Update README.md

README.md

@@ -1,292 +1,307 @@
 ---
 license: apache-2.0
 base_model:
-- Qwen/Qwen3-0.6B-Base
+- Qwen/Qwen3-Embedding-0.6B
+pipeline_tag: sentence-similarity
+library_name: sentence-transformers
 tags:
 - transformers
 - sentence-transformers
-- sentence-similarity
+- text-embeddings
 - feature-extraction
-- text-embeddings-inference
+- retrieval
+- rag
+- multilingual
+- semantic-search
+- qwen3
+- alfotech
 ---
-# Qwen3-Embedding-0.6B
+
+# Alfo Embedding 0.6B
+
+<p align="center">
+  <img src="https://huggingface.co/alfotech/alfo-tech-logo/resolve/main/logo.png" width="220"/>
+</p>
 
 <p align="center">
-    <img src="https://qianwen-res.oss-accelerate-overseas.aliyuncs.com/logo_qwen3.png" width="400"/>
-<p>
+
+**Production-oriented multilingual embedding model by Alfo Tech Industries**
+
+</p>
+
+---
 
 ## Highlights
 
-The Qwen3 Embedding model series is the latest proprietary model of the Qwen family, specifically designed for text embedding and ranking tasks. Building upon the dense foundational models of the Qwen3 series, it provides a comprehensive range of text embeddings and reranking models in various sizes (0.6B, 4B, and 8B). This series inherits the exceptional multilingual capabilities, long-text understanding, and reasoning skills of its foundational model. The Qwen3 Embedding series represents significant advancements in multiple text embedding and ranking tasks, including text retrieval, code retrieval, text classification, text clustering, and bitext mining.
+Alfo Embedding 0.6B is a production-focused embedding model built on **Qwen3-Embedding-0.6B**, optimized through an evaluation-first retrieval pipeline.
+
+Instead of training from scratch, this project focuses on improving real-world retrieval quality using:
+
+- Hard retrieval evaluation
+- Semantic hard-negative analysis
+- Contrastive embedding fine-tuning
+- Retrieval-oriented optimization
+- Production deployment validation
 
-**Exceptional Versatility**: The embedding model has achieved state-of-the-art performance across a wide range of downstream application evaluations. The 8B size embedding model ranks **No.1** in the MTEB multilingual leaderboard (as of June 5, 2025, score **70.58**), while the reranking model excels in various text retrieval scenarios.
+The objective is simple:
 
-**Comprehensive Flexibility**: The Qwen3 Embedding series offers a full spectrum of sizes (from 0.6B to 8B) for both embedding and reranking models, catering to diverse use cases that prioritize efficiency and effectiveness. Developers can seamlessly combine these two modules. Additionally, the embedding model allows for flexible vector definitions across all dimensions, and both embedding and reranking models support user-defined instructions to enhance performance for specific tasks, languages, or scenarios.
+> Build practical AI infrastructure for semantic search and Retrieval-Augmented Generation.
 
-**Multilingual Capability**: The Qwen3 Embedding series offer support for over 100 languages, thanks to the multilingual capabilites of Qwen3 models. This includes various programming languages, and provides robust multilingual, cross-lingual, and code retrieval capabilities.
+---
 
 ## Model Overview
 
-**Qwen3-Embedding-0.6B** has the following features:
+| Property | Value |
+|----------|-------|
+| Model Type | Text Embedding |
+| Base Model | Qwen3-Embedding-0.6B |
+| Parameters | 0.6B |
+| Context Length | 32K |
+| Default Embedding Dimension | 1024 |
+| Supported Dimensions | 32–1024 |
+| Languages | 100+ |
+| Instruction Aware | Yes |
 
-- Model Type: Text Embedding
-- Supported Languages: 100+ Languages
-- Number of Parameters: 0.6B
-- Context Length: 32k
-- Embedding Dimension: Up to 1024, supports user-defined output dimensions ranging from 32 to 1024
+The upstream Qwen3 embedding architecture provides multilingual understanding, long-context retrieval, configurable embedding dimensions, and instruction-aware query encoding. This project extends that foundation with a retrieval-focused development workflow.
 
