Add Infinity-compatible zembed configuration with original weights

.gitattributes

@@ -1,35 +1,6 @@
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+tokenizer.json filter=lfs diff=lfs merge=lfs -text
+zembed_eval_chart.png filter=lfs diff=lfs merge=lfs -text
+assets/zembed_eval_chart.png filter=lfs diff=lfs merge=lfs -text

1_Pooling/config.json

@@ -0,0 +1,10 @@
+{
+  "word_embedding_dimension": 2560,
+  "pooling_mode_cls_token": false,
+  "pooling_mode_mean_tokens": false,
+  "pooling_mode_max_tokens": false,
+  "pooling_mode_mean_sqrt_len_tokens": false,
+  "pooling_mode_weightedmean_tokens": false,
+  "pooling_mode_lasttoken": true,
+  "include_prompt": true
+}

LICENSE

@@ -0,0 +1,202 @@
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README.md

@@ -0,0 +1,51 @@
+---
+license: apache-2.0
+language:
+- en
+- multilingual
+base_model: zeroentropy/zembed-1-embedding
+pipeline_tag: feature-extraction
+library_name: sentence-transformers
+tags:
+- text-embeddings-inference
+- infinity
+model_max_length: 32768
+---
+
+# zembed-1 for Infinity
+
+Infinity configuration adaptation of [ZeroEntropy's zembed-1-embedding](https://huggingface.co/zeroentropy/zembed-1-embedding), pinned to revision `cf13c81f3274394053d166740294f7eea4586f7a`. Model weights, tokenizer, pooling, and learned projection weights are unchanged. The original model card is retained in [UPSTREAM_README.md](UPSTREAM_README.md), and the original license is retained in [LICENSE](LICENSE).
+
+`modules.json` replaces `modeling_zembed.ZembedTransformer` with the standard Sentence Transformers `Transformer` module so Infinity v3's LibTorch loader accepts the checkpoint. The original wrapper appends `<|im_end|>\n` to each input. **Clients must still append this suffix**; this copy does not make raw-text requests automatically equivalent to the original model.
+
+## Run
+
+```bash
+docker run --rm --gpus all -p 7997:80 -v infinity-hf-cache:/data \
+  ghcr.io/michaelfeil/infinity_v3:nvidia-3.0.0-alpha.3 \
+  --model-id michaelfeil/zembed-1-infinity \
+  --backend libtorch --torch-device cuda --device-id 0 \
+  --dtype bfloat16 --pooling last-token \
+  --default-prompt-name document --max-batch-tokens 32768
+```
+
+The saved `query` and `document` prompts provide the prefix. Supply the suffix exactly once, after the unformatted input text:
+
+```bash
+curl http://localhost:7997/embed \
+  -H 'Content-Type: application/json' \
+  -d '{"model":"michaelfeil/zembed-1-infinity","inputs":["What is backpropagation?<|im_end|>\n"],"prompt_name":"query","normalize":true,"truncate":false}'
+```
+
+Use `prompt_name: "document"` for documents. [infinity_client.py](infinity_client.py) adds the suffix automatically:
+
+```python
+from infinity_client import embed
+
+queries = embed(["What is backpropagation?"], kind="query")
+documents = embed(["Backpropagation computes gradients."], kind="document")
+```
+
+This setup returns normalized, full-width **2560-dimensional** embeddings with last-token pooling. The original learned dimensionality projections are preserved, but are not applied by this Infinity setup; simple truncation is not equivalent to those projections. Inputs above 32768 tokens should be chunked. Avoid truncating away the terminal suffix.
+
+This repository is a configuration adaptation, not a new training run. Weight hashes are recorded in [infinity-preparation.json](infinity-preparation.json). It has not yet undergone an end-to-end embedding parity run against the original Sentence Transformers model.

