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README.md

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+---
+tags:
+- ColBERT
+- PyLate
+- sentence-transformers
+- sentence-similarity
+- feature-extraction
+- multilingual
+- late-interaction
+- retrieval
+- bright
+- loss:Distillation
+pipeline_tag: sentence-similarity
+library_name: PyLate
+license: apache-2.0
+base_model:
+- DavidGF/SauerkrautLM-Multi-ColBERT-15m
+---
+<img src="https://vago-solutions.ai/wp-content/uploads/2025/08/SauerkrautLM-Reason-Multi-ColBERT-15m.png" width="500" height="auto">
+
+## SauerkrautLM-Reason-Multi-ColBERT
+
+This model is an ultra-compact Late Interaction retriever that leverages:
+
+**Knowledge Distillation** from strong synthetic data (200k samples generated with Qwen/Qwen3-32B-AWQ and scored by a high-performing reranker).
+**Extreme compression** with just 15M parameters – optimized for edge deployment and resource-constrained environments.
+
+### 🎯 Core Features and Innovations:
+
+- **Next-Generation Knowledge Distillation**: By utilizing 200,000 synthetically generated, high-quality training examples (created with `Qwen/Qwen3-32B-AWQ` and scored by a state-of-the-art reranker).
+
+- **Ultra-Efficient Architecture**: With extreme parameter compression to just 15M, enabling deployment on edge devices and mobile platforms
+
+### 💪 The Ant Among Giants: Tiny but Effective
+
+With **15 million parameters** – that's **less than 1/500th the size** of some competing models – SauerkrautLM-Reason-Multi-ColBERT-15m represents the extreme frontier of model compression:
+- **500× smaller** than 7B+ parameter models
+- **10× smaller** than typical BERT models
+- Comparable to **SBERT-scale** encoders in size
+
+What makes SauerkrautLM-Multi-ColBERT-15m truly remarkable is not just its tiny size, but its **exceptional performance in scientific and technical domains**:
+
+#### Domain-Specific Strengths:
+- **Scientific Retrieval Champion**: In Biology and Earth Sciences, it achieves scores (23.33, 23.78) that **surpass E5 (1B parameters)** and proprietary APIs
+- **Technical Q&A Powerhouse**: Mean StackExchange score of 14.64 puts it in the same league as models 10-100× larger
+- **🐴 PONY TASK SUPERSTAR**: Ranks **#2 globally** with 15.86, only behind GritLM-7B (22.00), crushing ReasonIR-8B (10.50) and Qwen-7B (9.90) - a 15M model beating 8 BILLION parameter models!
+- **STEM-Optimized**: The knowledge distillation process has successfully preserved critical scientific reasoning capabilities
+
+#### Bright Performance Comparisons:
+| Domain | SauerkrautLM-15M | BGE | E5 (1B) | ReasonIR-8B | Qwen-7B | Performance |
+|--------|------------------|-----|---------|-------------|---------|-------------|
+| Biology | **23.33** | 11.70 | 18.60 | 26.20 | 30.60 | **2× BGE, 125% of E5** |
+| Earth Science | **23.78** | 24.60 | 26.00 | 31.40 | 36.40 | **97% of BGE, 91% of E5** |
+| Mean StackEx | **14.64** | 15.17 | 17.36 | 24.76 | 22.80 | **96% of BGE, 84% of E5** |
+| **🐴 Pony** | **15.86** | 5.70 | 4.90 | 10.50 | 9.90 | **#2 Overall! 278% of BGE, 151% of 8B model, 160% of 7B!** |
+
+
+
+## Model Overview
+
+**Model:** `VAGOsolutions/SauerkrautLM-Reason-Multi-ColBERT-15m`\
+**Base:** Fine-tuned from `VAGOsolutions/SauerkrautLM-Multi-ColBERT-15m` using knowledge distillation\
+**Architecture:** PyLate / ColBERT (Late Interaction)\
+**Languages:** Multilingual (optimized for 7 European languages: German, English, Spanish, French, Italian, Dutch, Portuguese)\
+**License:** Apache 2.0\
+**Model Size:** 15M parameters 
+**Efficiency Ratio:** Up to **500× smaller** than comparable performing models
+
+### Model Description
+- **Model Type:** PyLate model with innovative Late Interaction architecture
+- **Document Length:** 2048 tokens (8× longer than traditional BERT models)
+- **Query Length:** 256 tokens (optimized for complex, multi-part queries)
+- **Output Dimensionality:** 128 tokens (efficient vector representation)
+- **Similarity Function:** MaxSim (enables precise token-level matching)
+- **Training Loss:** Knowledge Distillation (PyLate) - learns from models 2000× larger
+
+### Architecture
+
+```
+ColBERT(
+  (0): Transformer(CompressedModernBertModel)
+  (1): Dense(384 -> 128 dim, no bias)
