feat: Add vocabulary quantization support

src/model.rs

@@ -11,6 +11,8 @@ use tokenizers::Tokenizer;
 pub struct StaticModel {
     tokenizer: Tokenizer,
     embeddings: Array2<f32>,
+    weights: Option<Vec<f32>>,
+    token_mapping: Option<Vec<usize>>,
     normalize: bool,
     median_token_length: usize,
     unk_token_id: Option<usize>,
@@ -114,9 +116,48 @@ impl StaticModel {
         };
         let embeddings = Array2::from_shape_vec((rows, cols), floats).context("failed to build embeddings array")?;
 
+        // Load optional weights for vocabulary quantization
+        let weights = match safet.tensor("weights") {
+            Ok(t) => {
+                let raw = t.data();
+                let v: Vec<f32> = match t.dtype() {
+                    Dtype::F64 => raw
+                        .chunks_exact(8)
+                        .map(|b| f64::from_le_bytes(b.try_into().unwrap()) as f32)
+                        .collect(),
+                    Dtype::F32 => raw
+                        .chunks_exact(4)
+                        .map(|b| f32::from_le_bytes(b.try_into().unwrap()))
+                        .collect(),
+                    Dtype::F16 => raw
+                        .chunks_exact(2)
+                        .map(|b| half::f16::from_le_bytes(b.try_into().unwrap()).to_f32())
+                        .collect(),
+                    other => return Err(anyhow!("unsupported weights dtype: {:?}", other)),
+                };
+                Some(v)
+            }
+            Err(_) => None,
+        };
+
+        // Load optional token mapping for vocabulary quantization
+        let token_mapping = match safet.tensor("mapping") {
+            Ok(t) => {
+                let raw = t.data();
+                let v: Vec<usize> = raw
+                    .chunks_exact(4)
+                    .map(|b| i32::from_le_bytes(b.try_into().unwrap()) as usize)
+                    .collect();
+                Some(v)
+            }
+            Err(_) => None,
+        };
+
         Ok(Self {
             tokenizer,
             embeddings,
+            weights,
+            token_mapping,
             normalize,
             median_token_length,
             unk_token_id: Some(unk_token_id),
@@ -201,18 +242,46 @@ impl StaticModel {
 
     /// Mean-pool a single token-ID list into a vector
     fn pool_ids(&self, ids: Vec<u32>) -> Vec<f32> {
-        let mut sum = vec![0.0; self.embeddings.ncols()];
+        let dim = self.embeddings.ncols();
+        let mut sum = vec![0.0; dim];
+        let mut cnt = 0usize;
+
         for &id in &ids {
-            let row = self.embeddings.row(id as usize);
+            let tok = id as usize;
+
+            // Remap: row = token_mapping[id] or id
+            let row_idx = if let Some(m) = &self.token_mapping {
+                *m.get(tok).unwrap_or(&tok)
+            } else {
+                tok
+            };
+
+            // Scale by per-token weight if present
+            let scale = if let Some(w) = &self.weights {
+                *w.get(tok).unwrap_or(&1.0)
+            } else {
+                1.0
+            };
+
+            let row = self.embeddings.row(row_idx);
             for (i, &v) in row.iter().enumerate() {
-                sum[i] += v;
+                sum[i] += v * scale;
             }
+            cnt += 1;
         }
-        let cnt = ids.len().max(1) as f32;
-        sum.iter_mut().for_each(|x| *x /= cnt);
+
+        // Mean pool the embeddings
+        let denom = (cnt.max(1)) as f32;
+        for x in &mut sum {
+            *x /= denom;
+        }
+
+        // Normalize the embeddings if required
         if self.normalize {
             let norm = sum.iter().map(|&v| v * v).sum::<f32>().sqrt().max(1e-12);
-            sum.iter_mut().for_each(|x| *x /= norm);
+            for x in &mut sum {
+                *x /= norm;
+            }
         }
         sum
     }

tests/common.rs

@@ -1,3 +1,4 @@
+#![allow(dead_code)]
 use model2vec_rs::model::StaticModel;
 
 /// Load the small float32 test model from fixtures
@@ -10,3 +11,24 @@ pub fn load_test_model() -> StaticModel {
     )
     .expect("Failed to load test model")
 }
+
+/// Load the vocab quantized test model from fixtures
+pub fn load_test_model_vocab_quantized() -> StaticModel {
+    StaticModel::from_pretrained(
+        "tests/fixtures/test-model-vocab-quantized",
+        None, // token
+        None, // normalize
+        None, // subfolder
+    )
+    .expect("Failed to load test model")
+}
+
+pub fn encode_with_model(path: &str) -> Vec<f32> {
+    // Helper function to load the model and encode "hello world"
+    let model = StaticModel::from_pretrained(path, None, None, None)
+        .unwrap_or_else(|e| panic!("Failed to load model at {path}: {e}"));
+
+    let out = model.encode(&["hello world".to_string()]);
+    assert_eq!(out.len(), 1);
+    out.into_iter().next().unwrap()
+}