-For more details, including benchmark evaluation, hardware requirements, and inference performance, please refer to our [blog](https://qwenlm.github.io/blog/qwen3-embedding/), [GitHub](https://github.com/QwenLM/Qwen3-Embedding).
+---
 
-## Qwen3 Embedding Series Model list
+## Development Philosophy
+
+Rather than optimizing only for easy benchmarks, the training pipeline intentionally focuses on difficult retrieval scenarios.
+
+Pipeline:
+
+```text
+Baseline
+   ↓
+Hard Benchmark
+   ↓
+Failure Analysis
+   ↓
+Hard Negative Mining
+   ↓
+Fine-Tuning
+   ↓
+Validation
+   ↓
+Production Testing
+```
 
-| Model Type       | Models               | Size | Layers | Sequence Length | Embedding Dimension | MRL Support | Instruction Aware |
-|------------------|----------------------|------|--------|-----------------|---------------------|-------------|----------------|
-| Text Embedding   | [Qwen3-Embedding-0.6B](https://huggingface.co/Qwen/Qwen3-Embedding-0.6B) | 0.6B | 28     | 32K             | 1024                | Yes         | Yes            |
-| Text Embedding   | [Qwen3-Embedding-4B](https://huggingface.co/Qwen/Qwen3-Embedding-4B)   | 4B   | 36     | 32K             | 2560                | Yes         | Yes            |
-| Text Embedding   | [Qwen3-Embedding-8B](https://huggingface.co/Qwen/Qwen3-Embedding-8B)   | 8B   | 36     | 32K             | 4096                | Yes         | Yes            |
-| Text Reranking   | [Qwen3-Reranker-0.6B](https://huggingface.co/Qwen/Qwen3-Reranker-0.6B) | 0.6B | 28     | 32K             | -                   | -           | Yes            |
-| Text Reranking   | [Qwen3-Reranker-4B](https://huggingface.co/Qwen/Qwen3-Reranker-4B)   | 4B   | 36     | 32K             | -                   | -           | Yes            |
-| Text Reranking   | [Qwen3-Reranker-8B](https://huggingface.co/Qwen/Qwen3-Reranker-8B)   | 8B   | 36     | 32K             | -                   | -           | Yes            |
+Every optimization is driven by measured evaluation rather than intuition.
 
-> **Note**:
-> - `MRL Support` indicates whether the embedding model supports custom dimensions for the final embedding. 
-> - `Instruction Aware` notes whether the embedding or reranking model supports customizing the input instruction according to different tasks.
-> - Our evaluation indicates that, for most downstream tasks, using instructions (instruct) typically yields an improvement of 1% to 5% compared to not using them. Therefore, we recommend that developers create tailored instructions specific to their tasks and scenarios. In multilingual contexts, we also advise users to write their instructions in English, as most instructions utilized during the model training process were originally written in English.
+---
 
-## Usage
+## Training Pipeline
 
-With Transformers versions earlier than 4.51.0, you may encounter the following error:
-```
-KeyError: 'qwen3'
-```
+Development was performed inside **Google Colab** using dual Tesla T4 GPUs.
 
-### Sentence Transformers Usage
+### Pipeline Components
 
-```python
-# Requires transformers>=4.51.0
-# Requires sentence-transformers>=2.7.0
+- Retrieval dataset preparation
+- Duplicate removal
+- Quality filtering
+- Validation leakage checks
+- Hard-negative mining
+- Multiple Negatives Ranking Loss
+- In-batch negatives
+- Continuous retrieval evaluation
+- Production validation
 
-from sentence_transformers import SentenceTransformer
+---
 
-# Load the model
-model = SentenceTransformer("Qwen/Qwen3-Embedding-0.6B")
-
-# We recommend enabling flash_attention_2 for better acceleration and memory saving,
-# together with setting `padding_side` to "left":
-# model = SentenceTransformer(
-#     "Qwen/Qwen3-Embedding-0.6B",
-#     model_kwargs={"attn_implementation": "flash_attention_2", "device_map": "auto"},
-#     tokenizer_kwargs={"padding_side": "left"},
-# )
-
-# The queries and documents to embed
-queries = [
-    "What is the capital of China?",
-    "Explain gravity",
-]
-documents = [
-    "The capital of China is Beijing.",
-    "Gravity is a force that attracts two bodies towards each other. It gives weight to physical objects and is responsible for the movement of planets around the sun.",
-]
-
-# Encode the queries and documents. Note that queries benefit from using a prompt
-# Here we use the prompt called "query" stored under `model.prompts`, but you can
-# also pass your own prompt via the `prompt` argument
-query_embeddings = model.encode(queries, prompt_name="query")
-document_embeddings = model.encode(documents)
-
-# Compute the (cosine) similarity between the query and document embeddings
-similarity = model.similarity(query_embeddings, document_embeddings)
-print(similarity)
-# tensor([[0.7646, 0.1414],
-#         [0.1355, 0.6000]])
-```
+## Evaluation Strategy
+
+The model is evaluated using both easy and difficult retrieval settings.
+
+### Easy Benchmark
+
+| Metric | Score |
+|--------|-------|
+| Recall@1 | **0.967** |
+| Recall@5 | **0.999** |
+| Recall@10 | **1.000** |
+| MRR@10 | **0.9815** |
 
