LFM2.5-ColBERT-350M

LFM2.5-ColBERT-350M is a late interaction retriever with best-in-class multilingual performance. It stores one vector per token and matches queries to documents with MaxSim, so you can store documents in one language (for example, a product description in English) and retrieve them in many languages with high accuracy.

• LFM2.5-ColBERT-350M offers best-in-class accuracy across 11 languages.
• Inference speed is on par with much smaller models, thanks to the efficient LFM2 backbone.
• You can use it as a drop-in replacement in your current RAG pipelines to improve performance.

Find more information about LFM2.5-ColBERT-350M in our blog post.

🏃 How to run

Example usage with llama.cpp:

Start llama-server

llama-server -hf LiquidAI/LFM2.5-ColBERT-350M-GGUF --embeddings

Make requests to embed queries and documents, and compute MaxSim similarity scores

❯ uv run colbert-rerank.py

Score: 29.04 | Q: What is panda? | D: hi
Score: 29.57 | Q: What is panda? | D: it is a bear
Score: 30.07 | Q: What is panda? | D: The giant panda (Ailuropoda melanoleuca), sometimes called a panda bear or simply panda, is a bear species endemic to China.

# /// script
# requires-python = ">=3.10"
# dependencies = [
#     "transformers",
#     "huggingface-hub",
#     "numpy",
#     "requests",
#     "torch",
# ]
# ///

# colbert-rerank.py
from transformers import AutoTokenizer
from huggingface_hub import hf_hub_download
import numpy as np, requests, torch, torch.nn.functional as F, json


model_id = "LiquidAI/LFM2.5-ColBERT-350M"
tokenizer = AutoTokenizer.from_pretrained(model_id)
config = json.load(open(hf_hub_download(model_id, "config_sentence_transformers.json")))
skiplist = set(
    t
    for w in config["skiplist_words"]
    for t in tokenizer.encode(w, add_special_tokens=False)
)


def maxsim(q, d):
    return (q @ d.T).max(dim=1).values.sum().item()


def preprocess(text, is_query):
    prefix = config["query_prefix"] if is_query else config["document_prefix"]
    toks = tokenizer.encode(prefix + text)
    max_len = config["query_length"] if is_query else config["document_length"]
    if is_query:
        toks += [tokenizer.pad_token_id] * (max_len - len(toks))
    else:
        toks = toks[:max_len]
    mask = None if is_query else [t not in skiplist for t in toks]
    return toks, mask


def embed(content, mask=None):
    emb = np.array(
        requests.post(
            "http://localhost:8080/embedding",
            json={"content": content},
        ).json()[0]["embedding"]
    )
    if mask:
        emb = emb[mask]
    emb = torch.from_numpy(emb)
    emb = F.normalize(emb, p=2, dim=-1)  # L2 normalize each token embedding
    return emb.unsqueeze(0)


docs = [
    "hi",
    "it is a bear",
    "The giant panda (Ailuropoda melanoleuca), sometimes called a panda bear or simply panda, is a bear species endemic to China.",
]
query = "What is panda?"

q = embed(*preprocess(query, True))
d = [embed(*preprocess(doc, False)) for doc in docs]
s = [(query, doc, maxsim(q.squeeze(), di.squeeze())) for doc, di in zip(docs, d)]
for q_text, d_text, score in s:
    print(f"Score: {score:.2f} | Q: {q_text} | D: {d_text}")

Find more details in the original model card: https://huggingface.co/LiquidAI/LFM2.5-ColBERT-350M