Upload Zembed GGUF

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README.md

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+---
+license: apache-2.0
+language:
+- en
+- multilingual
+base_model: zeroentropy/zembed-1-embedding
+base_model_relation: quantized
+pipeline_tag: feature-extraction
+tags:
+- gguf
+- finance
+- legal
+- healthcare
+- code
+- stem
+- medical
+- multilingual
+library_name: llama.cpp
+model_max_length: 32768
+---
+
+# About
+
+This model was converted to GGUF format from zeroentropy/zerank-2 using llama.cpp (release b10269). Refer to the [original model card](https://huggingface.co/zeroentropy/zembed-1-embedding) for more details on the model.
+
+# Original Model Card
+
+<img src="https://i.imgur.com/oxvhvQu.png"/>
+
+# Releasing zeroentropy/zembed-1
+
+In retrieval systems, [embedding models determine the quality of your search](https://www.zeroentropy.dev/articles/how-to-overcome-poor-search-results-with-the-right-embedding-solution).
+
+However, SOTA embedding models are closed-source and proprietary. At ZeroEntropy, we've trained a SOTA 4B open-weight multilingual embedding model that outperforms every competitor we benchmarked, and we're launching it here on HuggingFace.
+
+This model [outperforms](https://huggingface.co/zeroentropy/zembed-1#evaluations) `OpenAI text-embedding-large`, `Cohere Embed v4`, `gemini-embedding-001`, and `voyage-4-nano` across finance, healthcare, legal, conversational, manufacturing, code, and STEM.
+
+zembed-1 is distilled directly from our SOTA reranker [zerank-2](https://huggingface.co/zeroentropy/zerank-2) using our [zELO methodology](https://arxiv.org/abs/2509.12541), which models relevance scores as adjusted [Elo ratings](https://en.wikipedia.org/wiki/Elo_rating_system). Standard contrastive training on binary labels can't match this signal. See [our blog post](https://www.zeroentropy.dev/articles/introducing-zembed-1-the-worlds-best-multilingual-text-embedding-model) for details.
+
+The model supports flexible dimension projections (2560, 1280, 640, 320, 160, 80, 40) and quantization down to binary, compressing a full 8 KB vector to under 128 bytes with a controlled accuracy trade-off. See our Technical Report (Coming soon!) for details on the projection method. zembed-1 is multilingual from the ground up, with over half the training data in non-English languages.
+
+This model is released under the [Apache License 2.0](./LICENSE).
+
+## Model Details
+
+| Property | Value |
+|---|---|
+| Parameters | 4B |
+| Context Length | 32,768 tokens (32k) |
+| Base Model | Qwen/Qwen3-4B |
+| Embedding Dimensions | 2560, 1280, 640, 320, 160, 80, 40 |
+| License | Apache-2.0 |
+
+## How to Use
+
+```python
+from sentence_transformers import SentenceTransformer
+
+# Initialize model
+model = SentenceTransformer(
+    "zeroentropy/zembed-1",
+    trust_remote_code=True,
+    model_kwargs={"torch_dtype": "bfloat16"},
+)
+
+# Define query and documents
+query = "What is backpropagation?"
+documents = [
+    "Backpropagation is a fundamental algorithm for training neural networks by computing gradients.",
+    "Gradient descent is used to optimize model parameters during the training process.",
+    "Neural network training relies on efficient computation of derivatives through backpropagation.",
+]
+
+# Encode query and documents (uses task-specific prompts automatically)
+query_embeddings = model.encode_query(query)
+document_embeddings = model.encode_document(documents)
+# (2560,) (3, 2560)
+
+# Compute cosine similarities
+similarities = model.similarity(query_embeddings, document_embeddings)
+# tensor([[0.7525, 0.5670, 0.6835]])
+```
+
+The model can also be used through ZeroEntropy's [/models/embed](https://docs.zeroentropy.dev/api-reference/models/embed) endpoint.
+
+## Evaluations
+
+NDCG@10 scores between `zembed-1` and competing embedding models, averaged across public and private benchmarks per domain. Full per-benchmark breakdown [here](https://docs.google.com/spreadsheets/d/1qFXGZLMg6-O5tVLIJS3tpf5QNJxCHiiQtj35dZub4vY/edit?gid=0#gid=0).
+
+| Domain | ZeroEntropy zembed-1 | voyage-4-nano | Qwen3 4B | Cohere Embed v4 | gemini-embed-001 | jina-v5-small | OpenAI Large | bge-m3 |
+|------------------|----------------------|---------------|----------|-----------------|-------------------|---------------|--------------|--------|
+| Finance | **0.4476** | 0.4227 | 0.3715 | 0.3670 | 0.3291 | 0.3576 | 0.3291 | 0.3085 |
+| Healthcare | **0.6260** | 0.5356 | 0.5134 | 0.4750 | 0.5008 | 0.5132 | 0.5315 | 0.3620 |
+| Legal | **0.6723** | 0.5957 | 0.5858 | 0.5894 | 0.6069 | 0.5716 | 0.5099 | 0.5207 |
+| Conversational | **0.5385** | 0.4045 | 0.4034 | 0.4244 | 0.4247 | 0.4430 | 0.3988 | 0.3296 |
+| Manufacturing | **0.5556** | 0.4857 | 0.4932 | 0.4919 | 0.4664 | 0.4725 | 0.4736 | 0.3736 |
+| Web Search | 0.6165 | 0.5977 | 0.6914 | **0.7242** | 0.5881 | 0.6772 | 0.6750 | 0.6311 |
+| Code | **0.6452** | 0.6415 | 0.6379 | 0.6277 | 0.6305 | 0.6354 | 0.6155 | 0.5584 |
+| STEM & Math | **0.5283** | 0.5012 | 0.5219 | 0.4698 | 0.4840 | 0.3780 | 0.3905 | 0.3399 |
+| Enterprise | **0.3750** | 0.3600 | 0.2935 | 0.2915 | 0.3224 | 0.3012 | 0.3307 | 0.2213 |
+| **Average** | **0.5561** | **0.5050** | **0.5013** | **0.4957** | **0.4837** | **0.4833** | **0.4727** | **0.4050** |
+
+<img src="assets/zembed_eval_chart.png" alt="Bar chart comparing zembed-1 NDCG@10 scores against competing embedding models across domains" width="1000"/>
+
+## License
+
+This model is licensed under the [Apache License 2.0](./LICENSE).

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