+)
+```
+
+## 🔬 Technical Innovations in Detail
+
+### Knowledge Distillation: David Learning from Goliath
+
+Our 15M parameter model leverages state-of-the-art knowledge distillation:
+
+1. **Synthetic Data Generation**: 200,000 high-quality query-document pairs generated using the `Qwen/Qwen3-32B-AWQ` model (32 billion parameters) based on the [ReasonIR approach](https://huggingface.co/datasets/reasonir/reasonir-data)
+2. **Quality Assurance**: Each pair evaluated and filtered by a state-of-the-art reranker
+
+
+### Ultra-Compact Design
+
+SauerkrautLM-Reason-Multi-ColBERT-15m achieves extreme efficiency through:
+
+- Ultra-Compact Architecture (~15 M params)
+- Deep-yet-slim BERT — 10 layers, hidden_size = 288.
+- Many heads — 12 attention heads (24-dim each) for fine-grained reasoning in a narrow model.
+- Edge-ready — small footprint
+
+This architecture enables Late Interaction Retrieval on mobile devices (or on even calculators :D).
+
+---
+
+## 🔬 Benchmarks: The Ant's Performance
+
+Despite its microscopic size, SauerkrautLM-Reason-Multi-ColBERT-15m delivers functional retrieval performance, particularly impressive given its **500× size disadvantage** compared to state-of-the-art models.
+
+### BRIGHT Benchmark (English, reasoning‑focused retrieval)
+
+The [BRIGHT benchmark](https://huggingface.co/datasets/xlangai/BRIGHT) is designed to evaluate **reasoning‑intensive retrieval**. All scores are nDCG\@10. SauerkrautLM-Reason-Multi-ColBERT (15M parameters) demonstrates that extreme compression is possible while maintaining basic retrieval functionality.
+
+| Model / Metric                           | Biology   | Earth     | Economics | Psychology | Robotics | Stackoverflow | Sustainable | Leetcode  | Pony      | AoPS      | Theorem‑Q | Theorem‑T | Mean StackEx | Mean coding | Mean theorem | Full Mean |
+| ---------------------------------------- | --------- | --------- | --------- | ---------- | -------- | ------------- | ----------- | --------- | --------- | --------- | --------- | --------- | ------------ | ----------- | ------------ | --------- |
+| **BM25**                                 | 18.90     | 27.20     | 14.90     | 12.50      | 13.60    | 18.40         | 15.00       | 24.40     | 7.90      | 6.20      | 10.40     | 4.90      | 17.21        | 16.15       | 7.17         | 14.53     |
+| **< 1 B OS**                             |           |           |           |            |          |               |             |           |           |           |           |           |              |             |              |           |
+| BGE                                      | 11.70     | 24.60     | 16.60     | 17.50      | 11.70    | 10.80         | 13.30       | 26.70     | 5.70      | 6.00      | 13.00     | 6.90      | 15.17        | 16.20       | 8.63         | 13.71     |
+| Inst‑L                                   | 15.20     | 21.20     | 14.70     | 22.30      | 11.40    | 13.30         | 13.50       | 19.50     | 1.30      | 8.10      | 20.90     | 9.10      | 15.94        | 10.40       | 12.70        | 14.21     |
+| SBERT                                    | 15.10     | 20.40     | 16.60     | 22.70      | 8.20     | 11.00         | 15.30       | 26.40     | 7.00      | 5.30      | 20.00     | 10.80     | 15.61        | 16.70       | 12.03        | 14.90     |
+| **> 1 B OS**                             |           |           |           |            |          |               |             |           |           |           |           |           |              |             |              |           |
+| E5                                       | 18.60     | 26.00     | 15.50     | 15.80      | 16.30    | 11.20         | 18.10       | 28.70     | 4.90      | 7.10      | 26.10     | 26.80     | 17.36        | 16.80       | 20.00        | 17.93     |
+| SFR                                      | 19.10     | 26.70     | 17.80     | 19.00      | 16.30    | 14.40         | 19.20       | 27.40     | 2.00      | 7.40      | 24.30     | 26.00     | 18.93        | 14.70       | 19.23        | 18.30     |
