How to use ronit01/rag_tuned_minilm_mnr_10 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("ronit01/rag_tuned_minilm_mnr_10") sentences = [ "What are the three use case tutorials provided for RAG and context engineering, and what type of workflow does each demonstrate?", " :param rpm_limit: Rate limit for requests per minute to the OpenAI API. Used for throttling to avoid exceeding Open AI API quotas. Check the rate limit published by Open AI for details on your tier and the latest per-model limits on `this page <https://platform.openai.com/docs/guides/rate-limits>`__.\n :type rpm_limit: int\n\n :param tpm_limit: Rate limit for tokens per minute to the OpenAI API. Used for throttling to avoid exceeding API quotas. See the rate limit page above for details.\n :type tpm_limit: int", "This use case notebook features an all-closed model API workflow, with Open AI calls used for both embedding for generation. So, you do not need a GPU to run this notebook.", "Follow these steps to install RapidFire AI on your local machine or remote/cloud instance for complete functionality without limitations.\n\n\nStep 1: Install dependencies and package\n-----------------------\n\nObtain the RapidFire AI OSS package from pypi (includes all dependencies) and ensure it is installed correctly.\n\n.. important::\n\n Requires Python 3.12+. Ensure that ``python3`` resolves to Python 3.12 before creating the venv.\n\n.. code-block:: bash\n\n python3 --version # must be 3.12.x\n python3 -m venv .venv\n source .venv/bin/activate\n\n pip install rapidfireai\n\n rapidfireai --version\n # Verify it prints the following:\n # RapidFire AI 0.14.0\n\nProvide your Hugging Face account token to access the gated Llama and Mistral models \nshowcased in the tutorial notebooks. \nIf you do not have such a token, you have two options:\n\n* Switch the :code:`model_name` in the tutorial notebook to a non-gated model from Hugging Face. Then proceed to Step 2.\n\n* Create a Hugging Face token `as explained here <https://huggingface.co/docs/hub/en/security-tokens>`_. Then request access on the following gated models' Hugging Face pages:\n\n * `mistralai/Mistral-7B-Instruct-v0.3 <https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.3>`_\n * `meta-llama/Llama-3.1-8B-Instruct <https://huggingface.co/meta-llama/Llama-3.1-8B-Instruct>`_\n * `meta-llama/Llama-3.2-1B-Instruct <https://huggingface.co/meta-llama/Llama-3.2-1B-Instruct>`_\n \n Headsup: the approval for the Llama models may take a few hours. Then provide your HF token in the same venv.\n\n.. code-block:: bash\n\n source .venv/bin/activate\n pip install \"huggingface-hub[cli]\"\n\n # Replace YOUR_TOKEN with your actual HF token\n # https://huggingface.co/docs/hub/en/security-tokens\n hf auth login --token YOUR_TOKEN\n\n # Due to current issue: https://github.com/huggingface/xet-core/issues/527\n pip uninstall -y hf-xet\n\n\nFeel free to ask us on Discord if you need any help with accessing gated Hugging Face models. Unfortunately, we are not allowed to provide a publicly visible token here for your use due to Hugging Face's policies." ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4]
