Instructions to use mahmoudd777/qwen35-realestate-gguf-v5 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use mahmoudd777/qwen35-realestate-gguf-v5 with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="mahmoudd777/qwen35-realestate-gguf-v5",
	filename="Qwen3.5-4B.Q4_K_M.gguf",
)

llm.create_chat_completion(
	messages = "No input example has been defined for this model task."
)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use mahmoudd777/qwen35-realestate-gguf-v5 with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf mahmoudd777/qwen35-realestate-gguf-v5:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf mahmoudd777/qwen35-realestate-gguf-v5:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf mahmoudd777/qwen35-realestate-gguf-v5:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf mahmoudd777/qwen35-realestate-gguf-v5:Q4_K_M

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf mahmoudd777/qwen35-realestate-gguf-v5:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf mahmoudd777/qwen35-realestate-gguf-v5:Q4_K_M

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf mahmoudd777/qwen35-realestate-gguf-v5:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf mahmoudd777/qwen35-realestate-gguf-v5:Q4_K_M

Use Docker

docker model run hf.co/mahmoudd777/qwen35-realestate-gguf-v5:Q4_K_M

LM Studio
Jan
Ollama
How to use mahmoudd777/qwen35-realestate-gguf-v5 with Ollama:
```
ollama run hf.co/mahmoudd777/qwen35-realestate-gguf-v5:Q4_K_M
```

Unsloth Studio

How to use mahmoudd777/qwen35-realestate-gguf-v5 with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for mahmoudd777/qwen35-realestate-gguf-v5 to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for mahmoudd777/qwen35-realestate-gguf-v5 to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for mahmoudd777/qwen35-realestate-gguf-v5 to start chatting

How to use mahmoudd777/qwen35-realestate-gguf-v5 with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf mahmoudd777/qwen35-realestate-gguf-v5:Q4_K_M

Configure the model in Pi

# Install Pi:
npm install -g @mariozechner/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "mahmoudd777/qwen35-realestate-gguf-v5:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use mahmoudd777/qwen35-realestate-gguf-v5 with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf mahmoudd777/qwen35-realestate-gguf-v5:Q4_K_M

Configure Hermes

# Install Hermes:
curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash
hermes setup
# Point Hermes at the local server:
hermes config set model.provider custom
hermes config set model.base_url http://127.0.0.1:8080/v1
hermes config set model.default mahmoudd777/qwen35-realestate-gguf-v5:Q4_K_M

Run Hermes

hermes

Docker Model Runner
How to use mahmoudd777/qwen35-realestate-gguf-v5 with Docker Model Runner:
```
docker model run hf.co/mahmoudd777/qwen35-realestate-gguf-v5:Q4_K_M
```

Lemonade

How to use mahmoudd777/qwen35-realestate-gguf-v5 with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull mahmoudd777/qwen35-realestate-gguf-v5:Q4_K_M

Run and chat with the model

lemonade run user.qwen35-realestate-gguf-v5-Q4_K_M

List all available models

lemonade list

Qwen3.5-4B Real Estate Call Analysis V5 (Egyptian Market)

LoRA fine-tune of Qwen/Qwen3.5-4B for structured-JSON extraction from Egyptian real estate call transcripts (English + Egyptian Arabic).

Training

Hyperparameter	Value
Base model	Qwen/Qwen3.5-4B
Method	LoRA via Unsloth
LoRA rank (r)	16
LoRA alpha	16
LoRA dropout	0.05
Target modules	q_proj, k_proj, v_proj, o_proj, gate_proj, up_proj, down_proj
Max seq length	2048
Epochs	6
Effective batch size	16
Learning rate	2e-4, cosine schedule
Warmup steps	50
Optimizer	adamw_8bit
Gradient clip	1.0
Weight decay	0.01
Seed	3407

Dataset

Item	Value
File	training_data_v10_final.jsonl
SHA-256 (first 12)	d2026dc822bf
Records	1026 (train 922, val 104)
Train/val split	90/10 stratified by language, seed=3407

Final metrics

Metric	Value
Train loss	0.7738
Best clean-eval	0.6224
Best noisy-eval	1.4256
Training time	316.3 min

Output schema

Structured JSON: client_name, customer_sentiment, urgency, timeline, confidence_score, transcript_quality_score, client_profile, special_requests, action_items, call_summary, total_units_requested, and requested_units[] with intent, property_type, location, currency, budget ranges, payment_method, area_sqm, bedrooms, finishing, key_objection.

Inference

Quantized to Q4_K_M (~2.7 GB). Run via llama.cpp / llama-cpp-python. Designed to run on GPUs as small as 6 GB VRAM (RTX 3050 class).

Downloads last month: 307

GGUF

Model size

4B params

Architecture

qwen35

Hardware compatibility

4-bit

Inference Providers NEW

This model isn't deployed by any Inference Provider. 🙋 Ask for provider support

Model tree for mahmoudd777/qwen35-realestate-gguf-v5

Base model

Qwen/Qwen3.5-4B-Base

Finetuned

Qwen/Qwen3.5-4B

Adapter

(255)

this model