tests/fixtures/embeddings_vocab_quantized.json

@@ -0,0 +1 @@
+[[-0.363402567303566, -0.17280622671667234, 0.39940570500107386, -0.16813011070789555, -0.15618451933846883, -0.14994650725286524, 0.006071081245258223, -0.16688048919255372, 0.22185219763399205, -0.02887293996697886, -0.17287425174176316, -0.01464316366136059, 0.16364637740934518, 0.15876414737891392, 0.06036445094799073, 0.15604592511625437, -0.0671308839546444, -0.016413190151500695, 0.016156947144592284, -0.04046410877612431, 0.08342219180115457, -0.06072982382315607, -0.15155935894530448, -0.27756653365043565, 0.04183386122272067, -0.02478048814648766, 0.048693007647196467, 0.15564136567656622, 0.03729875053535759, -0.06892603188806953, 0.08513432033392887, 0.0036654278831274112, -0.017677908666363845, 0.062035159999304555, -0.1394435606629564, 0.05264278960819571, -0.10000422994390393, 0.162456462739632, 0.0026303158188036926, -0.010224468015697916, -0.12629957405039433, -0.08506841545219175, -0.06720500777509077, -0.04443293593977252, 0.01816271214883152, 0.11269895366859049, 0.15572718186207016, -0.12838458617894438, 0.020126459971623472, -0.16689367919078762, 0.1038076137507656, 0.005876202291780198, 0.11467950137199819, -0.06360069640738063, 0.12898717602987858, 0.06665970239323335, 0.1263998072107107, 0.054322006298590964, 0.02275680905863399, -0.09242075684142392, 0.0003214892909238989, 0.06269664923701938, 0.007532826486481935, 0.006629162182434642]]

tests/fixtures/test-model-vocab-quantized/README.md

@@ -0,0 +1,100 @@
+---
+base_model: BAAI/bge-base-en-v1.5
+language:
+- en
+library_name: model2vec
+license: mit
+model_name: test-model-vocab-quantized
+tags:
+- embeddings
+- static-embeddings
+- sentence-transformers
+---
+
+# test-model-vocab-quantized Model Card
+
+This [Model2Vec](https://github.com/MinishLab/model2vec) model is a distilled version of the BAAI/bge-base-en-v1.5(https://huggingface.co/BAAI/bge-base-en-v1.5) Sentence Transformer. It uses static embeddings, allowing text embeddings to be computed orders of magnitude faster on both GPU and CPU. It is designed for applications where computational resources are limited or where real-time performance is critical. Model2Vec models are the smallest, fastest, and most performant static embedders available. The distilled models are up to 50 times smaller and 500 times faster than traditional Sentence Transformers.
+
+
+## Installation
+
+Install model2vec using pip:
+```
+pip install model2vec
+```
+
+## Usage
+
+### Using Model2Vec
+
+The [Model2Vec library](https://github.com/MinishLab/model2vec) is the fastest and most lightweight way to run Model2Vec models.
+
+Load this model using the `from_pretrained` method:
+```python
+from model2vec import StaticModel
+
+# Load a pretrained Model2Vec model
+model = StaticModel.from_pretrained("test-model-vocab-quantized")
+
+# Compute text embeddings
+embeddings = model.encode(["Example sentence"])
+```
+
+### Using Sentence Transformers
+
+You can also use the [Sentence Transformers library](https://github.com/UKPLab/sentence-transformers) to load and use the model:
+
+```python
+from sentence_transformers import SentenceTransformer
+
+# Load a pretrained Sentence Transformer model
+model = SentenceTransformer("test-model-vocab-quantized")
+
+# Compute text embeddings
+embeddings = model.encode(["Example sentence"])
+```
+
+### Distilling a Model2Vec model
+
+You can distill a Model2Vec model from a Sentence Transformer model using the `distill` method. First, install the `distill` extra with `pip install model2vec[distill]`. Then, run the following code:
+
+```python
+from model2vec.distill import distill
+
+# Distill a Sentence Transformer model, in this case the BAAI/bge-base-en-v1.5 model
+m2v_model = distill(model_name="BAAI/bge-base-en-v1.5", pca_dims=256)
+
+# Save the model
+m2v_model.save_pretrained("m2v_model")
+```
+
+## How it works
+
+Model2vec creates a small, fast, and powerful model that outperforms other static embedding models by a large margin on all tasks we could find, while being much faster to create than traditional static embedding models such as GloVe. Best of all, you don't need any data to distill a model using Model2Vec.
+
+It works by passing a vocabulary through a sentence transformer model, then reducing the dimensionality of the resulting embeddings using PCA, and finally weighting the embeddings using [SIF weighting](https://openreview.net/pdf?id=SyK00v5xx). During inference, we simply take the mean of all token embeddings occurring in a sentence.
+
+## Additional Resources
+
+- [Model2Vec Repo](https://github.com/MinishLab/model2vec)
+- [Model2Vec Base Models](https://huggingface.co/collections/minishlab/model2vec-base-models-66fd9dd9b7c3b3c0f25ca90e)
+- [Model2Vec Results](https://github.com/MinishLab/model2vec/tree/main/results)
+- [Model2Vec Tutorials](https://github.com/MinishLab/model2vec/tree/main/tutorials)
+- [Website](https://minishlab.github.io/)
+
+
+## Library Authors
+
+Model2Vec was developed by the [Minish Lab](https://github.com/MinishLab) team consisting of [Stephan Tulkens](https://github.com/stephantul) and [Thomas van Dongen](https://github.com/Pringled).
+
+## Citation
+
+Please cite the [Model2Vec repository](https://github.com/MinishLab/model2vec) if you use this model in your work.
+```
+@article{minishlab2024model2vec,
+  author = {Tulkens, Stephan and {van Dongen}, Thomas},
+  title = {Model2Vec: Fast State-of-the-Art Static Embeddings},
+  year = {2024},
+  url = {https://github.com/MinishLab/model2vec}
+}
+```

