sentence-transformers How to use ronit01/rag_tuned_minilm_100 with sentence-transformers:
from sentence_transformers import SentenceTransformer
model = SentenceTransformer("ronit01/rag_tuned_minilm_100")
sentences = [
"What three vector store backends does RapidFire AI support and what modes of operation do they offer?",
"RFDPOConfig\n------\n\nThis is a wrapper around :class:`DPOConfig` in HF TRL. \nThe full signature and list of arguments are available on `this page \n<https://huggingface.co/docs/trl/dpo_trainer#trl.DPOConfig>`__.\n\nAgain, the only difference here is that the individual arguments (knobs) can be :class:`List` \nvalued or :class:`Range` valued in :class:`RFDPOConfig`. \nThat is how you can specify a base set of knob combinations from which a config group can \nbe produced. Also read :doc:`the Multi-Config Specification page</configs>`.\nOther than the multi-config specification, this class preserves all semantics of \nHugging Face's DPO trainer under the hood. \n\n\n**Example:**\n\n.. code-block:: python\n\n\t# Based on the DPO tutorial notebook; one knob has list of values\n\tbase_dpo_config = RFDPOConfig(\n\t\tmodel_adapter_name=\"default\",\n\t\tref_adapter_name=\"reference\",\n\t\tforce_use_ref_model=False, \n\t\tloss_type=\"sigmoid\",\n\t\tbeta=List([0.1,0.001]), \n\t\tmax_prompt_length=1024,\n\t\tmax_completion_length=1024,\n\t\tmax_length=2048, \n\t\tper_device_train_batch_size=2,\n\t\tgradient_accumulation_steps=4,\n\t\tlearning_rate=5e-6, \n\t\twarmup_ratio=0.1,\n\t\tweight_decay=0,\n\t\tlr_scheduler_type=\"linear\",\n\t\toptim=\"adamw_8bit\",\n\t\tnum_train_epochs=1, \n\t\tlogging_strategy=\"steps\",\n\t\tlogging_steps=1,\n\t\tbf16=True,\n\t\tsave_strategy=\"epoch\",\n\t)\n\n\nJust like for SFT, you can specify an FSDP configuration for DPO too for larger LLMs that need cross-GPU partitioning (within a machine).\n\n**Example:**\n\n.. code-block:: python\n\n\t# From the DPO FSDP Lite notebook\n\tbase_dpo_config_lite = RFDPOConfig(\n\t\t...\n\t\tfsdp=\"full_shard auto_wrap\",\n\t\tfsdp_config={\n\t\t\t\"backward_prefetch\": \"backward_pre\",\n\t\t\t\"forward_prefetch\": True,\n\t\t\t\"use_orig_params\": False,\n\t\t\t\"cpu_ram_efficient_loading\": True,\n\t\t\t\"offload_params\": False,\n\t\t\t\"sync_module_states\": True,\n\t\t\t\"min_num_params\": 1000000,\n\t\t\t\"limit_all_gathers\": True,\n\t\t\t\"sharding_strategy\": \"FULL_SHARD\",\n\t\t\t\"auto_wrap_policy\": \"TRANSFORMER_BASED_WRAP\",\n\t\t\t\"activation_checkpointing\":False\n\t\t}\n\t)\n\n\nRFGRPOConfig\n------\n\nThis is a wrapper around :class:`GRPOConfig` in HF TRL. \nThe full signature and list of arguments are available on `this page \n<https://huggingface.co/docs/trl/grpo_trainer#trl.GRPOConfig>`__.\n\nAgain, the only difference here is that the individual arguments (knobs) can be :class:`List` \nvalued or :class:`Range` valued in :class:`RFGROConfig`. \nThat is how you can specify a base set of knob combinations from which a config group can \nbe produced. Also read :doc:`the Multi-Config Specification page</configs>`.\nOther than the multi-config specification, this class preserves all semantics of \nHugging Face's GRPO trainer under the hood. \n\n**Example:**\n\n.. code-block:: python\n\n\t# Based on the GRPO tutorial notebook\n\tRFGRPOConfig(\n\t\tlearning_rate=5e-6,\n\t\twarmup_ratio=0.1,\n\t\tweight_decay=0.1,\n\t\tmax_grad_norm=0.1,\n\t\tadam_beta1=0.9,\n\t\tadam_beta2=0.99,\n\t\tlr_scheduler_type = \"linear\",\n\t\tper_device_train_batch_size=4,\n\t\tgradient_accumulation_steps=4,\n\t\tnum_generations=8,\n\t\toptim =\"adamw_8bit\",\n\t\tnum_train_epochs=2,\n\t\tmax_prompt_length=1024,\n\t\tmax_completion_length=1024,\n\t\tlogging_steps=2,\n\t\teval_steps=5,\n\t)\n\n.. note::\n As of this writing, out-of-the-box support for FSDP for GRPO is still in the works. Watch this space for updates.",
".. note::\n When constructing a config group with :func:`List()` or :func:`Range()`, the interaction of :code:`k` in :code:`search_cfg` with :code:`top_n` in :code:`reranker_cfg` has a nuance. If the assigned :code:`k` is less than the assigned :code:`top_n`, the combination is meaningless and will be omitted automatically by RapidFire AI. All other combinations will create valid runs. For example, if :code:`k` is :func:`List([5, 10])` and :code:`top_n` is :func:`List([5, 6])`, then in the 2 x 2 grid obtained, the combination :code:`k` = 5 and :code:`top_n` = 6 will be automatically omitted but the other 3 will be valid.\n",
".. py:function:: get_runs_info(self) -> pd.DataFrame:\n\n\t:return: A DataFrame with the following columns: run_id, status, mlflow_run_id, completed_steps, total_steps, start_chunk_id, num_chunks_visited_curr_epoch, num_epochs_completed, error, source, ended_by, warm_started_from, config (full config dictionary)\n\n\t:rtype: pandas.DataFrame"
]
embeddings = model.encode(sentences)
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# [4, 4] 
