litfit 0.1.5

A lightweight library for fast finetuning of embeddings

litfit

litfit /lɪt fɪt/ — the shortest path from someone else's embedding to your task.

Why litfit?

Fine-tuning dense embedding models means writing a training loop, picking a loss function, tuning a learning rate, and waiting minutes to hours — whether you're working with text, images, or multimodal embeddings. litfit takes a different approach: given pairs of items that should be similar (duplicates, relevant matches, same-class images), it computes covariance statistics and solves for the optimal linear projection in closed form. No gradient descent, no hyperparameters to babysit.

What you get:

• Fast — one pass over your pairs to collect statistics, then everything is solved in closed form. No iterative training = fast.
• Any dense embeddings — text, vision, multimodal. If it outputs a vector, litfit can probably improve it.
• Simple — pass in embeddings + pair labels, get a well-tuned projection matrix back.

Benchmarks

Task	Model		R@1	MAP@50	Dims	Time (CPU)
Fashion retrieval	SigLIP2-SO400M	baseline	0.833	0.532	1152	—
(DeepFashion In-Shop)		+ litfit	0.923	0.738	228	37s
Duplicate detection	e5-base-v2	baseline	0.556	0.522	768	—
(AskUbuntu)		+ litfit	0.598	0.590	294	~3min

Embeddings precomputed. DeepFashion In-Shop uses fast mode (~40 configs); AskUbuntu uses full sweep (~860 projections). Closed-form solution means you can safely merge val into training data without risk of overfitting — typically adds a fraction of a second.

Try it yourself — no setup needed

More benchmarks welcome — if you run litfit on your dataset, open an issue or PR with results!

Installation

pip install litfit

For an editable (development) install:

pip install -e ".[dev]"

For faster statistics computation on CUDA GPUs:

pip install triton

Usage

from litfit import (
    load_askubuntu, encode_texts, split_data,
    compute_stats, generate_all_projections, evaluate_projections,
)

all_ids, all_texts, id_to_group = load_askubuntu(max_groups=1000)
embs = encode_texts("intfloat/e5-base-v2", all_texts)
data = split_data(all_ids, all_texts, embs, id_to_group)
train_ids, _, train_embs, _ = data["train"]
val_ids, _, val_embs, _ = data["val"]
test_ids, _, test_embs, _ = data["test"]

st = compute_stats(train_embs, train_ids, id_to_group)
all_W = generate_all_projections(st, neg=None, include_neg_methods=False)
results, summary = evaluate_projections(
    all_W, val_embs, val_ids, id_to_group,
    test_embs=test_embs, test_ids=test_ids,
    dim_fractions=(0.1, 0.2, 0.5, 1.0),
)

Streaming + fast + dim search (low memory)

Combine streaming statistics, fast projections (~40 configs), lazy evaluation, and automatic dimension search for a memory-efficient pipeline:

from litfit import (
    compute_stats_streaming, generate_fast_projections,
    find_dim_range, evaluate_projections,
)

def pair_batches():
    for i in range(0, len(X_pairs_memmap), 1024):
        yield X_pairs_memmap[i:i+1024], Y_pairs_memmap[i:i+1024]

st = compute_stats_streaming(pair_batches())
dim_fractions = find_dim_range(st, val_embs, val_ids, id_to_group)
all_W = generate_fast_projections(st, lazy=True)
results, summary = evaluate_projections(
    all_W, test_embs, test_ids, id_to_group,
    dim_fractions=dim_fractions,
)

Full walkthrough: data concepts, splitting, extracting the best projection

litfit operates on three data structures:

• ids — a list of unique identifiers, one per embedding (strings, ints, anything hashable).
• id_to_group — a dict mapping each id to a group label. Items that share a group are treated as positives (duplicates / paraphrases / relevant matches). Everything else is a negative.
• embs — a numpy array or torch tensor of shape (n, d), one row per id.

