gauravprasadgp
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qwen3-embedding_0.6B_lora

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----
-base_model: Qwen/Qwen3-Embedding-0.6B
-library_name: peft
-tags:
-- base_model:adapter:Qwen/Qwen3-Embedding-0.6B
-- lora
-- transformers
-license: mit
-language:
-- en
----
-# Model Card for Model ID
-<!-- Provide a quick summary of what the model is/does. -->
 ## Model Details
-### Model Description
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-### Framework versions
-- PEFT 0.16.0

+# 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:** [Your Name/Organization Here]
+* **Contact:** [Your Email/Contact Information Here]
+* **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 [Specify Language(s) if known, e.g., English] text. Performance on other languages may vary.
 ## Training Details
+* **Fine-tuning Method:** LoRA (Low-Rank Adaptation)
+* **LoRA Rank (r):** [Specify LoRA rank used, e.g., 8, 16, 32]
+* **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:** [Brief description of your training dataset, e.g., "A proprietary dataset of customer reviews," "A collection of scientific abstracts," "Paragraphs from Wikipedia articles."]
+    * **Dataset Size:** [Number of samples in your training dataset]
+    * **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:** [e.g., AdamW, learning rate schedule]
+* **Software Frameworks:** [e.g., PyTorch, Hugging Face Transformers, PEFT library]
+## Performance Metrics
+*(Note: Provide actual metrics from your evaluation. Examples below are placeholders.)*
+* **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]}
+}
+---
+## License
+This fine-tuned model inherits the license of the original **Qwen/Qwen3-Embedding-0.6B** model. Please refer to the [original model's license]([Link to original model's license, e.g., Hugging Face model page]) for details.
+---
+## Acknowledgements
+* The developers of **Qwen/Qwen3-Embedding-0.6B** for providing the base model.
+* The developers of the **PEFT** library for enabling efficient LoRA fine-tuning.
+* [Any other relevant acknowledgements, e.g., dataset creators, funding bodies]