YAML Metadata
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The pipeline tag "text2text-generation" is not in the official list: text-classification, token-classification, table-question-answering, question-answering, zero-shot-classification, translation, summarization, feature-extraction, text-generation, fill-mask, sentence-similarity, text-to-speech, text-to-audio, automatic-speech-recognition, audio-to-audio, audio-classification, audio-text-to-text, voice-activity-detection, depth-estimation, image-classification, object-detection, image-segmentation, text-to-image, image-to-text, image-to-image, image-to-video, unconditional-image-generation, video-classification, reinforcement-learning, robotics, tabular-classification, tabular-regression, tabular-to-text, table-to-text, multiple-choice, text-ranking, text-retrieval, time-series-forecasting, text-to-video, image-text-to-text, visual-question-answering, document-question-answering, zero-shot-image-classification, graph-ml, mask-generation, zero-shot-object-detection, text-to-3d, image-to-3d, image-feature-extraction, video-text-to-text, keypoint-detection, visual-document-retrieval, any-to-any, video-to-video, other
Qwen3 0.6B - Real Estate Fine-Tuned Adapter (LoRA)
This model is a Qwen3-0.6B adapter fine-tuned using LoRA on a real estate dataset for tasks such as property description generation and value estimation. Fine-tuning was performed using LLaMA Factory.
Base Model
Qwen/Qwen3-0.6B- Fine-tuned using LoRA with
lora_rank=64targeting all transformer layers.
Fine-Tuning Details
| Setting | Value |
|---|---|
| Framework | LLaMA Factory |
| Finetuning Type | LoRA |
| LoRA Rank | 64 |
| Dataset | Custom real estate dataset |
| Cutoff Length | 3500 tokens |
| Epochs | 3 |
| Batch Size | 1 (accumulated over 8 steps) |
| Learning Rate | 1e-4 |
| Scheduler | Cosine |
| Evaluation Metric | eval_loss |
| Best Model Criterion | Lowest validation loss |
Dataset
- Sample LlaMa-Factory Dataset :
heba1998/real-estate-data-for-llm-fine-tuning - Full Dataset:
heba1998/real-estate-data-for-llm-fine-tuning
Usage
from transformers import AutoModelForCausalLM, AutoTokenizer
from peft import PeftModel
base_model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen3-0.6B", device_map="auto", trust_remote_code=True)
tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen3-0.6B", trust_remote_code=True)
adapter = PeftModel.from_pretrained(base_model, "heba1998/Qwen-LoRA-Estate")
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