sentence-transformers How to use ronit01/rag_tuned_minilm_mnr_50epoch with sentence-transformers:
from sentence_transformers import SentenceTransformer
model = SentenceTransformer("ronit01/rag_tuned_minilm_mnr_50epoch")
sentences = [
"What parameters does the Experiment constructor accept?",
"The crux of RapidFire AI's difference is in its *adaptive execution engine*: it enables \"interruptible\"\nexecution of configurations across GPUs/CPUs. To do so, it first shards the training and/or evaluation \ndataset randomly into \"chunks\" (also called \"shards\").\nThen instead of waiting for a run to see the whole dataset for all epochs (for SFT/RFT) or for full \neval metrics calculation (for RAG evals), RapidFire AI schedules all runs on *one shard at a time*, \nand then cycles through all shards.\n\nSuppose you have only 1 GPU, say an A100 or H100, and you want to run SFT on a Llama model. \nCurrent tools force you to run one config after another *sequentially* as shown in the (simplified) illustration below. \nIn contrast, by operating on shards, RapidFire AI offers a far more concurrent learning experience by \nautomatically *swapping* adapters (and base models, if needed) across GPU(s) and DRAM. \nIt does this via efficient shared memory-based caching mechanisms that can spill to disk when needed.\n\n.. image:: /images/gantt-1gpu.png\n :width: 800px\n\nIn the above figure, all 3 model configs are shown for 1 epoch. RapidFire AI is set to use 4 chunks.\nSo, before model config 3 (M3) even starts in the sequential approach, RapidFire AI already shows you \nthe learning behaviors of all 3 configs on the first 2-3 chunks. \nThe overhead of swapping, represented by the thin gray box, is minimal, less than 5% of the runtime,\nas per our measurements--thanks to our new efficient memory management techniques.\n\nFor inference evals for RAG/context engineering, such sharded execution means RapidFire AI surfaces eval metrics \nsooner based on a statistical technique known as *online aggregation* from the database systems literature.\nBasically, see estimated values and confidence intervals for all eval metrics in real time as the shards \nget processed, ultimately converging to the exact metrics on the full dataset.",
".. py:function:: __init__(self, experiment_name: str, mode: str = \"fit\", experiments_path: str = \"./rapidfire_experiments\") -> None\n\n\t:param experiment_name: Unique name for this experiment\n\t:type experiment_name: str\n\t\n\t:param mode: Mode of this experiment, either :code:`\"fit\"` or :code:`\"eval\"`; default is :code:`\"fit\"`\n\t:type mode: str\n\t\n\t:param experiments_path: Path to a folder to store this experiment's artifacts. Default is ``\"./rapidfire_experiments\"``)\n\t:type experiments_path: str, optional \n\n\t:return: None\n\t:rtype: None",
"just start rapidfireai again with the above command.\n\nIf the start command fails for whatever reason, wait for half a minute and rerun it.\nFor diagnostics and common fixes (including Linux/macOS and Windows steps), see :doc:`Troubleshooting </troubleshooting>`.\n"
]
embeddings = model.encode(sentences)
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# [4, 4] 
