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@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+tokenizer.json 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
+}

2_Normalize/config.json

@@ -0,0 +1,3 @@
+{
+  "normalize_embeddings": true
+}

README.md

@@ -0,0 +1,216 @@
+---
+language:
+- en
+- zh
+- multilingual
+license: apache-2.0
+library_name: sentence-transformers
+tags:
+- sentence-transformers
+- sentence-similarity
+- feature-extraction
+- embedding
+- text-embedding
+- retrieval
+- quantization
+- int8
+pipeline_tag: sentence-similarity
+base_model: Qwen/Qwen3-Embedding-4B
+---
+
+# Octen-Embedding-4B-INT8
+
+Octen-Embedding-4B-INT8 is a text embedding model developed by [Octen](https://octen.ai/) for semantic search and retrieval tasks. This model is fine-tuned from [Qwen/Qwen3-Embedding-4B](https://huggingface.co/Qwen/Qwen3-Embedding-4B) and supports multiple languages, providing high-quality embeddings for various applications.
+
+**Quantization**: This is an INT8 quantized version using bitsandbytes. INT8 quantization significantly reduces memory footprint (~50% smaller), making it suitable for deployment on resource-constrained environments. Note that while memory usage is reduced, inference speed may not necessarily improve and could be slightly slower than the BF16 version on some hardware.
+
+## Key Highlights
+
+### 🥇 RTEB Leaderboard Champion (as of January 12, 2026)
+- **Octen-Embedding-8B ranks #1 on the [RTEB Leaderboard](https://huggingface.co/spaces/mteb/leaderboard)** with Mean (Task) score of **0.8045**
+- Excellent performance on both Public (0.7953) and Private (0.8157) datasets
+- Demonstrates true generalization capability without overfitting to public benchmarks
+
+### Industry-Oriented Vertical Domain Expertise
+- **Legal**: Legal document retrieval
+- **Finance**: Financial reports, Q&A, and personal finance content
+- **Healthcare**: Medical Q&A, clinical dialogues, and health consultations
+- **Code**: Programming problems, code search, and SQL queries
+
+### Ultra-Long Context Support
+- Supports up to **32,768 tokens** context length
+- Suitable for processing long documents in legal, healthcare, and other domains
+- High-dimensional embedding space for rich semantic representation
+
+### Multilingual Capability
+- Supports **100+ languages**
+- Includes various programming languages
+- Strong multilingual, cross-lingual, and code retrieval capabilities
+
+---
+
+## Open Source Model List
+
+| Model Type | Model | Size | Max Tokens | Embedding Dimensions | HuggingFace Link |
+|------------|-------|------|------------|---------------------|------------------|
+| Text Embedding | [Octen-Embedding-0.6B](https://huggingface.co/Octen/Octen-Embedding-0.6B) | 0.6B | 32,768 | 1024 | ✅ Available |
+| Text Embedding | [Octen-Embedding-4B](https://huggingface.co/Octen/Octen-Embedding-4B) | 4.0B | 32,768 | 2560 | ✅ Available |
+| Text Embedding | [Octen-Embedding-8B](https://huggingface.co/Octen/Octen-Embedding-8B) | 7.6B | 32,768 | 4096 | ✅ Available |
+
+**Model Family Design**:
+- **Octen-Embedding-8B**: Best performance, RTEB #1, for high-precision retrieval
+- **Octen-Embedding-4B**: Best in 4B category, balanced performance and efficiency
+- **Octen-Embedding-0.6B**: Lightweight deployment, suitable for edge devices and resource-constrained environments
+
+For API access, deployment solutions, and technical documentation, visit [octen.ai](https://octen.ai/).
+
+---
+
+## Experimental Results
+
+### RTEB Leaderboard (Overall Performance)
+
+| Model | Embedding Dim | Max Tokens | Mean (Public) | Mean (Private) | Mean (Task) |
+|-------|---------------|------------|---------------|----------------|-------------|
+| **Octen-Embedding-8B** | **4096** | **32768** | **0.7953** | **0.8157** | **0.8045** |
+| voyage-3-large | 1024 | 32000 | 0.7434 | 0.8277 | 0.7812 |
+| gemini-embedding-001 | 3072 | 2048 | 0.7218 | 0.8075 | 0.7602 |
+| **Octen-Embedding-4B** | **2560** | **32768** | **0.7747** | **0.7942** | **0.7834** |
+| MoD-Embedding | 2560 | 32768 | 0.7642 | 0.7900 | 0.7758 |
+| Qwen3-Embedding-8B | 4096 | 32768 | 0.7310 | 0.7838 | 0.7547 |
+| **Octen-Embedding-0.6B** | **1024** | **32768** | **0.7241** | **-** | **-** |
+| voyage-3.5 | 1024 | 32000 | 0.7139 | 0.8102 | 0.7571 |
+| Cohere-embed-v4.0 | 1536 | 128000 | 0.6534 | 0.7943 | 0.7166 |
+| jina-embeddings-v4 | 2048 | 32768 | 0.6652 | 0.7664 | 0.7105 |
+| GritLM-7B | 4096 | 32768 | 0.6187 | 0.7385 | 0.6724 |