-### Transformers Usage
+This benchmark validates the retrieval pipeline under standard retrieval conditions.
+
+---
+
+### Hard Retrieval Benchmark
+
+Corpus size:
+
+**72,635 documents**
+
+Evaluation queries:
+
+**1,000**
+
+| Metric | Score |
+|--------|-------|
+| Recall@1 | **0.614** |
+| Recall@5 | **0.883** |
+| Recall@10 | **0.929** |
+| MRR@10 | **0.7268** |
+
+This benchmark introduces semantically similar distractors, making it significantly more representative of real-world retrieval systems.
+
+---
+
+## Failure Analysis
+
+Hard benchmark analysis identified semantic discrimination as the primary remaining challenge.
+
+Summary:
+
+- Semantic hard negatives: **386**
+- Positive similarity: **0.689**
+- Top negative similarity: **0.653**
+- Average margin: **0.036**
+- Duplicate contamination detected: **0**
+- Noisy positives detected: **0**
+
+These observations guided the retrieval-focused optimization process.
+
+---
+
+## Recommended Use Cases
+
+- Semantic Search
+- RAG
+- Enterprise Search
+- Knowledge Base Retrieval
+- FAQ Matching
+- Code Search
+- Similarity Search
+- Vector Databases
+- Multilingual Retrieval
+
+---
+
+## Usage
+
+### Sentence Transformers
 
 ```python
-# Requires transformers>=4.51.0
-
-import torch
-import torch.nn.functional as F
-
-from torch import Tensor
-from transformers import AutoTokenizer, AutoModel
-
-
-def last_token_pool(last_hidden_states: Tensor,
-                 attention_mask: Tensor) -> Tensor:
-    left_padding = (attention_mask[:, -1].sum() == attention_mask.shape[0])
-    if left_padding:
-        return last_hidden_states[:, -1]
-    else:
-        sequence_lengths = attention_mask.sum(dim=1) - 1
-        batch_size = last_hidden_states.shape[0]
-        return last_hidden_states[torch.arange(batch_size, device=last_hidden_states.device), sequence_lengths]
-
-
-def get_detailed_instruct(task_description: str, query: str) -> str:
-    return f'Instruct: {task_description}\nQuery:{query}'
-
-# Each query must come with a one-sentence instruction that describes the task
-task = 'Given a web search query, retrieve relevant passages that answer the query'
-
-queries = [
-    get_detailed_instruct(task, 'What is the capital of China?'),
-    get_detailed_instruct(task, 'Explain gravity')
-]
-# No need to add instruction for retrieval documents
-documents = [
-    "The capital of China is Beijing.",
-    "Gravity is a force that attracts two bodies towards each other. It gives weight to physical objects and is responsible for the movement of planets around the sun."
-]
-input_texts = queries + documents
-
-tokenizer = AutoTokenizer.from_pretrained('Qwen/Qwen3-Embedding-0.6B', padding_side='left')
-model = AutoModel.from_pretrained('Qwen/Qwen3-Embedding-0.6B')
-
-# We recommend enabling flash_attention_2 for better acceleration and memory saving.
-# model = AutoModel.from_pretrained('Qwen/Qwen3-Embedding-0.6B', attn_implementation="flash_attention_2", torch_dtype=torch.float16).cuda()
-
-max_length = 8192
-
-# Tokenize the input texts
-batch_dict = tokenizer(
-    input_texts,
-    padding=True,
-    truncation=True,
-    max_length=max_length,
-    return_tensors="pt",
+from sentence_transformers import SentenceTransformer
+
+model = SentenceTransformer("alfotech/alfo-embedding-0.6b")
+
+embeddings = model.encode(
+    [
+        "Artificial intelligence improves search.",
+        "Vector embeddings represent semantic meaning."
+    ],
+    normalize_embeddings=True
 )
-batch_dict.to(model.device)
-outputs = model(**batch_dict)
-embeddings = last_token_pool(outputs.last_hidden_state, batch_dict['attention_mask'])
-
-# normalize embeddings
-embeddings = F.normalize(embeddings, p=2, dim=1)
-scores = (embeddings[:2] @ embeddings[2:].T)
-print(scores.tolist())
-# [[0.7645568251609802, 0.14142508804798126], [0.13549736142158508, 0.5999549627304077]]
 ```
 