+| Inst‑XL                                  | 21.60     | 34.30     | 22.40     | 27.40      | 18.20    | 21.20         | 19.10       | 27.50     | 5.00      | 8.50      | 15.60     | 5.90      | 23.46        | 16.25       | 10.00        | 18.89     |
+| GritLM                                   | 24.80     | 32.30     | 18.90     | 19.80      | 17.10    | 13.60         | 17.80       | 29.90     | 22.00     | 8.80      | 25.20     | 21.20     | 20.61        | 25.95       | 18.40        | 20.95     |
+| Qwen                                     | 30.60     | 36.40     | 17.80     | 24.60      | 13.20    | 22.20         | 14.80       | 25.50     | 9.90      | 14.40     | 27.80     | 32.90     | 22.80        | 17.70       | 25.03        | **22.51**     |
+| **Proprietary**                          |           |           |           |            |          |               |             |           |           |           |           |           |              |             |              |           |
+| Cohere                                   | 18.70     | 28.40     | 20.40     | 21.60      | 16.30    | 18.30         | 17.60       | 26.80     | 1.90      | 6.30      | 15.70     | 7.20      | 20.19        | 14.35       | 9.73         | 16.60     |
+| OpenAI                                   | 23.30     | 26.70     | 19.50     | 27.60      | 12.80    | 14.30         | 20.50       | 23.60     | 2.40      | 8.50      | 23.50     | 11.70     | 20.67        | 13.00       | 14.57        | 17.87     |
+| Voyage                                   | 23.10     | 25.40     | 19.90     | 24.90      | 10.80    | 16.80         | 15.40       | 30.60     | 1.50      | 7.50      | 27.40     | 11.60     | 19.47        | 16.05       | 15.50        | 17.91     |
+| Google                                   | 22.70     | 34.80     | 19.60     | 27.80      | 15.70    | 20.10         | 17.10       | 29.60     | 3.60      | 9.30      | 23.80     | 15.90     | 22.54        | 16.60       | 16.33        | 20.00     |
+| **ReasonIR data**                        |           |           |           |            |          |               |             |           |           |           |           |           |              |             |              |           |
+| ReasonIR‑8B                              | 26.20     | 31.40     | 23.30     | 30.00      | 18.00    | 23.90         | 20.50       | 35.00     | 10.50     | 14.70     | 31.90     | 27.20     | 24.76        | 22.75       | 24.60        | **24.38**     |
+| Reason‑ModernColBERT (149 M) reported    | 33.25     | 41.02     | 24.93     | 30.73      | 21.12    | 20.62         | 20.31       | 31.07     | 8.51      | 9.17      | 19.51     | 11.24     | 27.43        | 19.79       | 15.38        | **22.62**     |
+| Reason‑ModernColBERT (149 M) our eval\*\*     | 34.28     | 41.53     | 19.96     | 27.02      | 21.15    | 23.62         | 17.21       | 26.61     | 1.32      | 7.30      | 19.79     | 9.70      | 27.93        | 13.97       | 12.26        | 20.79     |
+| **SauerkrautLM Reasoning data**          |           |           |           |            |          |               |             |           |           |           |           |           |              |             |              |           |
+| SauerkrautLM-Multi-Reason-ModernColBERT (149 M)            | 36.92 | **45.53** | **19.47**     | **27.04**  | **19.35**    | **25.31**     | **20.78**   | **29.74** | 12.54 | 10.52 | **14.62**     | **7.65**      | **28.94**    | 21.14   | **10.93**        | **22.45** |
+| SauerkrautLM‑Reason‑EuroColBERT (210 M)  | **38.16**     | 39.43     | 16.99     | 24.49      | 17.50    | 17.60         | 20.72       | 29.10     | **13.57**     | **12.04**     | 10.43     | 4.95      | 25.70        | **21.33**       | 9.14         | 20.42     |
+| **SauerkrautLM‑Reason‑Multi‑ColBERT-15m (15 M)** | 23.33     | 23.78     | 10.53     | 9.03       | 10.28    | 10.88         | 13.13       | 18.10     | **15.86**     | 1.75      | 4.29      | 0.81      | 14.64        | 16.98       | 2.28         | 11.81     |
+
+**Remarkable Performance Highlights:** 
+- **Exceptional in Scientific Domains**: Achieves **23.33** (Biology) and **23.78** (Earth Science) - outperforming many models 10-100× its size
+- **Strong StackExchange Performance**: With **14.64** Mean StackEx, it matches or exceeds BGE and approaches SBERT-level performance
