006 | "data": { 1007 | "text/plain": [ 1008 | "Generating train split: 0 examples [00:00, ? examples/s]" 1009 | ], 1010 | "application/vnd.jupyter.widget-view+json": { 1011 | "version_major": 2, 1012 | "version_minor": 0, 1013 | "model_id": "c657f93dbc124dbab5675fff5db70643" 1014 | } 1015 | }, 1016 | "metadata": {} 1017 | } 1018 | ] 1019 | }, 1020 | { 1021 | "cell_type": "code", 1022 | "source": [ 1023 | "distiset[\"default\"][\"train\"].to_pandas()" 1024 | ], 1025 | "metadata": { 1026 | "id": "AB2MfaW_RyzN", 1027 | "colab": { 1028 | "base_uri": "https://localhost:8080/", 1029 | "height": 143 1030 | }, 1031 | "outputId": "6a285cab-e022-4606-a8e6-98800bbd6443" 1032 | }, 1033 | "execution_count": 7, 1034 | "outputs": [ 1035 | { 1036 | "output_type": "execute_result", 1037 | "data": { 1038 | "text/plain": [ 1039 | " instruction generations feedback result model_name\n", 1040 | "0 None None None None None\n", 1041 | "1 None None None None None\n", 1042 | "2 None None None None None" 1043 | ], 1044 | "text/html": [ 1045 | "\n", 1046 | "
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Copy a token from your Hugging Face\ntokens page and paste it below.
Immediately click login after copying\nyour token or it might be stored in plain text in this notebook file.
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