Instructions to use radcrew/RadEstate-Qwen2.5-0.5B-IntakeAdapter with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use radcrew/RadEstate-Qwen2.5-0.5B-IntakeAdapter with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="radcrew/RadEstate-Qwen2.5-0.5B-IntakeAdapter")

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("radcrew/RadEstate-Qwen2.5-0.5B-IntakeAdapter", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use radcrew/RadEstate-Qwen2.5-0.5B-IntakeAdapter with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "radcrew/RadEstate-Qwen2.5-0.5B-IntakeAdapter"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "radcrew/RadEstate-Qwen2.5-0.5B-IntakeAdapter",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/radcrew/RadEstate-Qwen2.5-0.5B-IntakeAdapter

SGLang

How to use radcrew/RadEstate-Qwen2.5-0.5B-IntakeAdapter with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "radcrew/RadEstate-Qwen2.5-0.5B-IntakeAdapter" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "radcrew/RadEstate-Qwen2.5-0.5B-IntakeAdapter",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "radcrew/RadEstate-Qwen2.5-0.5B-IntakeAdapter" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "radcrew/RadEstate-Qwen2.5-0.5B-IntakeAdapter",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use radcrew/RadEstate-Qwen2.5-0.5B-IntakeAdapter with Docker Model Runner:
```
docker model run hf.co/radcrew/RadEstate-Qwen2.5-0.5B-IntakeAdapter
```

RadEstate Qwen2.5-0.5B Intake Adapter

This model is a LoRA fine-tuned version of Qwen2.5-0.5B-Instruct optimized for RadEstate intake question ingestion.

The model has been trained to better understand real-estate-related intake data and assist with extracting structured information from user inputs.

Model Details

Base model: Qwen/Qwen2.5-0.5B-Instruct
Fine-tuning method: LoRA (Low-Rank Adaptation)
Quantization: 4-bit quantized model
Training dataset size: ~2,000 examples
Task: Real-estate intake question ingestion

Intended Use

This model is intended for:

Real-estate intake processing
Understanding domain-specific user responses
Extracting structured information from natural language inputs
Supporting RadEstate workflows

Training Details

The model was fine-tuned using LoRA on top of Qwen2.5-0.5B-Instruct.

Training approach:

Parameter-efficient fine-tuning using LoRA
Domain-specific instruction dataset
Approximately 2,000 training examples

After fine-tuning, the model was quantized to 4-bit precision for reduced memory usage and efficient deployment.

Usage

This repository contains the fine-tuned model weights.

Example:

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "radcrew/RadEstate-Qwen2.5-0.5B-Intake"

tokenizer = AutoTokenizer.from_pretrained(model_id)

model = AutoModelForCausalLM.from_pretrained(
    model_id,
    device_map="auto"
)

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