For example, if questions 0, 1, 2 are duplicates and 3, 4 are duplicates:

ids = [0, 1, 2, 3, 4]
id_to_group = {0: 'A', 1: 'A', 2: 'A', 3: 'B', 4: 'B'}

Here is a complete pipeline — from loading data to exporting a torch.nn.Linear:

import torch
import torch.nn as nn
from litfit import (
    load_askubuntu, encode_texts, split_data,
    compute_stats, generate_fast_projections,
    find_dim_range, evaluate_projections,
)

# --- 1. Load & encode ---
# load_askubuntu returns (ids, texts, id_to_group).
# max_groups limits how many duplicate-groups to keep (for speed).
all_ids, all_texts, id_to_group = load_askubuntu(max_groups=1500)
embs = encode_texts("intfloat/e5-base-v2", all_texts)

# --- 2. Split into train / val / test ---
# split_data does a group-aware split: all items in a group stay together,
# so no group leaks across splits. Default: 60/20/20.
data = split_data(all_ids, all_texts, embs, id_to_group)
train_ids, _, train_embs, _ = data["train"]
val_ids,   _, val_embs,   _ = data["val"]
test_ids,  _, test_embs,  _ = data["test"]

# --- 3. Compute sufficient statistics from training pairs ---
# compute_stats builds covariance matrices (Sigma_XX, Sigma_XY, etc.)
# from all positive pairs implied by id_to_group.
st = compute_stats(train_embs, train_ids, id_to_group)

# --- 4. Find useful dimension range ---
# Scans Rayleigh projections at many dims to find where performance peaks.
# Returns dim_fractions focused on the useful range.
dim_fractions = find_dim_range(st, val_embs, val_ids, id_to_group)

# --- 5. Generate & evaluate projections ---
# generate_fast_projections returns ~40 (method, hyperparams) configs.
# evaluate_projections uses explore-exploit scheduling on the val set.
all_W = generate_fast_projections(st)
results, summary = evaluate_projections(
    all_W, val_embs, val_ids, id_to_group,
    test_embs=test_embs, test_ids=test_ids,
    dim_fractions=dim_fractions,
)

# --- 6. Extract the best projection ---
# results keys are tuples like ('m_rayleigh', 'reg=0.1').
# Each value is {n_dims: {'MAP@50': ..., 'R@1': ..., ...}}.
# n_dims=None means full-dimensional.
best_key = max(results, key=lambda k: results[k][None]['MAP@50'])
W = all_W[best_key]                # shape (d, d) or (d, k)

# Optionally truncate to the best reduced dimension:
best_dim = 128
projected = test_embs @ W[:, :best_dim]  # shape (n, best_dim)

# --- 7. (Optional) Recompute stats on ALL data for best performance ---
# The train split was used for fitting and val/test for model selection.
# Once you've picked the best config, recompute stats on all available
# embeddings so the final projection sees the most signal.
full_st = compute_stats(embs, all_ids, id_to_group)
all_W_full = generate_fast_projections(full_st, verbose=False)
W = all_W_full[best_key]

# --- 8. Export as torch.nn.Linear for inference ---
out_dim = best_dim             # or W.shape[1] for full
layer = nn.Linear(W.shape[0], out_dim, bias=False)
layer.weight = nn.Parameter(W[:, :out_dim].T.cpu().float())
# Use it: projected = layer(input_embs)

See the docs for more examples, architecture diagrams, and streaming scripts.

Device Support

• CUDA: Full support with optional Triton acceleration
• CPU: Full support
• MPS: Not supported (missing linalg ops)

How it works

1. You provide embeddings and group labels (which items are duplicates/relevant/same-class)
2. litfit computes covariance matrices from all positive pairs (the "sufficient statistics")
3. It generates ~40 candidate projections in fast mode, or 800+ in full sweep, using different methods (generalized Rayleigh quotients, CCA-style decompositions, asymmetric refinements, MSE regularization)
4. You get a projection matrix W — multiply your embeddings by it and you're done

The result is a linear transformation that can also reduce dimensionality: a 1152-dim SigLIP embedding projected to 228 dims can score better than the original on your task.

Development

pip install -e ".[dev]"
pytest
mypy litfit
black litfit tests