-### vLLM Usage
+### Retrieval Queries
 
-```python
-# Requires vllm>=0.8.5
-import torch
-import vllm
-from vllm import LLM
-
-def get_detailed_instruct(task_description: str, query: str) -> str:
-    return f'Instruct: {task_description}\nQuery:{query}'
-
-# Each query must come with a one-sentence instruction that describes the task
-task = 'Given a web search query, retrieve relevant passages that answer the query'
-
-queries = [
-    get_detailed_instruct(task, 'What is the capital of China?'),
-    get_detailed_instruct(task, 'Explain gravity')
-]
-# No need to add instruction for retrieval documents
-documents = [
-    "The capital of China is Beijing.",
-    "Gravity is a force that attracts two bodies towards each other. It gives weight to physical objects and is responsible for the movement of planets around the sun."
-]
-input_texts = queries + documents
-
-model = LLM(model="Qwen/Qwen3-Embedding-0.6B", task="embed")
-
-outputs = model.embed(input_texts)
-embeddings = torch.tensor([o.outputs.embedding for o in outputs])
-scores = (embeddings[:2] @ embeddings[2:].T)
-print(scores.tolist())
-# [[0.7620252966880798, 0.14078938961029053], [0.1358368694782257, 0.6013815999031067]]
+The underlying Qwen3 embedding family supports instruction-aware retrieval.
+
+Example:
+
+```text
+Instruct: Given a web search query, retrieve relevant passages that answer the query.
+
+Query: How can I optimize transformer inference?
 ```
 
-📌 **Tip**: We recommend that developers customize the `instruct` according to their specific scenarios, tasks, and languages. Our tests have shown that in most retrieval scenarios, not using an `instruct` on the query side can lead to a drop in retrieval performance by approximately 1% to 5%.
+Using retrieval instructions can improve search performance for many production workloads.
 
-### Text Embeddings Inference (TEI) Usage
+---
 
-You can either run / deploy TEI on NVIDIA GPUs as:
+## Production Recommendations
 
-```bash
-docker run --gpus all -p 8080:80 -v hf_cache:/data --pull always ghcr.io/huggingface/text-embeddings-inference:cpu-1.7.2 --model-id Qwen/Qwen3-Embedding-0.6B --dtype float16
-```
+Recommended starting configuration.
 
-Or on CPU devices as:
+| Setting | Value |
+|---------|-------|
+| Sequence Length | 512 |
+| Batch Size | 32 |
+| Normalize Embeddings | Enabled |
+| Similarity | Cosine |
 
-```bash
-docker run -p 8080:80 -v hf_cache:/data --pull always ghcr.io/huggingface/text-embeddings-inference:1.7.2 --model-id Qwen/Qwen3-Embedding-0.6B
-```
+These settings provide a practical balance between retrieval quality and inference throughput.
 
-And then, generate the embeddings sending a HTTP POST request as:
+---
 
-```bash
-curl http://localhost:8080/embed \
-    -X POST \
-    -d '{"inputs": ["Instruct: Given a web search query, retrieve relevant passages that answer the query\nQuery: What is the capital of China?", "Instruct: Given a web search query, retrieve relevant passages that answer the query\nQuery: Explain gravity"]}' \
-    -H "Content-Type: application/json"
-```
+## Roadmap
+
+Planned improvements include:
+
+- Larger curated retrieval datasets
+- Better hard-negative refinement
+- Domain-specific adaptation
+- Full blind evaluation
+- Extended benchmark coverage
+- Additional deployment optimizations
+
+---
 
-## Evaluation
-
-### MTEB (Multilingual)
-
-| Model                            |  Size   |  Mean (Task)  | Mean (Type) | Bitxt Mining | Class. | Clust. | Inst. Retri. | Multi. Class. | Pair. Class. | Rerank | Retri. | STS  |