UPSTREAM_README.md

@@ -0,0 +1,100 @@
+---
+license: apache-2.0
+language:
+- en
+- multilingual
+base_model:
+- Qwen/Qwen3-4B
+pipeline_tag: feature-extraction
+tags:
+- finance
+- legal
+- healthcare
+- code
+- stem
+- medical
+- multilingual
+library_name: sentence-transformers
+model_max_length: 32768
+---
+
+<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).

added_tokens.json

@@ -0,0 +1,28 @@
+{
+  "</think>": 151668,
+  "</tool_call>": 151658,
+  "</tool_response>": 151666,
+  "<think>": 151667,
+  "<tool_call>": 151657,
+  "<tool_response>": 151665,
+  "<|box_end|>": 151649,
+  "<|box_start|>": 151648,
+  "<|endoftext|>": 151643,
+  "<|file_sep|>": 151664,
+  "<|fim_middle|>": 151660,
+  "<|fim_pad|>": 151662,
+  "<|fim_prefix|>": 151659,
+  "<|fim_suffix|>": 151661,
+  "<|im_end|>": 151645,
+  "<|im_start|>": 151644,
+  "<|image_pad|>": 151655,
+  "<|object_ref_end|>": 151647,
+  "<|object_ref_start|>": 151646,
+  "<|quad_end|>": 151651,
+  "<|quad_start|>": 151650,
+  "<|repo_name|>": 151663,
+  "<|video_pad|>": 151656,
+  "<|vision_end|>": 151653,
+  "<|vision_pad|>": 151654,
+  "<|vision_start|>": 151652
+}

chat_template.jinja

@@ -0,0 +1,89 @@
+{%- if tools %}
+    {{- '<|im_start|>system\n' }}
+    {%- if messages[0].role == 'system' %}
+        {{- messages[0].content + '\n\n' }}
+    {%- endif %}
+    {{- "# Tools\n\nYou may call one or more functions to assist with the user query.\n\nYou are provided with function signatures within <tools></tools> XML tags:\n<tools>" }}
+    {%- for tool in tools %}
+        {{- "\n" }}
+        {{- tool | tojson }}
+    {%- endfor %}
+    {{- "\n</tools>\n\nFor each function call, return a json object with function name and arguments within <tool_call></tool_call> XML tags:\n<tool_call>\n{\"name\": <function-name>, \"arguments\": <args-json-object>}\n</tool_call><|im_end|>\n" }}
+{%- else %}
+    {%- if messages[0].role == 'system' %}
+        {{- '<|im_start|>system\n' + messages[0].content + '<|im_end|>\n' }}
+    {%- endif %}
+{%- endif %}
+{%- set ns = namespace(multi_step_tool=true, last_query_index=messages|length - 1) %}
+{%- for message in messages[::-1] %}
+    {%- set index = (messages|length - 1) - loop.index0 %}
+    {%- if ns.multi_step_tool and message.role == "user" and message.content is string and not(message.content.startswith('<tool_response>') and message.content.endswith('</tool_response>')) %}
+        {%- set ns.multi_step_tool = false %}
+        {%- set ns.last_query_index = index %}
+    {%- endif %}
+{%- endfor %}
+{%- for message in messages %}
+    {%- if message.content is string %}
+        {%- set content = message.content %}
+    {%- else %}
+        {%- set content = '' %}
+    {%- endif %}
+    {%- if (message.role == "user") or (message.role == "system" and not loop.first) %}
+        {{- '<|im_start|>' + message.role + '\n' + content + '<|im_end|>' + '\n' }}
+    {%- elif message.role == "assistant" %}
+        {%- set reasoning_content = '' %}
+        {%- if message.reasoning_content is string %}
+            {%- set reasoning_content = message.reasoning_content %}
+        {%- else %}
+            {%- if '</think>' in content %}
+                {%- set reasoning_content = content.split('</think>')[0].rstrip('\n').split('<think>')[-1].lstrip('\n') %}
+                {%- set content = content.split('</think>')[-1].lstrip('\n') %}
+            {%- endif %}
+        {%- endif %}
+        {%- if loop.index0 > ns.last_query_index %}
+            {%- if loop.last or (not loop.last and reasoning_content) %}
+                {{- '<|im_start|>' + message.role + '\n<think>\n' + reasoning_content.strip('\n') + '\n</think>\n\n' + content.lstrip('\n') }}
+            {%- else %}
+                {{- '<|im_start|>' + message.role + '\n' + content }}
+            {%- endif %}
+        {%- else %}
+            {{- '<|im_start|>' + message.role + '\n' + content }}
+        {%- endif %}
+        {%- if message.tool_calls %}
+            {%- for tool_call in message.tool_calls %}
+                {%- if (loop.first and content) or (not loop.first) %}
+                    {{- '\n' }}
+                {%- endif %}
+                {%- if tool_call.function %}
+                    {%- set tool_call = tool_call.function %}
+                {%- endif %}
+                {{- '<tool_call>\n{"name": "' }}
+                {{- tool_call.name }}
+                {{- '", "arguments": ' }}
+                {%- if tool_call.arguments is string %}
+                    {{- tool_call.arguments }}
+                {%- else %}
+                    {{- tool_call.arguments | tojson }}
+                {%- endif %}
+                {{- '}\n</tool_call>' }}
+            {%- endfor %}
+        {%- endif %}
+        {{- '<|im_end|>\n' }}
+    {%- elif message.role == "tool" %}
+        {%- if loop.first or (messages[loop.index0 - 1].role != "tool") %}
+            {{- '<|im_start|>user' }}
+        {%- endif %}
+        {{- '\n<tool_response>\n' }}
+        {{- content }}
+        {{- '\n</tool_response>' }}
+        {%- if loop.last or (messages[loop.index0 + 1].role != "tool") %}
+            {{- '<|im_end|>\n' }}
+        {%- endif %}
+    {%- endif %}
+{%- endfor %}
+{%- if add_generation_prompt %}
+    {{- '<|im_start|>assistant\n' }}
+    {%- if enable_thinking is defined and enable_thinking is false %}
+        {{- '<think>\n\n</think>\n\n' }}
+    {%- endif %}
+{%- endif %}