+| text-embedding-3-large | 3072 | 8191 | 0.6110 | 0.7130 | 0.6567 |
+| e5-mistral-7b-instruct | 4096 | 32768 | 0.5090 | 0.7091 | 0.5987 |
+| NV-Embed-v2 | 4096 | 32768 | 0.5805 | 0.6691 | 0.6203 |
+| snowflake-arctic-embed-l-v2.0 | 1024 | 8192 | 0.5395 | 0.7079 | 0.6150 |
+| multilingual-e5-large-instruct | 1024 | 514 | 0.5478 | 0.6859 | 0.6097 |
+| gte-multilingual-base | 768 | 8192 | 0.5291 | 0.6697 | 0.5921 |
+| text-embedding-3-small | 1536 | 8191 | 0.5260 | 0.6630 | 0.5874 |
+| bge-m3 | 1024 | 8194 | 0.5216 | 0.6726 | 0.5893 |
+| Qwen3-Embedding-4B | 2560 | 32768 | - | 0.7711 | - |
+| Qwen3-Embedding-0.6B | 1024 | 32768 | - | 0.7117 | - |
+
+---
+
+## Model Details
+
+- **Base Model**: [Qwen/Qwen3-Embedding-4B](https://huggingface.co/Qwen/Qwen3-Embedding-4B)
+- **Model Size**: 4B parameters (INT8 quantized)
+- **Max Sequence Length**: 40,960 tokens
+- **Embedding Dimension**: 2560
+- **Languages**: English, Chinese, and multilingual support
+- **Training Method**: LoRA fine-tuning
+- **Quantization**: INT8 (bitsandbytes)
+- **Memory Footprint**: ~4GB (vs ~8GB for BF16 version)
+
+## Usage
+
+### Using Sentence Transformers
+
+```python
+from sentence_transformers import SentenceTransformer
+
+model = SentenceTransformer("Octen/Octen-Embedding-4B-INT8")
+
+# Encode sentences
+sentences = [
+    "This is an example sentence",
+    "Each sentence is converted to a vector"
+]
+
+embeddings = model.encode(sentences)
+print(embeddings.shape)
+# Output: (2, 2560)
+
+# Compute similarity
+from sentence_transformers.util import cos_sim
+similarity = cos_sim(embeddings[0], embeddings[1])
+print(f"Similarity: {similarity.item():.4f}")
+```
+
+### Using Transformers
+
+```python
+from transformers import AutoModel, AutoTokenizer
+import torch
+import torch.nn.functional as F
+
+tokenizer = AutoTokenizer.from_pretrained("Octen/Octen-Embedding-4B-INT8", padding_side="left")
+model = AutoModel.from_pretrained("Octen/Octen-Embedding-4B-INT8")
+model.eval()
+
+def encode(texts):
+    inputs = tokenizer(texts, padding=True, truncation=True,
+                      max_length=8192, return_tensors="pt")
+
+    with torch.no_grad():
+        outputs = model(**inputs)
+        # Use last token embedding
+        embeddings = outputs.last_hidden_state[:, -1, :]
+        # Normalize embeddings
+        embeddings = F.normalize(embeddings, p=2, dim=1)
+
+    return embeddings
+
+# Example usage
+texts = ["Hello world", "你好世界"]
+embeddings = encode(texts)
+similarity = torch.matmul(embeddings[0], embeddings[1])
+print(f"Similarity: {similarity.item():.4f}")
+```
+
+## Recommended Use Cases
+
+- Semantic search and information retrieval
+- Document similarity and clustering
+- Question answering
+- Cross-lingual retrieval
+- Text classification with embeddings
+- Deployment on GPU-constrained environments
+
+## Known Issues
+
+When encoding documents without any instruction prefix, you may encounter unexpected behavior due to an [upstream issue in Qwen3-Embedding](https://huggingface.co/Qwen/Qwen3-Embedding-8B/discussions/21). To avoid this issue, we recommend adding `"- "` (dash followed by space) at the beginning of your text when encoding documents:
+
+```python
+# Recommended: Add "- " prefix for document encoding
+documents = ["- " + doc for doc in documents]
+embeddings = model.encode(documents)
+```
+
+This workaround ensures consistent and expected embedding behavior.
+
+## Limitations
+
+- Performance may vary across different domains and languages
+- Very long documents (>40K tokens) require truncation
+- Optimized for retrieval tasks, not for text generation
+- INT8 quantization may introduce minor accuracy degradation compared to BF16 version
+- Inference speed may not improve despite reduced memory usage
+
+## License
+
+This model is licensed under the [Apache License 2.0](https://www.apache.org/licenses/LICENSE-2.0).
+
+This model is derived from [Qwen/Qwen3-Embedding-4B](https://huggingface.co/Qwen/Qwen3-Embedding-4B), which is also licensed under Apache License 2.0.
+
+## Paper
+
+For more details, please refer to our blog post: [Octen Series: Optimizing Embedding Models to #1 on RTEB Leaderboard](https://octen-team.github.io/octen_blog/posts/octen-rteb-first-place/)
+
+## Citation
+
+If you find our work helpful, please consider citing:
+
+```bibtex
+@misc{octen2025rteb,
+  title={Octen Series: Optimizing Embedding Models to #1 on RTEB Leaderboard},
+  author={Octen Team},
+  year={2025},
+  url={https://octen-team.github.io/octen_blog/posts/octen-rteb-first-place/}
+}
+```

