rag_tuned_minilm_100

SentenceTransformer based on sentence-transformers/all-MiniLM-L6-v2

This is a sentence-transformers model finetuned from sentence-transformers/all-MiniLM-L6-v2. It maps sentences & paragraphs to a 384-dimensional dense vector space and can be used for retrieval.

Model Details

Model Description

• Model Type: Sentence Transformer
• Base model: sentence-transformers/all-MiniLM-L6-v2 <!-- at revision c9745ed1d9f207416be6d2e6f8de32d1f16199bf -->
• Maximum Sequence Length: 256 tokens
• Output Dimensionality: 384 dimensions
• Similarity Function: Cosine Similarity
• Supported Modality: Text

<!-- - **Training Dataset:** Unknown --> <!-- - **Language:** Unknown --> <!-- - **License:** Unknown -->

Model Sources

• Documentation: Sentence Transformers Documentation
• Repository: Sentence Transformers on GitHub
• Hugging Face: Sentence Transformers on Hugging Face

Full Model Architecture

SentenceTransformer(
  (0): Transformer({'transformer_task': 'feature-extraction', 'modality_config': {'text': {'method': 'forward', 'method_output_name': 'last_hidden_state'}}, 'module_output_name': 'token_embeddings', 'architecture': 'BertModel'})
  (1): Pooling({'embedding_dimension': 384, 'pooling_mode': 'mean', 'include_prompt': True})
  (2): Normalize({})
)

Usage

Direct Usage (Sentence Transformers)

First install the Sentence Transformers library:

pip install -U sentence-transformers

Then you can load this model and run inference.

from sentence_transformers import SentenceTransformer

# Download from the 🤗 Hub
model = SentenceTransformer("ronit01/rag_tuned_minilm_100")
# Run inference
sentences = [
    'What three vector store backends does RapidFire AI support and what modes of operation do they offer?',
    'RapidFire AI also supports external persistent vector stores beyond the default in-memory FAISS.\nThis allows you to scale to larger corpora, persist indexes across runs and experiments, and leverage managed vector DBMS services.\nAs of this writing, **Pinecone** (hosted serverless or pod-based) and **PostgreSQL PGVector** (self-hosted or managed) are supported.\n\nEach external store supports three modes of operation:\n\n- **Create mode:** Build a new index from base documents from within RapidFire AI itself and use it for RAG.\n- **Read mode:** Retrieve from a pre-existing index and use it for RAG. \n- **Update mode:** Add new content to an existing index from additional base documents from within RapidFire AI itself and use it for RAG. \n\nSee the :doc:`API: LangChain RAG Spec page</ragspecs>` for more details on how to specify these external vector stores.',
    '.. 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',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]

# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities)
# tensor([[1.0000, 0.9882, 0.2547],
#         [0.9882, 1.0000, 0.2603],
#         [0.2547, 0.2603, 1.0000]])

<!-- ### Direct Usage (Transformers) <details><summary>Click to see the direct usage in Transformers</summary> </details> --> <!-- ### Downstream Usage (Sentence Transformers) You can finetune this model on your own dataset. <details><summary>Click to expand</summary> </details> --> <!-- ### Out-of-Scope Use *List how the model may foreseeably be misused and address what users ought not to do with the model.* --> <!-- ## Bias, Risks and Limitations *What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.* --> <!-- ### Recommendations *What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.* -->

Training Details

Training Dataset

Unnamed Dataset

• Size: 208 training samples

• Columns: <code>sentence_0</code>, <code>sentence_1</code>, and <code>label</code>

• Approximate statistics based on the first 208 samples:

  	sentence_0	sentence_1	label
  type	string	string	float
  details	<ul><li>min: 11 tokens</li><li>mean: 24.87 tokens</li><li>max: 34 tokens</li></ul>	<ul><li>min: 31 tokens</li><li>mean: 218.51 tokens</li><li>max: 256 tokens</li></ul>	<ul><li>min: 0.0</li><li>mean: 0.25</li><li>max: 1.0</li></ul>

• Samples:

  sentence_0	sentence_1	label
  <code>What arguments does the RFModelConfig class accept for defining a model configuration in RapidFire AI?</code>	<code> :param search_cfg: The search algorithm type and its kwargs to use for retrieval of vectors/chunks, provided as a single dictionary. Must include a key :code:"type" with one of the following three options listed as value; default is :code:"similarity".

    * :code:`"similarity"`: Standard cosine similarity search.
    * :code:`"similarity_score_threshold"`: Similarity search with minimum score threshold (SST).
    * :code:`"mmr"`: Maximum Marginal Relevance (MMR) search for diversity.
  