config.json

@@ -0,0 +1,69 @@
+{
+  "architectures": [
+    "Qwen3ForCausalLM"
+  ],
+  "attention_bias": false,
+  "attention_dropout": 0.0,
+  "bos_token_id": 151643,
+  "dtype": "bfloat16",
+  "eos_token_id": 151645,
+  "head_dim": 128,
+  "hidden_act": "silu",
+  "hidden_size": 2560,
+  "initializer_range": 0.02,
+  "intermediate_size": 9728,
+  "layer_types": [
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention"
+  ],
+  "max_position_embeddings": 40960,
+  "max_window_layers": 36,
+  "model_type": "qwen3",
+  "num_attention_heads": 32,
+  "num_hidden_layers": 36,
+  "num_key_value_heads": 8,
+  "rms_norm_eps": 1e-06,
+  "rope_scaling": null,
+  "rope_theta": 1000000,
+  "sliding_window": null,
+  "tie_word_embeddings": true,
+  "transformers_version": "4.57.1",
+  "use_cache": true,
+  "use_sliding_window": false,
+  "vocab_size": 151936,
+  "torch_dtype": "bfloat16"
+}

config_sentence_transformers.json

@@ -0,0 +1,10 @@
+{
+  "model_type": "SentenceTransformer",
+  "prompts": {
+    "query": "<|im_start|>system\nquery<|im_end|>\n<|im_start|>user\n",
+    "document": "<|im_start|>system\ndocument<|im_end|>\n<|im_start|>user\n"
+  },
+  "suffix": "<|im_end|>\n",
+  "default_prompt_name": "document",
+  "similarity_fn_name": "cosine"
+}

generation_config.json

@@ -0,0 +1,13 @@
+{
+  "bos_token_id": 151643,
+  "do_sample": true,
+  "eos_token_id": [
+    151645,
+    151643
+  ],
+  "pad_token_id": 151643,
+  "temperature": 0.6,
+  "top_k": 20,
+  "top_p": 0.95,
+  "transformers_version": "4.57.1"
+}

infinity-preparation.json

@@ -0,0 +1,14 @@
+{
+  "source": "zeroentropy/zembed-1-embedding",
+  "revision": "cf13c81f3274394053d166740294f7eea4586f7a",
+  "changes": {
+    "modules.json": "Replace ZembedTransformer with standard Transformer; client supplies original terminal suffix."
+  },
+  "required_input_suffix": "<|im_end|>\n",
+  "output_dimensions": 2560,
+  "weight_sha256": {
+    "model-00001-of-00002.safetensors": "630bc6ead0804903296b264bf536b0859702605cfbb89e58e2d2887ccce9ee6c",
+    "model-00002-of-00002.safetensors": "6cb060593bbad76139ac9ae8614a7711406225e464254fc453b7ae1787b56325",
+    "projections.safetensors": "c2857f09a857d564c78224cdc7baa763773fa96475dd7b9b29d598756b61d083"
+  }
+}

