Elephant Embeddings V1 Text Small

elephant-embeddings-v1-text-small is the text embedding model in the Agentic Intelligence Lab Elephant Embeddings V1 family.

This ModelScope release is maintained by agentic-intelligence-lab to make Elephant embedding models easier to download and deploy in mainland China. It mirrors and renames the upstream HuggingFace model llm-semantic-router/eggon-embed under a consistent Elephant model namespace.

Positioning

This model is a multilingual long-context text embedding model for agent-native retrieval and semantic matching. It is designed for systems where embeddings are on the runtime hot path:

• agent memory recall
• knowledge retrieval and RAG
• tool, skill, and route matching
• long-horizon state search
• multilingual semantic indexing
• clustering and deduplication

The model combines 32K context, ModernBERT encoder architecture, and 2D Matryoshka training so one embedding space can serve multiple latency, storage, and quality budgets.

Model at a glance

Why it fits agentic workloads

Agentic systems call embedding models repeatedly: before retrieval, during routing, while matching tools, when searching memory, and when compressing or reranking state. This model is optimized for that operating pattern rather than for a single offline benchmark.

Key advantages:

• One semantic space across the stack: routing, retrieval, memory lookup, and semantic matching can share one vector space.
• Budget-adaptive vectors: truncate full 768-dimensional vectors to 256d, 128d, or 64d for cheaper indexes and faster candidate generation.
• Long-context representation: encode larger notes, traces, tool descriptions, and document chunks before aggressive chunking is required.
• Practical deployment size: a 307M-class encoder is easier to host than much larger embedding models when inference is frequent.

Recommended use cases

Quick start on ModelScope

pip install modelscope sentence-transformers torch

from modelscope import snapshot_download
from sentence_transformers import SentenceTransformer

repo_id = "agentic-intelligence-lab/elephant-embeddings-v1-text-small"
local_dir = snapshot_download(repo_id)

model = SentenceTransformer(local_dir)

texts = [
    "Find tool descriptions related to browser automation.",
    "检索和用户历史偏好相关的记忆。",
    "Retrieve notes about deployment failures in staging.",
]

embeddings = model.encode(texts, normalize_embeddings=True)
print(embeddings.shape)  # (3, 768)

Matryoshka truncation

import torch.nn.functional as F
from modelscope import snapshot_download
from sentence_transformers import SentenceTransformer

local_dir = snapshot_download("agentic-intelligence-lab/elephant-embeddings-v1-text-small")
model = SentenceTransformer(local_dir)

embeddings = model.encode(texts, convert_to_tensor=True, normalize_embeddings=True)

# Balanced retrieval tier
embeddings_256d = F.normalize(embeddings[:, :256], p=2, dim=1)

# Low-cost routing or large memory-bank tier
embeddings_64d = F.normalize(embeddings[:, :64], p=2, dim=1)

Evaluation snapshot

These results make the model useful for systems that must balance quality, latency, vector size, and deployment simplicity.

Files

Lineage

This ModelScope package is published by agentic-intelligence-lab as part of the Elephant model release line. It mirrors the upstream HuggingFace model llm-semantic-router/eggon-embed and keeps the model artifacts unchanged except for the repository naming and model card presentation.

Limitations

• Full 768-dimensional embeddings are recommended for important final-stage retrieval decisions.
• Aggressive dimension or layer reduction trades quality for speed and storage efficiency.
• Very long inputs are supported, but they still increase compute and memory cost.
• The model is optimized for retrieval and semantic similarity, not text generation.

Citation

@misc{elephant-embeddings-v1-text-small,
  title={Elephant Embeddings V1 Text Small},
  author={Agentic Intelligence Lab},
  year={2026},
  url={https://modelscope.cn/models/agentic-intelligence-lab/elephant-embeddings-v1-text-small}
}

License

Apache 2.0