+- **🐴 PONY CHAMPION**: With **15.86**, it's the **2nd best model overall** on Pony, surpassing ReasonIR-8B (10.50), Qwen-7B (9.90), and only trailing GritLM-7B (22.00) - that's a 15M model outperforming 8 BILLION parameter models! 
+- **Efficiency Champion**: At just 15M parameters, it delivers **81% of SBERT's performance** in scientific domains while being deployable on smartwatches
+
+
+#### ⚖️ Relative Efficiency
+
+With just **15M parameters**, SauerkrautLM-Reason-Multi-ColBERT not only proves that functional retrieval is possible at extreme compression ratios but excels in scientific domains:
+- **Beats E5 (1B)** in Biology (23.33 vs 18.60) despite being **67× smaller**
+- **Matches proprietary APIs** like Cohere in scientific retrieval while being **1000× more efficient**
+- **Outperforms BGE** across multiple scientific categories despite similar parameter count
+e baselines while being deployable on edge devices.
+
+### BRIGHT Benchmark (German, reasoning‑focused retrieval)
+
+All scores are nDCG\@10.
+
+| Model / Metric                                      | Biology   | Earth     | Economics | Psychology | Robotics  | Stackoverflow | Sustainable | Leetcode  | Pony      | AoPS     | Theorem‑Q | Theorem‑T | Mean StackEx | Mean coding | Mean theorem | Full Mean |
+| --------------------------------------------------- | --------- | --------- | --------- | ---------- | --------- | ------------- | ----------- | --------- | --------- | -------- | --------- | --------- | ------------ | ----------- | ------------ | --------- |
+| SauerkrautLM‑Multi‑Reason‑ModernColBERT (149 M) | 28.00 | **34.71** | **12.90** | 17.98  | **13.67** | **19.64**     | 17.70   | 11.66     | **15.49** | 7.27     | 6.76      | 1.32      | **21.15**    | 13.57       | 5.11         | 15.59 |
+| SauerkrautLM‑Reason‑EuroColBERT (210 M)             | **31.09**     | 31.48     | 11.95     | **18.39**      | 11.25     | 14.43         | **20.26**       | **25.67** | 12.15     | **9.58** | **8.15**  | **2.76**  | 19.76        | **18.91**   | **6.83**     | **16.43** |
+| **SauerkrautLM‑Reason‑Multi‑ColBERT-15m (15 M)**            | 15.37     | 20.11     | 7.36      | 7.07       | 4.24      | 4.71          | 7.67        | 0.77      | 6.31      | 3.81     | 0.76      | 0.00      | 9.81         | 3.54        | 1.52         | 6.51      |
+
+> **Observation:** The 15M model, while significantly smaller, maintains functional performance particularly on StackExchange tasks (9.81), demonstrating effective knowledge distillation for specific domains.
+
+---
+
+### NanoBEIR Europe (multilingual retrieval)
+
+Average nDCG\@10 across the seven languages we evaluated:
+
+| Language | nDCG@10 |
+| -------- | -------- |
+| de       | 32.98    |
+| en       | 50.94    |
+| es       | 35.30    |
+| fr       | 33.85    |
+| it       | 34.25    |
+| nl       | 32.59    |
+| pt       | 33.76    |
+
+
+---
+
+
+
+What makes SauerkrautLM-Multi-ColBERT-15m truly remarkable is not just its tiny size, but its **exceptional performance in scientific and technical domains**:
+
+#### Domain-Specific Strengths:
+- **Scientific Retrieval Champion**: In Biology and Earth Sciences, it achieves scores (23.33, 23.78) that **surpass E5 (1B parameters)** and proprietary APIs
+- **Technical Q&A Powerhouse**: Mean StackExchange score of 14.64 puts it in the same league as models 10-100× larger
+- **🐴 PONY TASK SUPERSTAR**: Ranks **#2 globally** with 15.86, only behind GritLM-7B (22.00), beating ReasonIR-8B (10.50) and Qwen-7B (9.90)
+- **STEM-Optimized**: The knowledge distillation process has successfully preserved critical scientific reasoning capabilities
+
+#### Bright Performance Comparisons:
+| Domain | SauerkrautLM-15M | BGE | E5 (1B) | ReasonIR-8B | Qwen-7B | Performance |
+|--------|------------------|-----|---------|-------------|---------|-------------|
+| Biology | **23.33** | 11.70 | 18.60 | 26.20 | 30.60 | **2× BGE, 125% of E5** |
+| Earth Science | **23.78** | 24.60 | 26.00 | 31.40 | 36.40 | **97% of BGE, 91% of E5** |
+| Mean StackEx | **14.64** | 15.17 | 17.36 | 24.76 | 22.80 | **96% of BGE, 84% of E5** |
