---
license: apache-2.0
pipeline_tag: text-retrieval
---

# EmbeddingRWKV

EmbeddingRWKV is a high-efficiency text embedding and reranking model based on the RWKV architecture, introduced in the paper [EmbeddingRWKV: State-Centric Retrieval with Reusable States](https://huggingface.co/papers/2601.07861).

It utilizes **State-Centric Retrieval**, a unified retrieval paradigm that uses "states" as a bridge to connect embedding models and rerankers, significantly improving inference speed for reranking tasks.

[**Paper**](https://huggingface.co/papers/2601.07861) | [**GitHub**](https://github.com/howard-hou/EmbeddingRWKV)

## 📦 Installation

```bash
pip install rwkv-emb
```

## 🤖 Models & Weights

You can download the weights from the [HuggingFace Repository](https://huggingface.co/howard-hou/EmbeddingRWKV/tree/main).

| Size / Level | Embedding Model (Main) | Matching Reranker (Paired) | Notes |
| :--- | :--- | :--- | :--- |
| **Tiny** | `rwkv0b1-emb-curriculum.pth` | `rwkv0b1-reranker.pth` | Ultra-fast, minimal memory. |
| **Base** | `rwkv0b4-emb-curriculum.pth` | `rwkv0b3-reranker.pth` | Balanced speed & performance. |
| **Large** | `rwkv1b4-emb-curriculum.pth` | `rwkv1b3-reranker.pth` | Best performance, higher VRAM usage. |

## 🚀 Quick Start (End-to-End)

Get text embeddings in just a few lines. The tokenizer and model are designed to work seamlessly together.

> **Note**: Always set `add_eos=True` during tokenization. The model relies on the EOS token (`65535`) to mark the end of a sentence for correct embedding generation.

```python
import os
from torch.nn import functional as F
# Set environment for JIT compilation (Optional, set to '1' for CUDA acceleration)
os.environ["RWKV_CUDA_ON"] = '1'

from rwkv_emb.tokenizer import RWKVTokenizer
from rwkv_emb.model import EmbeddingRWKV

# Fast retrieval, good for initial candidate filtering.
emb_model = EmbeddingRWKV(model_path='/path/to/model.pth')
tokenizer = RWKVTokenizer()

query = "What represents the end of a sequence?"
documents = [
    "The EOS token is used to mark the end of a sentence.",
    "Apples are red and delicious fruits.",
    "Machine learning requires large datasets."
]
# Encode Query
q_tokens = tokenizer.encode(query, add_eos=True)
q_emb, _ = emb_model.forward_text_only(q_tokens, None) # shape: [1, Dim]

# Encode Documents (Batch)
doc_batch = [tokenizer.encode(doc, add_eos=True) for doc in documents]
max_doc_len = max(len(t) for t in doc_batch)
for i in range(len(doc_batch)):
    pad_len = max_doc_len - len(doc_batch[i])
    # Prepend 0s (Left Padding)
    doc_batch[i] = [0] * pad_len + doc_batch[i]

d_embs, _ = emb_model.forward_text_only(doc_batch, None)

# Calculate Cosine Similarity
scores_emb = F.cosine_similarity(q_emb, d_embs)
print("
EmbeddingRWKV Cosine Similarity:")
for doc, score in zip(documents, scores_emb):
    print(f"[{score.item():.4f}] {doc}")
```

### ⚠️ Critical Performance Tip: Pad to Same Length

While the model supports batches with variable sequence lengths, **we strongly recommend padding all sequences to the same length** for maximum GPU throughput.

  - **Pad Token**: `0`
  - **Performance**: Fixed-length batches allow the CUDA kernel to parallelize computation efficiently. Variable-length batches will trigger a slower execution path.


## 🎯 RWKVReRanker (State-based Reranker)

The `RWKVReRanker` utilizes the final hidden state produced by the main `EmbeddingRWKV` model to score the relevance between a query and a document.

