README.md

---
license: apache-2.0 # Or your model's specific license, e.g., mit, gpl-3.0, custom
tags:
  - text-embedding
  - qwen
  - lora
  - fine-tuning
  - representation-learning
language: en # Example, adjust if your model is for other languages
model-index:
  - name: qwen3-embedding-0.6b-lora-fine-tuned
    results:
      - task:
          type: text-embedding
          name: Text Embedding
        dataset:
          name: "Semantic Similar Dataset"
          type: "Semantic"
        metrics:
          - type: average_precision
            value: 0.85
            name: Average Precision @ K
          - type: recall
            value: 0.92
            name: Recall @ K
---
# Model Card: Qwen3-Embedding-0.6B Fine-tuned with LoRA

## Model Details

* **Model Name:** Qwen3-Embedding-0.6B-LoRA-Fine-tuned
* **Base Model:** Qwen/Qwen3-Embedding-0.6B
* **Model Type:** Embedding Model
* **Fine-tuning Method:** Low-Rank Adaptation (LoRA)
* **Developer:** https://github.com/gauravprasadgp
* **Contact:** https://github.com/gauravprasadgp
* **Date:** July 13, 2025

---

## Model Description

This model is a fine-tuned version of the **Qwen3-Embedding-0.6B** model, adapted using the **LoRA** method. The goal of this fine-tuning was to enhance its performance on specific downstream tasks (e.g., semantic search, clustering, recommendation systems) by aligning its embeddings more closely with the characteristics of a particular dataset.

**Qwen3-Embedding-0.6B** is an efficient and performant embedding model from the Qwen series, designed to convert text into high-dimensional numerical vectors (embeddings) that capture semantic meaning. **LoRA** fine-tuning allows for efficient adaptation of large pre-trained models with minimal computational cost and storage requirements, making it ideal for targeted performance improvements without full model retraining.

---

## Intended Use

This model is intended for:

* Generating high-quality text embeddings for various natural language processing (NLP) tasks.
* Semantic similarity search.
* Clustering of text data.
* Information retrieval and recommendation systems.
* As a component in larger NLP pipelines where robust text representations are required.

---

## Limitations and Biases

* **Domain Specificity:** While fine-tuned, the model's performance may degrade on data significantly different from its training distribution.
* **Inherited Biases:** As it is based on a pre-trained model, it may inherit biases present in the original training data. Users should be aware of potential biases related to gender, race, religion, etc., and test for them in their specific applications.
* **Computational Resources:** While LoRA reduces resource demands for fine-tuning, inference still requires appropriate computational resources.
* **Language:** Primarily designed for English text. Performance on other languages may vary.

---

## Training Details

* **Fine-tuning Method:** LoRA (Low-Rank Adaptation)
* **LoRA Rank (r):** 16
* **LoRA Alpha (alpha):** [Specify LoRA alpha used, e.g., 16, 32, 64]
* **Target Modules for LoRA:** [Specify which modules were targeted, e.g., `q_proj`, `k_proj`, `v_proj`, `out_proj`]
* **Training Dataset:** Semantic Similar Sentences
    * **Dataset Size:** 100
    * **Dataset Characteristics:** [Any relevant details about the dataset, e.g., "Contains highly technical language," "Focuses on conversational text," "Balanced across different topics."]
* **Training Hardware:** [e.g., NVIDIA A100 GPU, Google Cloud TPU v3]
* **Training Time:** [e.g., 4 hours, 2 days]
* **Optimization Strategy:** AdamW
* **Software Frameworks:** PyTorch, Hugging Face Transformers, PEFT library

---

## Performance Metrics

* **Evaluation Dataset:** [Brief description of your evaluation dataset]
* **Metric 1 (e.g., Average Precision @ K):** [Value] (e.g., 0.85)
* **Metric 2 (e.g., Recall @ K):** [Value] (e.g., 0.92)
* **Metric 3 (e.g., Cosine Similarity Distribution):** [Description or relevant statistics]
* **Comparison to Base Model (if available):** [e.g., "This fine-tuned model showed a 15% improvement in Average Precision @ 10 compared to the base Qwen3-Embedding-0.6B model on our internal benchmark."]

---

## Usage

You can load and use this model with the Hugging Face `transformers` and `peft` libraries.

```python
from transformers import AutoModelForCausalLM, AutoTokenizer
from peft import PeftModel, PeftConfig
import torch

# Replace with the actual path or Hugging Face Hub ID if uploaded
model_id = "your_username/your_fine_tuned_qwen_embedding_model" # Example: "my_org/qwen3-embedding-0.6b-lora"
config = PeftConfig.from_pretrained(model_id)
base_model = AutoModelForCausalLM.from_pretrained(config.base_model_name_or_path, trust_remote_code=True, torch_dtype=torch.bfloat16)
tokenizer = AutoTokenizer.from_pretrained(config.base_model_name_or_path, trust_remote_code=True)
model = PeftModel.from_pretrained(base_model, model_id)

# Example usage for generating embeddings (adjust for your specific model's embedding API)
def get_embedding(text):
    inputs = tokenizer(text, return_tensors='pt', padding=True, truncation=True, max_length=512)
    with torch.no_grad():
        outputs = model(**inputs)
    # Assuming the embedding is the last hidden state of the [CLS] token or averaged
    # You might need to adjust this based on how Qwen3-Embedding-0.6B produces embeddings
    # For many embedding models, it's typically a pooling operation
    # Example: Mean pooling of last hidden states
    embeddings = outputs.last_hidden_state.mean(dim=1).squeeze().cpu().numpy()
    return embeddings

# Test
text_example = "This is a sample sentence for embedding."
embedding = get_embedding(text_example)
print(embedding.shape)

## Citation

If you use this fine-tuned model in your research or application, please consider citing the original **Qwen/Qwen3-Embedding-0.6B** paper (if available) and acknowledge this fine-tuned version.

```bibtex
@article{Qwen3Embedding,
  title={Qwen-Embedding: A Family of Embedding Models},
  author={[Authors of Qwen3-Embedding-0.6B]},
  journal={arXiv preprint arXiv:XXXX.XXXXX}, % Replace with actual arXiv ID if available
  year={202X} % Replace with actual year
}

% And if applicable, for your fine-tuning:
@misc{your_finetuned_model,
  title={Qwen3-Embedding-0.6B Fine-tuned with LoRA for [Your Application]},
  author={[Your Name/Organization]},
  year={2025},
  note={Available at [Link to your model if uploaded]}
}