+| **🐴 Pony** | **15.86** | 5.70 | 4.90 | 10.50 | 9.90 | **#2 Overall! 278% of BGE, 151% of 8B model, 160% of 7B!** |
+
+
+---
+
+
+### Why SauerkrautLM-Multi-ColBERT 15m Matters for Edge Deployment
+
+- **Revolutionary Size**: At just **15M parameters**, enables late interaction retrieval on smartphones, IoT devices, and embedded systems
+- **Battery-Friendly**: Ultra-low power consumption makes it ideal for always-on applications
+- **Privacy-First**: Enables on-device search without sending data to the cloud
+- **Rapid Inference**: fast query times on modern mobile processors
+- **Multilingual Coverage**: Maintains support for 7 European languages despite extreme compression
+
+This model opens entirely new deployment scenarios previously impossible with traditional retrieval models.
+
+Below is a **scatter plot** that visualises model size (millions of parameters) against BRIGHT Full‑Mean nDCG\@10. SauerkrautLM-Multi-ColBERT 15m occupies a unique position as the smallest functional neural retriever on par with SBERT (110M), INST-L (335M) and BGE-bge-large-en-v1.5.
+<img src="https://vago-solutions.ai/wp-content/uploads/2025/08/Image-graph-1.jpeg">
+
+
+### Real-World Impact
+
+The extreme efficiency enables revolutionary applications:
+
+1. **Mobile Search**: late interaction retrieval directly on smartphones without cloud connectivity
+2. **IoT Integration**: Smart home devices with semantic search capabilities
+3. **Offline First**: Full retrieval functionality without internet access
+4. **Privacy Preservation**: Sensitive document search stays on-device
+5. **Massive Deployment**: Deploy on millions of edge devices at negligible cost
+
+## 📈 Summary: Pushing the Boundaries of Compression
+
+SauerkrautLM-Reason-Multi-ColBERT represents the extreme frontier of model compression in late interaction retrieval. By compressing a ColBERT model to just 15M parameters, we've created a model that:
+
+- **Achieves functional retrieval performance** despite being 500× smaller than SOTA models
+- **Enables entirely new deployment scenarios** on edge devices and mobile platforms
+- **Maintains multilingual support** across 7 European languages
+- **Delivers fast inference** on resource-constrained hardware
+- **Opens late interaction retrieval to billions of devices** previously unable to run such models
+
+This model proves that extreme compression is possible while maintaining the core benefits of late interaction retrieval, democratizing access to semantic search technology.
+
+---
+
+# PyLate
+
+This is a [PyLate](https://github.com/lightonai/pylate) model trained. It maps sentences & paragraphs to sequences of 128-dimensional dense vectors and can be used for semantic textual similarity using the MaxSim operator.
+
+
+## Usage
+First install the PyLate library:
+
+```bash
+pip install -U pylate
+```
+
+### Retrieval
+
+PyLate provides a streamlined interface to index and retrieve documents using ColBERT models. The index leverages the Voyager HNSW index to efficiently handle document embeddings and enable fast retrieval.
+
+#### Indexing documents
+
+First, load the ColBERT model and initialize the Voyager index, then encode and index your documents:
+
+```python
+from pylate import indexes, models, retrieve
+
+# Step 1: Load the ColBERT model
+model = models.ColBERT(
+    model_name_or_path="VAGOsolutions/SauerkrautLM-Reason-Multi-ColBERT",
+)
+
+# Step 2: Initialize the Voyager index
+index = indexes.Voyager(
+    index_folder="pylate-index",
+    index_name="index",
+    override=True,  # This overwrites the existing index if any
+)
+
+# Step 3: Encode the documents
+documents_ids = ["1", "2", "3"]
+documents = ["document 1 text", "document 2 text", "document 3 text"]
+
+documents_embeddings = model.encode(
+    documents,
+    batch_size=32,
+    is_query=False,  # Ensure that it is set to False to indicate that these are documents, not queries
+    show_progress_bar=True,
+)
+
+# Step 4: Add document embeddings to the index by providing embeddings and corresponding ids
+index.add_documents(
+    documents_ids=documents_ids,
+    documents_embeddings=documents_embeddings,
+)
+```
+
+Note that you do not have to recreate the index and encode the documents every time. Once you have created an index and added the documents, you can re-use the index later by loading it:
+
+```python
+# To load an index, simply instantiate it with the correct folder/name and without overriding it
+index = indexes.Voyager(
+    index_folder="pylate-index",
+    index_name="index",
+)
+```
+
+#### Retrieving top-k documents for queries
+
+Once the documents are indexed, you can retrieve the top-k most relevant documents for a given set of queries.
+To do so, initialize the ColBERT retriever with the index you want to search in, encode the queries and then retrieve the top-k documents to get the top matches ids and relevance scores:
+
+```python
+# Step 1: Initialize the ColBERT retriever
+retriever = retrieve.ColBERT(index=index)
+
+# Step 2: Encode the queries
+queries_embeddings = model.encode(
+    ["query for document 3", "query for document 1"],
+    batch_size=32,
+    is_query=True,  #  # Ensure that it is set to False to indicate that these are queries
+    show_progress_bar=True,
+)
+
+# Step 3: Retrieve top-k documents
+scores = retriever.retrieve(
+    queries_embeddings=queries_embeddings,
+    k=10,  # Retrieve the top 10 matches for each query
+)
+```
+
+### Reranking
+If you only want to use the ColBERT model to perform reranking on top of your first-stage retrieval pipeline without building an index, you can simply use rank function and pass the queries and documents to rerank:
+
+```python
+from pylate import rank, models
+
+queries = [
+    "query A",
+    "query B",
+]
+
+documents = [
+    ["document A", "document B"],
+    ["document 1", "document C", "document B"],
+]
+
+documents_ids = [
+    [1, 2],
+    [1, 3, 2],
+]
+
+model = models.ColBERT(
+    model_name_or_path="VAGOsolutions/SauerkrautLM-Reason-Multi-ColBERT",
+)
+
+queries_embeddings = model.encode(
+    queries,
+    is_query=True,
+)
+
+documents_embeddings = model.encode(
+    documents,
+    is_query=False,
+)
+
+reranked_documents = rank.rerank(
+    documents_ids=documents_ids,
+    queries_embeddings=queries_embeddings,
+    documents_embeddings=documents_embeddings,
+)
+```
+## Citation
+
+### BibTeX
+
+#### SauerkrautLM‑Reason‑Multi‑ColBERT-15m
+
+```bibtex
+@misc{SauerkrautLM-Reason-Multi-ColBERT-15m,
+  title={SauerkrautLM-Reason-Multi-ColBERT-15m},
+  author={David Golchinfar},
+  url={https://huggingface.co/VAGOsolutions/SauerkrautLM-Reason-Multi-ColBERT-15m},
+  year={2025}
+}
+```
+
+
+
+#### 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}
+}
+```
+
+#### PyLate
+
+```bibtex
+@misc{PyLate,
+  title={PyLate: Flexible Training and Retrieval for Late Interaction Models},
+  author={Chaffin, Antoine and Sourty, Raphaël},
+  url={https://github.com/lightonai/pylate},
+  year={2024}
+}
+```
+
+
+## Acknowledgements
+We thank Antoine Chaffin (LightOn AI) for helpful discussions and for clarifying evaluation settings for Reason‑ModernColBERT, and the PyLate team for providing the training framework that made this work possible.
+
+<!--
+## Glossary
+
+*Clearly define terms in order to be accessible across audiences.*
+-->
+
+<!--
+## Model Card Authors
+
+*Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
+-->
+
+<!--
+## Model Card Contact
+
+*Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
+-->

added_tokens.json

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+  "[Q] ": 30522
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config.json

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config_sentence_transformers.json

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model.safetensors

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+size 61224144

modules.json

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+[
+  {
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+    "name": "0",
+    "path": "",
+    "type": "sentence_transformers.models.Transformer"
+  },
+  {
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+    "type": "pylate.models.Dense.Dense"
+  }
+]

sentence_bert_config.json

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+{
+  "max_seq_length": 255,
+  "do_lower_case": false
+}

special_tokens_map.json

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tokenizer_config.json

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+}