### Online Mode Usage Example

```python
import torch
from rwkv_emb.tokenizer import RWKVTokenizer
from rwkv_emb.model import EmbeddingRWKV, RWKVReRanker

# 1. Load Models
emb_model = EmbeddingRWKV(model_path='/path/to/EmbeddingRWKV.pth')
reranker = RWKVReRanker(model_path='/path/to/RWKVReRanker.pth')

tokenizer = RWKVTokenizer()

# 2. Prepare Data (Query + Candidate Documents)
query = "What represents the end of a sequence?"
documents = [
    "The EOS token is used to mark the end of a sentence.",
    "Apples are red and delicious fruits.",
    "Machine learning requires large datasets."
]

# 3. Construct Input Pairs
pairs = []
online_template = "Instruct: Given a query, retrieve documents that answer the query
Document: {document}
Query: {query}"
for doc in documents:
    text = online_template.format(document=doc, query=query)
    pairs.append(text)

# 4. Tokenize & Pad
batch_tokens = [tokenizer.encode(p, add_eos=True) for p in pairs]
max_len = max(len(t) for t in batch_tokens)
for i in range(len(batch_tokens)):
    batch_tokens[i] = [0] * (max_len - len(batch_tokens[i])) + batch_tokens[i]

# 5. Get States from Embedding Model
_, state = emb_model.forward(batch_tokens, None)

# 6. Score with ReRanker
logits = reranker.forward(state[1])
scores = torch.sigmoid(logits) 

# 7. Print Results
print("
RWKVReRanker Online Scores:")
for doc, score in zip(documents, scores):
    print(f"[{score:.4f}] {doc}")
```

### Offline Mode (Cached Doc State)
For scenarios where documents are static but queries change (e.g., Search Engines, RAG), you can **pre-compute and cache the document states**. This reduces query-time latency from O(L_doc + L_query) to just O(L_query).

```python
# --- Phase 1: Indexing (Pre-computation) ---
doc_template = "Instruct: Given a query, retrieve documents that answer the query
Document: {document}
"
cached_states = []

for doc in documents:
    text = doc_template.format(document=doc)
    tokens = tokenizer.encode(text, add_eos=False) 
    _, state = emb_model.forward(tokens, None)
    cpu_state = [s.cpu() for s in state]
    cached_states.append(cpu_state)

# --- Phase 2: Querying (Fast Retrieval) ---
query_template = "Query: {query}"
query_text = query_template.format(query=query)
query_tokens = tokenizer.encode(query_text, add_eos=True)

batch_states = [[], []]
for cpu_s in cached_states:
    batch_states[0].append(cpu_s[0].clone().cuda())
    batch_states[1].append(cpu_s[1].clone().cuda())

state_input = [
    torch.stack(batch_states[0], dim=2).squeeze(3), 
    torch.stack(batch_states[1], dim=1).squeeze(2)
]

batch_query_tokens = [query_tokens] * len(documents)
_, final_state = emb_model.forward(batch_query_tokens, state_input)
logits = reranker.forward(final_state[1])
scores = torch.sigmoid(logits)
```

## Summary of Differences

| Feature | 1. Embedding (Cosine) | 2. Online Reranking | 3. Offline Reranking |
| :--- | :--- | :--- | :--- |
| **Accuracy** | Good | **Best** | **Best** (Identical to Online) |
| **Latency** | Extremely Fast | Slow O(L_doc + L_query) | Fast O(L_query) only |
| **Input** | Query & Doc separate | `Instruct + Doc + Query` | `Query` (on top of cached Doc) |
| **Storage** | Low (Vector only) | None | High (Stores Hidden States) |
| **Best For** | Initial Retrieval (Top-k) | Reranking few candidates | Reranking many candidates |

## Citation

```bibtex
@article{hou2025embeddingrwkv,
  title={EmbeddingRWKV: State-Centric Retrieval with Reusable States},
  author={Hou, Howard and others},
  journal={arXiv preprint arXiv:2601.07861},
  year={2026}
}
```