rwkv-emb 0.1.3

The EmbeddingRWKV Model

EmbeddingRWKV

A high-efficiency text embedding and reranking model based on RWKV architecture.

🤖 Models & Weights

You can download the weights from the HuggingFace Repository.

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.

import os
# 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(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(doc_batch, None)

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

For production use cases, running inference in batches is significantly faster.

⚠️ 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

Workflow

1. Format Query and Document based on Online template.
2. Run the Embedding Model to generate the final State.
3. Feed the Attention State (state[1]) into the ReRanker to get a relevance score.

📝 Online Mode Usage Example

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

# 1. Load Models
# The ReRanker weights are stored in the differernt checkpoint
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
# We treat the Query and Document as a single sequence.
pairs = []
online_template = "Instruct: Given a query, retrieve documents that answer the query\nDocument: {document}\nQuery: {query}"
for doc in documents:
    # Format: Instruct + Document + Query
    text = online_template.format(document=doc, query=query)
    pairs.append(text)

# 4. Tokenize & Pad (Critical for Batch Performance)
batch_tokens = [tokenizer.encode(p, add_eos=True) for p in pairs]

# Left pad to same length for efficiency
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
# We don't need the embedding output here, we only need the final 'state'
_, state = emb_model.forward(batch_tokens, None)

# 6. Score with ReRanker
# The ReRanker expects the Attention State: state[1]
# state[1] shape: [Layers, Batch, Heads, HeadSize, HeadSize]
logits = reranker.forward(state[1])
scores = torch.sigmoid(logits) # Convert logits to probabilities (0-1)

# 7. Print Results
print("\nRWKVReRanker 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 $O(L_{query})$.

Workflow

1. Indexing: Process Instruct + Document -> Save State.
2. Querying: Load State -> Process Query -> Score.

📝 Offline Mode Usage Example

# --- Phase 1: Indexing (Pre-computation) ---
# Note: Do NOT add EOS here, because the sequence continues with the query later.
doc_template = "Instruct: Given a query, retrieve documents that answer the query\nDocument: {document}\n"
cached_states = []

print("Indexing documents...")
for doc in documents:
    text = doc_template.format(document=doc)
    # add_eos=False is CRITICAL here
    tokens = tokenizer.encode(text, add_eos=False) 
    
    # Forward pass
    _, state = emb_model.forward(tokens, None)
    
    # Move state to CPU to save GPU memory during storage
    # State structure: [Tensor(Tokenshift), Tensor(TimeMix)]
    cpu_state = [s.cpu() for s in state]
    cached_states.append(cpu_state)
# Save cached states to disk (optional)
torch.save(cached_states, 'cached_doc_states.pth')

# --- Phase 2: Querying (Fast Retrieval) ---
query_template = "Query: {query}"
query_text = query_template.format(query=query)
# Now we add EOS to mark the end of the full sequence
query_tokens = tokenizer.encode(query_text, add_eos=True)

print(f"Processing query: '{query}' against {len(cached_states)} cached docs...")

# We can batch the query processing against multiple document states
# 1. Prepare a batch of states (Move back to GPU)
#    Note: We must CLONE/DEEPCOPY because RWKV modifies state in-place!
batch_states = [[], []]
for cpu_s in cached_states:
    batch_states[0].append(cpu_s[0].clone().cuda()) # Tokenshift State
    batch_states[1].append(cpu_s[1].clone().cuda()) # TimeMix State

# Stack into batch tensors
# State[0]: [Layers, 2, 1, Hidden] -> Stack dim 2 -> [Layers, 2, Batch, Hidden]
# State[1]: [Layers, 1, Heads, HeadSize, HeadSize] -> Stack dim 1 -> [Layers, Batch, Heads, ...]
state_input = [
    torch.stack(batch_states[0], dim=2).squeeze(3), 
    torch.stack(batch_states[1], dim=1).squeeze(2)
]

# 2. Prepare query tokens (Broadcast query to batch size)
batch_size = len(documents)
batch_query_tokens = [query_tokens] * batch_size

# 3. Fast Forward (Only processing query tokens!)
_, final_state = emb_model.forward(batch_query_tokens, state_input)
logits = reranker.forward(final_state[1])
scores = torch.sigmoid(logits)

print("\nRWKVReRanker Offline Scores:")
for doc, score in zip(documents, scores):
    print(f"[{score:.4f}] {doc}")

Summary of Differences

Feature	1. Embedding (Cosine)	2. Online Reranking	3. Offline Reranking
Accuracy	Good	Best	Best (Identical to Online)
Latency	Extremely Fast	Slow ($L_{doc} + L_{query}$)	Fast ($L_{query}$ 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