infinity_client.py

@@ -0,0 +1,29 @@
+"""Supply zembed's saved prompt and terminal suffix to Infinity's /embed API."""
+import json
+import urllib.error
+import urllib.request
+
+
+def embed(texts, kind="document", base_url="http://localhost:7997", model="michaelfeil/zembed-1-infinity"):
+    if isinstance(texts, str):
+        texts = [texts]
+    texts = list(texts)
+    if kind not in ("query", "document") or not texts or any(not isinstance(text, str) for text in texts):
+        raise ValueError("Provide text strings and kind='query' or kind='document'")
+    body = {
+        "model": model,
+        "inputs": [text + "<|im_end|>\n" for text in texts],
+        "prompt_name": kind,
+        "normalize": True,
+        "truncate": False,
+    }
+    request = urllib.request.Request(
+        base_url.rstrip("/") + "/embed",
+        data=json.dumps(body).encode(),
+        headers={"Content-Type": "application/json"},
+    )
+    try:
+        with urllib.request.urlopen(request, timeout=300) as response:
+            return json.load(response)
+    except urllib.error.HTTPError as error:
+        raise RuntimeError(f"HTTP {error.code}: {error.read().decode()}") from error

model-00001-of-00002.safetensors

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model-00002-of-00002.safetensors

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model.safetensors.index.json

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+    "model.norm.weight": "model-00002-of-00002.safetensors"
+  }
+}

modeling_zembed.py

@@ -0,0 +1,20 @@
+import torch
+from sentence_transformers.models import Transformer
+
+# pyright: basic
+
+
+class ZembedTransformer(Transformer):
+    def tokenize(
+        self,
+        texts: list[str] | list[dict] | list[tuple[str, str]],
+        padding: str | bool = True,
+    ) -> dict[str, torch.Tensor]:
+        texts = [text + "<|im_end|>\n" for text in texts]  # pyright: ignore[reportOperatorIssue]
+        return self.tokenizer(
+            texts,
+            padding=padding,
+            truncation="longest_first",
+            return_tensors="pt",
+            max_length=self.max_seq_length,
+        )

modules.json

@@ -0,0 +1,20 @@
+[
+  {
+    "idx": 0,
+    "name": "0",
+    "path": "",
+    "type": "sentence_transformers.models.Transformer"
+  },
+  {
+    "idx": 1,
+    "name": "1",
+    "path": "1_Pooling",
+    "type": "sentence_transformers.models.Pooling"
+  },
+  {
+    "idx": 2,
+    "name": "2",
+    "path": "2_Normalize",
+    "type": "sentence_transformers.models.Normalize"
+  }
+]

projections.safetensors

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+version https://git-lfs.github.com/spec/v1
+oid sha256:c2857f09a857d564c78224cdc7baa763773fa96475dd7b9b29d598756b61d083
+size 17472456

sentence_bert_config.json

@@ -0,0 +1,4 @@
+{
+  "max_seq_length": 32768,
+  "do_lower_case": false
+}

special_tokens_map.json

@@ -0,0 +1,31 @@
+{
+  "additional_special_tokens": [
+    "<|im_start|>",
+    "<|im_end|>",
+    "<|object_ref_start|>",
+    "<|object_ref_end|>",
+    "<|box_start|>",
+    "<|box_end|>",
+    "<|quad_start|>",
+    "<|quad_end|>",
+    "<|vision_start|>",
+    "<|vision_end|>",
+    "<|vision_pad|>",
+    "<|image_pad|>",
+    "<|video_pad|>"
+  ],
+  "eos_token": {
+    "content": "<|im_end|>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "pad_token": {
+    "content": "<|endoftext|>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  }
+}

tokenizer.json

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+version https://git-lfs.github.com/spec/v1
+oid sha256:aeb13307a71acd8fe81861d94ad54ab689df773318809eed3cbe794b4492dae4
+size 11422654

tokenizer_config.json

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+  "add_prefix_space": false,
+  "added_tokens_decoder": {
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