-|----------------------------------|:-------:|:-------------:|:-------------:|:--------------:|:--------:|:--------:|:--------------:|:---------------:|:--------------:|:--------:|:--------:|:------:|
-| NV-Embed-v2                      |   7B    |     56.29     | 49.58       | 57.84        | 57.29  | 40.80  | 1.04         | 18.63         | 78.94        | 63.82  | 56.72  | 71.10|
-| GritLM-7B                        |   7B    |     60.92     | 53.74       | 70.53        | 61.83  | 49.75  | 3.45         | 22.77         | 79.94        | 63.78  | 58.31  | 73.33|
-| BGE-M3                           |  0.6B   |     59.56     | 52.18       | 79.11        | 60.35  | 40.88  | -3.11        | 20.1          | 80.76        | 62.79  | 54.60  | 74.12|
-| multilingual-e5-large-instruct   |  0.6B   |     63.22     | 55.08       | 80.13        | 64.94  | 50.75  | -0.40        | 22.91         | 80.86        | 62.61  | 57.12  | 76.81|
-| gte-Qwen2-1.5B-instruct          |  1.5B   |     59.45     | 52.69       | 62.51        | 58.32  | 52.05  | 0.74         | 24.02         | 81.58        | 62.58  | 60.78  | 71.61|
-| gte-Qwen2-7b-Instruct            |   7B    |     62.51     | 55.93       | 73.92        | 61.55  | 52.77  | 4.94         | 25.48         | 85.13        | 65.55  | 60.08  | 73.98|
-| text-embedding-3-large           |    -    |     58.93     | 51.41       | 62.17        | 60.27  | 46.89  | -2.68        | 22.03         | 79.17        | 63.89  | 59.27  | 71.68|
-| Cohere-embed-multilingual-v3.0   |    -    |     61.12     | 53.23       | 70.50        | 62.95  | 46.89  | -1.89        | 22.74         | 79.88        | 64.07  | 59.16  | 74.80|
-| Gemini Embedding                 |    -    |     68.37     | 59.59       | 79.28        | 71.82  | 54.59  | 5.18         | **29.16**     | 83.63        | 65.58  | 67.71  | 79.40|
-| **Qwen3-Embedding-0.6B**         |  0.6B   |     64.33     | 56.00       | 72.22        | 66.83  | 52.33  | 5.09         | 24.59         | 80.83        | 61.41  | 64.64  | 76.17|
-| **Qwen3-Embedding-4B**           |   4B    |     69.45     | 60.86       | 79.36        | 72.33  | 57.15  | **11.56**    | 26.77         | 85.05        | 65.08  | 69.60  | 80.86|
-| **Qwen3-Embedding-8B**           |   8B    |   **70.58**   | **61.69**   | **80.89**    | **74.00** | **57.65** | 10.06      | 28.66         | **86.40**    | **65.63** | **70.88** | **81.08** |
-
-> **Note**: For compared models, the scores are retrieved from MTEB online [leaderboard](https://huggingface.co/spaces/mteb/leaderboard) on May 24th, 2025.
-
-### MTEB (Eng v2)
-
-| MTEB English / Models          |  Param.  | Mean(Task) | Mean(Type) | Class. | Clust. | Pair Class. | Rerank. | Retri. | STS   | Summ. |
-|--------------------------------|:--------:|:------------:|:------------:|:--------:|:--------:|:-------------:|:---------:|:--------:|:-------:|:-------:|
-| multilingual-e5-large-instruct |   0.6B   | 65.53      | 61.21      | 75.54  | 49.89  | 86.24       | 48.74   | 53.47  | 84.72 | 29.89 |
-| NV-Embed-v2                    |   7.8B   | 69.81      | 65.00      | 87.19  | 47.66  | 88.69       | 49.61   | 62.84  | 83.82 | 35.21 |
-| GritLM-7B                      |   7.2B   | 67.07      | 63.22      | 81.25  | 50.82  | 87.29       | 49.59   | 54.95  | 83.03 | 35.65 |
-| gte-Qwen2-1.5B-instruct        |   1.5B   | 67.20      | 63.26      | 85.84  | 53.54  | 87.52       | 49.25   | 50.25  | 82.51 | 33.94 |
-| stella_en_1.5B_v5              |   1.5B   | 69.43      | 65.32      | 89.38  | 57.06  | 88.02       | 50.19   | 52.42  | 83.27 | 36.91 |
-| gte-Qwen2-7B-instruct          |   7.6B   | 70.72      | 65.77      | 88.52  | 58.97  | 85.9        | 50.47   | 58.09  | 82.69 | 35.74 |
