sentence-transformers How to use ronit01/rag_tuned_minilm_mnr_100epoch with sentence-transformers:
from sentence_transformers import SentenceTransformer
model = SentenceTransformer("ronit01/rag_tuned_minilm_mnr_100epoch")
sentences = [
"How do the Stop and Delete IC Ops compare in terms of their effects on a run's state, visibility on the dashboard, resource usage, artifact preservation, and what further IC Ops can be performed on the run afterward?",
"RapidFire AI is a new AI experiment execution framework that transforms your LLM pipeline customization \nfrom slow, sequential processes into rapid, intelligent workflows with hyperparallelized execution, \ndynamic real-time experiment control, and automatic backend optimization.\n\nFor *RAG and context engineering evals*, start here: :doc:`Install and Get Started: RAG and Context Engineering</walkthroughrag>`.\n\nFor *SFT and RFT/post-training workflows*, start here: :doc:`Install and Get Started: SFT/RFT</walkthroughft>`.\n\n\nRapidFire AI is the first system of its kind to establish live three-way communication between the IDE\nwhere the experiment is launched, a metrics display/control dashboard, and a multi-core/multi-GPU execution backend.\n\n.. image:: /images/rf-usage.png\n :width: 800px\n\nJust pip install the :code:`rapidfireai` OSS package. It works on a CPU-only machine, a single-GPU machine, \nor a multi-GPU machine. Note that for RAG/context engineering with only closed model APIs, GPUs are not needed. ",
"Resume\n-----\n\nThis IC Op is applicable only to a previously stopped run. \nIt earmarks this run to be resumed from the next chunk onward, when it will be added to the mix of \nongoing runs and assigned GPU(s) automatically. \nYou cannot resume an already resumed or deleted run.",
"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."
]
embeddings = model.encode(sentences)
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
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