    Additional parameters for search configuration depend on the type; the keys can include the following:
  
    * :code:`"k"`: Number of documents to retrieve. Default is 5.
    * :code:`"filter"`: Optional filter criteria function for search results.
    * :code:`"score_threshold"`: Only for SST. Minimum similarity score threshold. 
    * :code:`"fetch_k"`: Only for MMR. Number of documents to fetch before MMR reranking. Default is 20.
    * :code:`"lambda_mult"`: Only for MMR...</code>     | <code>0.0</code> |
  

  | <code>How do reward functions work in RapidFire AI for GRPO training, and what arguments does TRL inject into them?</code> | <code>RapidFire AI supports both self-hosted open LLMs and closed model LLM APIs as the generator. <br>As of this writing, it wraps around the model config of vLLM for the former and the OpenAI API for the latter. <br>We plan to expand support for more generator plugins, including Gemini and Claude APIs, based on feedback. <br><br><br>RFvLLMModelConfig<br>------<br><br>This is a wrapper around vLLM's :class:config and :class:SamplingParams classes. <br>The full list of their arguments are available on this page <https://docs.vllm.ai/en/latest/api/vllm/config/index.html>__ <br>and this page <https://docs.vllm.ai/en/v0.6.4/dev/sampling_params.html>__, respectively.<br><br>The difference here is that the individual arguments (knobs) can be :class:List valued or <br>:class:Range valued in an :class:RFvLLMModelConfig. <br>That is how you can specify a base set of knob combinations from which a config group can <br>be produced. Also read :doc:the Multi-Config Specification page</configs>.</code> | <code>0.0</code> | | <code>What are the three use case tutorials provided for RAG and context engineering, and what type of workflow does each demonstrate?</code> | <code>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.</code> | <code>1.0</code> |

• Loss: <code>ContrastiveLoss</code> with these parameters:

  {
      "distance_metric": "SiameseDistanceMetric.COSINE_DISTANCE",
      "margin": 0.5,
      "size_average": true
  }
  

Training Hyperparameters

Non-Default Hyperparameters

• per_device_train_batch_size: 16
• per_device_eval_batch_size: 16
• num_train_epochs: 100
• multi_dataset_batch_sampler: round_robin

All Hyperparameters

<details><summary>Click to expand</summary>

• do_predict: False
• prediction_loss_only: True
• per_device_train_batch_size: 16
• per_device_eval_batch_size: 16
• gradient_accumulation_steps: 1
• eval_accumulation_steps: None
• torch_empty_cache_steps: None
• learning_rate: 5e-05
• weight_decay: 0.0
• adam_beta1: 0.9
• adam_beta2: 0.999
• adam_epsilon: 1e-08
• max_grad_norm: 1
• num_train_epochs: 100
• max_steps: -1
• lr_scheduler_type: linear
• lr_scheduler_kwargs: None
• warmup_ratio: None
• warmup_steps: 0
• log_level: passive
• log_level_replica: warning
• log_on_each_node: True
• logging_nan_inf_filter: True
• enable_jit_checkpoint: False
• save_on_each_node: False
• save_only_model: False
• restore_callback_states_from_checkpoint: False
• use_cpu: False
• seed: 42
• data_seed: None
• bf16: False
• fp16: False
• bf16_full_eval: False
• fp16_full_eval: False
• tf32: None
• local_rank: -1
• ddp_backend: None
• debug: []
• dataloader_drop_last: False
• dataloader_num_workers: 0
• dataloader_prefetch_factor: None
• disable_tqdm: False
• remove_unused_columns: True
• label_names: None
• load_best_model_at_end: False
• ignore_data_skip: False
• fsdp: []
• fsdp_config: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
• accelerator_config: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
• parallelism_config: None
• deepspeed: None
• label_smoothing_factor: 0.0
• optim: adamw_torch_fused
• optim_args: None
• group_by_length: False
• length_column_name: length
• project: huggingface
• trackio_space_id: trackio
• ddp_find_unused_parameters: None
• ddp_bucket_cap_mb: None
• ddp_broadcast_buffers: False
• dataloader_pin_memory: True
• dataloader_persistent_workers: False
• skip_memory_metrics: True
• push_to_hub: False
• resume_from_checkpoint: None
• hub_model_id: None
• hub_strategy: every_save
• hub_private_repo: None
• hub_always_push: False
• hub_revision: None
• gradient_checkpointing: False
• gradient_checkpointing_kwargs: None
• include_for_metrics: []
• eval_do_concat_batches: True
• auto_find_batch_size: False
• full_determinism: False
• ddp_timeout: 1800
• torch_compile: False
• torch_compile_backend: None
• torch_compile_mode: None
• include_num_input_tokens_seen: no
• neftune_noise_alpha: None
• optim_target_modules: None
• batch_eval_metrics: False
• eval_on_start: False
• use_liger_kernel: False
• liger_kernel_config: None
• eval_use_gather_object: False
• average_tokens_across_devices: True
• use_cache: False
• prompts: None
• batch_sampler: batch_sampler
• multi_dataset_batch_sampler: round_robin
• router_mapping: {}
• learning_rate_mapping: {}

</details>

Training Logs

Training Time

• Training: 3.6 minutes

Framework Versions

• Python: 3.12.13
• Sentence Transformers: 5.4.1
• Transformers: 5.0.0
• PyTorch: 2.10.0+cu128
• Accelerate: 1.13.0
• Datasets: 4.0.0
• Tokenizers: 0.22.2

Citation

BibTeX

Sentence Transformers

@inproceedings{reimers-2019-sentence-bert,
    title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
    author = "Reimers, Nils and Gurevych, Iryna",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
    month = "11",
    year = "2019",
    publisher = "Association for Computational Linguistics",
    url = "https://arxiv.org/abs/1908.10084",
}

ContrastiveLoss

@inproceedings{hadsell2006dimensionality,
    author={Hadsell, R. and Chopra, S. and LeCun, Y.},
    booktitle={2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06)},
    title={Dimensionality Reduction by Learning an Invariant Mapping},
    year={2006},
    volume={2},
    number={},
    pages={1735-1742},
    doi={10.1109/CVPR.2006.100}
}

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