-| gemini-embedding-exp-03-07     |    -     | 73.3       | 67.67      | 90.05  | 59.39  | 87.7        | 48.59   | 64.35  | 85.29 | 38.28 |
-| **Qwen3-Embedding-0.6B**       |   0.6B   | 70.70      | 64.88      | 85.76  | 54.05  | 84.37       | 48.18   | 61.83  | 86.57 | 33.43 |
-| **Qwen3-Embedding-4B**         |    4B    | 74.60      | 68.10      | 89.84  | 57.51  | 87.01       | 50.76   | 68.46  | 88.72 | 34.39 |
-| **Qwen3-Embedding-8B**         |    8B    | 75.22      | 68.71      | 90.43  | 58.57  | 87.52       | 51.56   | 69.44  | 88.58 | 34.83 |
-
-### C-MTEB (MTEB Chinese)
-
-| C-MTEB           | Param. | Mean(Task) | Mean(Type) | Class. | Clust. | Pair Class. | Rerank. | Retr. | STS   |
-|------------------|--------|------------|------------|--------|--------|-------------|---------|-------|-------|
-| multilingual-e5-large-instruct | 0.6B   | 58.08      | 58.24      | 69.80  | 48.23  | 64.52       | 57.45   | 63.65 | 45.81 |
-| bge-multilingual-gemma2 | 9B     | 67.64      | 75.31      | 59.30  | 86.67  | 68.28       | 73.73   | 55.19 | -     |
-| gte-Qwen2-1.5B-instruct  | 1.5B   | 67.12      | 67.79      | 72.53  | 54.61  | 79.5        | 68.21   | 71.86 | 60.05 |
-| gte-Qwen2-7B-instruct    | 7.6B   | 71.62      | 72.19      | 75.77  | 66.06  | 81.16       | 69.24   | 75.70 | 65.20 |
-| ritrieve_zh_v1          | 0.3B   | 72.71      | 73.85      | 76.88  | 66.5   | 85.98       | 72.86   | 76.97 | 63.92 |
-| **Qwen3-Embedding-0.6B** | 0.6B   | 66.33      | 67.45      | 71.40  | 68.74  | 76.42       | 62.58   | 71.03 | 54.52 |
-| **Qwen3-Embedding-4B**   | 4B     | 72.27      | 73.51      | 75.46  | 77.89  | 83.34       | 66.05   | 77.03 | 61.26 |
-| **Qwen3-Embedding-8B**   | 8B     | 73.84      | 75.00      | 76.97  | 80.08  | 84.23       | 66.99   | 78.21 | 63.53 |
+## Transparency
 
+This repository prioritizes reproducibility.
+
+Current documentation includes:
+
+- Training methodology
+- Retrieval evaluation
+- Hard benchmark results
+- Production inference examples
+- Deployment recommendations
+
+External benchmark leaderboard claims are intentionally omitted unless independently measured.
+
+---
+
+## Acknowledgements
+
+This project builds upon the open-source **Qwen3-Embedding-0.6B** foundation model, whose multilingual and long-context embedding architecture made this work possible.
+
+---
 
 ## Citation
 
-If you find our work helpful, feel free to give us a cite.
+If this project helps your work, please cite both the upstream Qwen3 Embedding paper and this repository.
 
-```
+### Qwen3 Embedding
+
+```bibtex
 @article{qwen3embedding,
   title={Qwen3 Embedding: Advancing Text Embedding and Reranking Through Foundation Models},
-  author={Zhang, Yanzhao and Li, Mingxin and Long, Dingkun and Zhang, Xin and Lin, Huan and Yang, Baosong and Xie, Pengjun and Yang, An and Liu, Dayiheng and Lin, Junyang and Huang, Fei and Zhou, Jingren},
+  author={Zhang, Yanzhao and Li, Mingxin and Long, Dingkun and others},
   journal={arXiv preprint arXiv:2506.05176},
   year={2025}
 }
-```
+```
+
+### Alfo Embedding
+
+```bibtex
+@misc{alfotech_alfo_embedding_2026,
+  title={Alfo Embedding 0.6B},
+  author={Alfo Tech Industries},
+  year={2026},
+  publisher={Hugging Face},
+  url={https://huggingface.co/alfotech/alfo-embedding-0.6b}
+}
+```
+
+---
+
+## License
+
+This repository follows the **Apache-2.0** licensing terms inherited from the upstream Qwen3-Embedding-0.6B model.
+
+---
+
+<p align="center">
+
+**Built by Alfo Tech Industries**
+
+*Building practical AI infrastructure for developers, startups, and enterprises.*
+
+</p>