How to use ronit01/rag_tuned_minilm_mnr_1epoch with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("ronit01/rag_tuned_minilm_mnr_1epoch") sentences = [ "How do you select specific GPUs for RapidFire AI to use, and how do you resolve port conflicts when starting the server?", "Task, Dataset, and Prompt\n-------\n\nThis tutorial shows Supervised Fine-Tuning (SFT) for creating a customer support Q&A chatbot.\n\nIt uses the \"Bitext customer support\" dataset; \n`see its details on Hugging Face <https://huggingface.co/datasets/bitext/Bitext-customer-support-llm-chatbot-training-dataset>`__. \nWe use a sample of 5,000 training examples and 200 evaluation examples for tractable demo runtimes.\n\nThe prompt format includes a system message defining the assistant as \"helpful and friendly customer \nsupport\" with user instructions and assistant responses\n\n\nModel, Adapter, and Trainer Knobs\n-------\n\nWe compare 2 base model architectures: Llama-3.1-8B-Instruct and Mistral-7B-Instruct-v0.3. \nThe lite version uses only one: TinyLlama-1.1B-Chat-v1.0.\n\nThere are 2 different LoRA adapter configurations: a low-capacity adapter (rank 16; 8 for lite) targeting \nonly 2 modules and a high-capacity adapter (rank 128; 32 for lite) targeting 4 modules.\n\nAll other knobs are fixed across all configs. Thus, there are a total of 4 combinations, \nall launched with a simple grid search.", "Port conflicts (services already running)\n----------------------------------------\n\nIf you encounter port conflicts, you can kill existing processes.\n\n.. code-block:: bash\n\n lsof -t -i:8852 | xargs kill -9 # mlflow\n lsof -t -i:8851 | xargs kill -9 # dispatcher\n lsof -t -i:8853 | xargs kill -9 # frontend server\n\nSelect specific GPU(s) to use\n-----------------------------\n\nSet the ``CUDA_VISIBLE_DEVICES`` environment variable BEFORE running ``rapidfireai start`` to control which GPU(s) RapidFire can see and use.\n\n.. code-block:: bash\n\n export CUDA_VISIBLE_DEVICES=2 # use GPU index 2 only\n rapidfireai start\n\nMultiple GPUs (example: GPUs 0 and 2):\n\n.. code-block:: bash\n\n export CUDA_VISIBLE_DEVICES=0,2\n rapidfireai start\n\nFrom a Python script (set before importing/starting RapidFire):\n\n.. code-block:: python\n\n import os\n os.environ[\"CUDA_VISIBLE_DEVICES\"] = \"2\"\n # then start your RapidFire workflow\n", "RapidFire AI offers a browser-based dashboard to automatically visualize all ML metrics and lets \nyou control runs on the fly from there. \nOur current default dashboard is a fork of the popular OSS tool `MLflow <https://mlflow.org/>`__, \nand it inherits much of MLflow's native features.\nThe dashboard URI is printed when the rapidfireai server is started; open it in a browser. \n\nAs of this writing, apart from MLflow, RapidFire AI also supports \n`TensorBoard <https://www.tensorflow.org/tensorboard>`__\nand `Trackio <https://huggingface.co/docs/trackio/en/index>`__\nfor logging metrics plots. \nSpecify any one, two, or all three dashboards to use with the following server start argument. \n\n.. code-block:: bash\n\n rapidfireai start --tracking-backends [mlflow | tensorboard | trackio]\n\nAlternatively, set the dashboard using its environment variable as below in your python code/notebook:\n\n.. code-block:: python\n\n os.environ[\"RF_MLFLOW_ENABLED\"] = \"true\"\n os.environ[\"RF_TENSORBOARD_ENABLED\"] = \"true\"\n os.environ[\"RF_TRACKIO_ENABLED\"] = \"true\"\n\nSupport for other popular dashboards such as Weights & Biases and CometML is coming soon. \nThe rest of this section explains the new features of our MLflow-fork dashboard.\nNote that these new features are not yet available on the other dashboards." ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4]
The community tab is the place to discuss and collaborate with the HF community!
