Instructions to use Bopalv/Qwen3-0.6B-quantized with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Bopalv/Qwen3-0.6B-quantized with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="Bopalv/Qwen3-0.6B-quantized",
	filename="Qwen3-0.6B-GGUF/Qwen3-0.6B.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

llama.cpp

How to use Bopalv/Qwen3-0.6B-quantized with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf Bopalv/Qwen3-0.6B-quantized:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf Bopalv/Qwen3-0.6B-quantized:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf Bopalv/Qwen3-0.6B-quantized:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf Bopalv/Qwen3-0.6B-quantized: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 Bopalv/Qwen3-0.6B-quantized:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf Bopalv/Qwen3-0.6B-quantized: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 Bopalv/Qwen3-0.6B-quantized:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf Bopalv/Qwen3-0.6B-quantized:Q4_K_M

Use Docker

docker model run hf.co/Bopalv/Qwen3-0.6B-quantized:Q4_K_M

LM Studio
Jan
Ollama
How to use Bopalv/Qwen3-0.6B-quantized with Ollama:
```
ollama run hf.co/Bopalv/Qwen3-0.6B-quantized:Q4_K_M
```

Unsloth Studio

How to use Bopalv/Qwen3-0.6B-quantized 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 Bopalv/Qwen3-0.6B-quantized 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 Bopalv/Qwen3-0.6B-quantized to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for Bopalv/Qwen3-0.6B-quantized to start chatting

How to use Bopalv/Qwen3-0.6B-quantized with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf Bopalv/Qwen3-0.6B-quantized: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": "Bopalv/Qwen3-0.6B-quantized:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use Bopalv/Qwen3-0.6B-quantized with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf Bopalv/Qwen3-0.6B-quantized: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 Bopalv/Qwen3-0.6B-quantized:Q4_K_M

Run Hermes

hermes

Docker Model Runner
How to use Bopalv/Qwen3-0.6B-quantized with Docker Model Runner:
```
docker model run hf.co/Bopalv/Qwen3-0.6B-quantized:Q4_K_M
```

Lemonade

How to use Bopalv/Qwen3-0.6B-quantized with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull Bopalv/Qwen3-0.6B-quantized:Q4_K_M

Run and chat with the model

lemonade run user.Qwen3-0.6B-quantized-Q4_K_M

List all available models

lemonade list

Qwen3-0.6B Quantized Models

This repository contains three quantized versions of the Qwen3-0.6B model, optimized for different use cases and hardware requirements.

Models Included

1. GGUF Q4_K_M (462 MB)

Format: GGUF (llama.cpp compatible)
Quantization: 4-bit K-quant (Q4_K_M)
Best for: CPU inference, llama.cpp/prima.cpp, resource-constrained environments
File: Qwen3-0.6B-GGUF/Qwen3-0.6B.Q4_K_M.gguf

2. GPTQ-Int4 (517 MB)

Format: Safetensors (HuggingFace Transformers)
Quantization: 4-bit GPTQ (group_size=128, symmetric)
Best for: GPU inference with AutoGPTQ or Transformers
Quantizer: gptqmodel 4.0.0
Directory: Qwen3-0.6B-GPTQ-Int4/

3. GPTQ-Int8 (727 MB)

Format: Safetensors (HuggingFace Transformers)
Quantization: 8-bit GPTQ (group_size=128, symmetric)
Best for: Higher accuracy with good compression
Quantizer: gptqmodel 2.2.0
Directory: Qwen3-0.6B-GPTQ-Int8/

Model Specifications

Feature	Value
Base Model	Qwen3-0.6B
Parameters	0.6B
Architecture	Qwen3ForCausalLM
Hidden Size	1024
Layers	28
Attention Heads	16
KV Heads	8
Max Context	40,960 tokens
Vocab Size	151,936

Usage

GGUF (llama.cpp / prima.cpp)

# Using prima.cpp
./llama-server -m Qwen3-0.6B-GGUF/Qwen3-0.6B.Q4_K_M.gguf --port 8080

# Using ollama
ollama run qwen3:0.6b

GPTQ (Transformers)

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained(
    "Bopalv/Qwen3-0.6B-quantized",
    subfolder="Qwen3-0.6B-GPTQ-Int4",
    device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained(
    "Bopalv/Qwen3-0.6B-quantized",
    subfolder="Qwen3-0.6B-GPTQ-Int4"
)

Quantization Details

Model	Bits	Group Size	Symmetric	Format	Size
GGUF Q4_K_M	4	N/A	Yes	GGUF	462 MB
GPTQ-Int4	4	128	Yes	Safetensors	517 MB
GPTQ-Int8	8	128	Yes	Safetensors	727 MB

Original Model

This is a quantized version of Qwen3-0.6B by Qwen Team.

License

Apache 2.0 (same as base model)

Downloads last month: 8

GGUF

Model size

0.8B params

Architecture

qwen3

Hardware compatibility

4-bit

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Model tree for Bopalv/Qwen3-0.6B-quantized

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