added_tokens.json

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chat_template.jinja

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+{%- if tools %}
+    {{- '<|im_start|>system\n' }}
+    {%- if messages[0]['role'] == 'system' %}
+        {{- messages[0]['content'] }}
+    {%- else %}
+        {{- 'You are a helpful assistant.' }}
+    {%- endif %}
+    {{- "\n\n# 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' }}
+    {%- else %}
+        {{- '<|im_start|>system\nYou are a helpful assistant.<|im_end|>\n' }}
+    {%- endif %}
+{%- endif %}
+{%- for message in messages %}
+    {%- if (message.role == "user") or (message.role == "system" and not loop.first) or (message.role == "assistant" and not message.tool_calls) %}
+        {{- '<|im_start|>' + message.role + '\n' + message.content + '<|im_end|>' + '\n' }}
+    {%- elif message.role == "assistant" %}
+        {{- '<|im_start|>' + message.role }}
+        {%- if message.content %}
+            {{- '\n' + message.content }}
+        {%- endif %}
+        {%- for tool_call in message.tool_calls %}
+            {%- if tool_call.function is defined %}
+                {%- set tool_call = tool_call.function %}
+            {%- endif %}
+            {{- '\n<tool_call>\n{"name": "' }}
+            {{- tool_call.name }}
+            {{- '", "arguments": ' }}
+            {{- tool_call.arguments | tojson }}
+            {{- '}\n</tool_call>' }}
+        {%- endfor %}
+        {{- '<|im_end|>\n' }}
+    {%- elif message.role == "tool" %}
+        {%- if (loop.index0 == 0) or (messages[loop.index0 - 1].role != "tool") %}
+            {{- '<|im_start|>user' }}
+        {%- endif %}
+        {{- '\n<tool_response>\n' }}
+        {{- message.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' }}
+{%- endif %}

config.json

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+{
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+    "Qwen3Model"
+  ],
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+  "transformers_version": "4.57.1",
+  "use_cache": true,
+  "use_sliding_window": false,
+  "vocab_size": 151665
+}

config_sentence_transformers.json

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+{
+  "prompts": {
+    "query": "Instruct: Given a web search query, retrieve relevant passages that answer the query\nQuery:",
+    "document": " "
+  },
+  "default_prompt_name": null,
+  "similarity_fn_name": "cosine"
+}

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modules.json

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special_tokens_map.json

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