Instructions to use mansoorhamidzadeh/qwen3-0.6b-entity-attr-basalam-gguf with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use mansoorhamidzadeh/qwen3-0.6b-entity-attr-basalam-gguf with llama.cpp:

Install (macOS, Linux)

curl -LsSf https://llama.app/install.sh | sh
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf mansoorhamidzadeh/qwen3-0.6b-entity-attr-basalam-gguf:Q8_0
# Run inference directly in the terminal:
llama cli -hf mansoorhamidzadeh/qwen3-0.6b-entity-attr-basalam-gguf:Q8_0

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf mansoorhamidzadeh/qwen3-0.6b-entity-attr-basalam-gguf:Q8_0
# Run inference directly in the terminal:
llama cli -hf mansoorhamidzadeh/qwen3-0.6b-entity-attr-basalam-gguf:Q8_0

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 mansoorhamidzadeh/qwen3-0.6b-entity-attr-basalam-gguf:Q8_0
# Run inference directly in the terminal:
./llama-cli -hf mansoorhamidzadeh/qwen3-0.6b-entity-attr-basalam-gguf:Q8_0

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 mansoorhamidzadeh/qwen3-0.6b-entity-attr-basalam-gguf:Q8_0
# Run inference directly in the terminal:
./build/bin/llama-cli -hf mansoorhamidzadeh/qwen3-0.6b-entity-attr-basalam-gguf:Q8_0

Use Docker

docker model run hf.co/mansoorhamidzadeh/qwen3-0.6b-entity-attr-basalam-gguf:Q8_0

LM Studio
Jan

vLLM

How to use mansoorhamidzadeh/qwen3-0.6b-entity-attr-basalam-gguf with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "mansoorhamidzadeh/qwen3-0.6b-entity-attr-basalam-gguf"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "mansoorhamidzadeh/qwen3-0.6b-entity-attr-basalam-gguf",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/mansoorhamidzadeh/qwen3-0.6b-entity-attr-basalam-gguf:Q8_0

Ollama
How to use mansoorhamidzadeh/qwen3-0.6b-entity-attr-basalam-gguf with Ollama:
```
ollama run hf.co/mansoorhamidzadeh/qwen3-0.6b-entity-attr-basalam-gguf:Q8_0
```

Unsloth Studio

How to use mansoorhamidzadeh/qwen3-0.6b-entity-attr-basalam-gguf 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 mansoorhamidzadeh/qwen3-0.6b-entity-attr-basalam-gguf 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 mansoorhamidzadeh/qwen3-0.6b-entity-attr-basalam-gguf to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for mansoorhamidzadeh/qwen3-0.6b-entity-attr-basalam-gguf to start chatting

How to use mansoorhamidzadeh/qwen3-0.6b-entity-attr-basalam-gguf with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf mansoorhamidzadeh/qwen3-0.6b-entity-attr-basalam-gguf:Q8_0

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": "mansoorhamidzadeh/qwen3-0.6b-entity-attr-basalam-gguf:Q8_0"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use mansoorhamidzadeh/qwen3-0.6b-entity-attr-basalam-gguf with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf mansoorhamidzadeh/qwen3-0.6b-entity-attr-basalam-gguf:Q8_0

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 mansoorhamidzadeh/qwen3-0.6b-entity-attr-basalam-gguf:Q8_0

Run Hermes

hermes

Atomic Chat new

OpenClaw new

How to use mansoorhamidzadeh/qwen3-0.6b-entity-attr-basalam-gguf with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf mansoorhamidzadeh/qwen3-0.6b-entity-attr-basalam-gguf:Q8_0

Configure OpenClaw

# Install OpenClaw:
npm install -g openclaw@latest
# Register the local server and set it as the default model:
openclaw onboard --non-interactive --mode local \
  --auth-choice custom-api-key \
  --custom-base-url http://127.0.0.1:8080/v1 \
  --custom-model-id "mansoorhamidzadeh/qwen3-0.6b-entity-attr-basalam-gguf:Q8_0" \
  --custom-provider-id llama-cpp \
  --custom-compatibility openai \
  --custom-text-input \
  --accept-risk \
  --skip-health

Run OpenClaw

openclaw agent --local --agent main --message "Hello from Hugging Face"

Docker Model Runner
How to use mansoorhamidzadeh/qwen3-0.6b-entity-attr-basalam-gguf with Docker Model Runner:
```
docker model run hf.co/mansoorhamidzadeh/qwen3-0.6b-entity-attr-basalam-gguf:Q8_0
```

Lemonade

How to use mansoorhamidzadeh/qwen3-0.6b-entity-attr-basalam-gguf with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull mansoorhamidzadeh/qwen3-0.6b-entity-attr-basalam-gguf:Q8_0

Run and chat with the model

lemonade run user.qwen3-0.6b-entity-attr-basalam-gguf-Q8_0

List all available models

lemonade list

Model Details

⚠️ EXPERIMENTAL IMPLEMENTATION - NOT PRODUCTION-READY ⚠️
Proof-of-concept model for research purposes only

Model Type: This model is a fine-tuned version of qwen3-0.6b for generating product attributes and entities from product titles in Persian.

Fine-tuning Dataset: The model was fine-tuned on 20,000 samples from the BaSalam/entity-attribute-sft-dataset-GPT-4.0-generated-v1 dataset, which contains GPT-4 generated data for higher quality responses.

Training Environment:

GPU: Kaggle P100 GPU with 16GB memory
Epochs: 1
Library: Unsloth library was used for fine-tuning

Intended Use

This model is designed to take a product title in Persian and generate a JSON output containing the product entity and its attributes. It is particularly useful for applications that require structured product information extraction from unstructured text.

Example Usage

Input:

prompt = """instruction': \"here is a product title from a Iranian marketplace. \n         give me the Product Entity and Attributes of this product in Persian language.\n         give the output in this json format: {'attributes': {'attribute_name' : <attribute value>, ...}, 'product_entity': '<product entity>'}.\n         Don't make assumptions about what values to plug into json. Just give Json not a single word more.\n         \nproduct title:"""
title = """: ست شابلون ژله ای دو قلو صریر 20سانتی 1 عدد
1 عدد ست شابلون ژله ای دو قلو سریر 20سانتی متر
با کیفیت مناسب و صادراتی
شامل دو تکه شابلون ژله ای
در چهار رنگ سبز، قرمز، نارنجی و آبی موجود است.
پخش لوازم التحریر کیان""""

Output

{
  "attributes": {
    "تعداد در بسته‌بندی": ["1 عدد"],
    "ابعاد": ["20 سانتی متر"],
    "رنگ": ["سبز", "قرمز", "نارنجی", "آبی"],
    "کیفیت": ["مناسب و صادراتی"],
    "محتویات بسته‌بندی": ["دو تکه شابلون ژله ای"]
  },
  "product_entity": ["لوازم التحریر کیان"]
}

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GGUF

Model size

0.6B params

Architecture

qwen3

Hardware compatibility

8-bit

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mansoorhamidzadeh
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qwen3-0.6b-entity-attr-basalam-gguf

Model Details

Intended Use

Example Usage

Model tree for mansoorhamidzadeh/qwen3-0.6b-entity-attr-basalam-gguf

Dataset used to train mansoorhamidzadeh/qwen3-0.6b-entity-attr-basalam-gguf