tests/fixtures/test-model-vocab-quantized/config.json

@@ -0,0 +1,13 @@
+{
+    "model_type": "model2vec",
+    "architectures": [
+        "StaticModel"
+    ],
+    "tokenizer_name": "BAAI/bge-base-en-v1.5",
+    "apply_pca": 64,
+    "apply_zipf": null,
+    "sif_coefficient": 0.0001,
+    "hidden_dim": 64,
+    "seq_length": 1000000,
+    "normalize": true
+}

tests/fixtures/test-model-vocab-quantized/modules.json

@@ -0,0 +1,14 @@
+[
+    {
+        "idx": 0,
+        "name": "0",
+        "path": ".",
+        "type": "sentence_transformers.models.StaticEmbedding"
+    },
+    {
+        "idx": 1,
+        "name": "1",
+        "path": "1_Normalize",
+        "type": "sentence_transformers.models.Normalize"
+    }
+]

tests/test_model.rs

@@ -1,51 +1,6 @@
 mod common;
-use approx::assert_relative_eq;
 use common::load_test_model;
 use model2vec_rs::model::StaticModel;
-use std::fs;
-
-#[test]
-fn test_encode_matches_python_model2vec() {
-    // Load the test model
-    let model = load_test_model();
-
-    // Define the short and long text inputs
-    let long_text = vec!["hello"; 1000].join(" ");
-    let short_text = "hello world".to_string();
-    let cases = vec![
-        ("tests/fixtures/embeddings_short.json", vec![short_text]),
-        ("tests/fixtures/embeddings_long.json", vec![long_text]),
-    ];
-
-    for (fixture_path, inputs) in cases {
-        // Read and parse the Python‐generated embedding fixture
-        let fixture =
-            fs::read_to_string(fixture_path).unwrap_or_else(|_| panic!("Fixture not found: {}", fixture_path));
-        let expected: Vec<Vec<f32>> = serde_json::from_str(&fixture).expect("Failed to parse fixture");
-
-        // Encode with the Rust model
-        let output = model.encode(&inputs);
-
-        // Sanity checks
-        assert_eq!(
-            output.len(),
-            expected.len(),
-            "number of sentences mismatch for {}",
-            fixture_path
-        );
-        assert_eq!(
-            output[0].len(),
-            expected[0].len(),
-            "vector dimensionality mismatch for {}",
-            fixture_path
-        );
-
-        // Element‐wise comparison
-        for (o, e) in output[0].iter().zip(&expected[0]) {
-            assert_relative_eq!(o, e, max_relative = 1e-5);
-        }
-    }
-}
 
 /// Test that encoding an empty input slice yields an empty output
 #[test]

tests/test_load.rs → tests/test_quantization.rs

@@ -1,15 +1,6 @@
+mod common;
 use approx::assert_relative_eq;
-use model2vec_rs::model::StaticModel;
-
-fn encode_with_model(path: &str) -> Vec<f32> {
-    // Helper function to load the model and encode "hello world"
-    let model = StaticModel::from_pretrained(path, None, None, None)
-        .unwrap_or_else(|e| panic!("Failed to load model at {path}: {e}"));
-
-    let out = model.encode(&["hello world".to_string()]);
-    assert_eq!(out.len(), 1);
-    out.into_iter().next().unwrap()
-}
+use common::encode_with_model;
 
 #[test]
 fn quantized_models_match_float32() {