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
Maximum Sequence Length: 256 tokens
Output Dimensionality: 384 dimensions
Similarity Function: Cosine Similarity
Supported Modality: Text

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.

python

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]])

Training Details

Training Dataset

Unnamed Dataset

Size: 208 training samples
Columns: sentence_0, sentence_1, and label
Approximate statistics based on the first 208 samples:
sentence_0 sentence_1 label
type string string float
details
min: 11 tokens
mean: 24.87 tokens
max: 34 tokens
min: 31 tokens
mean: 218.51 tokens
max: 256 tokens
min: 0.0
mean: 0.25
max: 1.0

Samples:

sentence_0	sentence_1	label
`What arguments does the RFModelConfig class accept for defining a model configuration in RapidFire AI?`	`: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:[object Object] with one of the following three options listed as value; default is :code:[object Object].`

  * :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> |

| How do reward functions work in RapidFire AI for GRPO training, and what arguments does TRL inject into them? |

RapidFire AI supports both self-hosted open LLMs and closed model LLM APIs as the generator. [object Object]As of this writing, it wraps around the model config of vLLM for the former and the OpenAI API for the latter. [object Object]We plan to expand support for more generator plugins, including Gemini and Claude APIs, based on feedback. [object Object][object Object][object Object]RFvLLMModelConfig[object Object]------[object Object][object Object]This is a wrapper around vLLM's :class:[object Object] and :class:[object Object] classes. [object Object]The full list of their arguments are available on [object Object]__ [object Object]and [object Object]__, respectively.[object Object][object Object]The difference here is that the individual arguments (knobs) can be :class:[object Object] valued or [object Object]:class:[object Object] valued in an :class:[object Object]. [object Object]That is how you can specify a base set of knob combinations from which a config group can [object Object]be produced. Also read :doc:[object Object].

| 0.0 | |

What are the three use case tutorials provided for RAG and context engineering, and what type of workflow does each demonstrate?

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.

| 1.0 |

Loss: ContrastiveLoss with these parameters:

json
1{ 2 "distance_metric": "SiameseDistanceMetric.COSINE_DISTANCE", 3 "margin": 0.5, 4 "size_average": true 5}

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

Click to expand

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: {}

Training Logs

Epoch	Step	Training Loss
38.4615	500	0.0022
76.9231	1000	0.0002

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

bibtex

@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

bibtex

@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}
}

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