Instructions to use codebasic/Qwen3-8B-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use codebasic/Qwen3-8B-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="codebasic/Qwen3-8B-GGUF",
	filename="Qwen3-8B-F16.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 codebasic/Qwen3-8B-GGUF with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf codebasic/Qwen3-8B-GGUF:F16
# Run inference directly in the terminal:
llama-cli -hf codebasic/Qwen3-8B-GGUF:F16

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf codebasic/Qwen3-8B-GGUF:F16
# Run inference directly in the terminal:
llama-cli -hf codebasic/Qwen3-8B-GGUF:F16

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 codebasic/Qwen3-8B-GGUF:F16
# Run inference directly in the terminal:
./llama-cli -hf codebasic/Qwen3-8B-GGUF:F16

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 codebasic/Qwen3-8B-GGUF:F16
# Run inference directly in the terminal:
./build/bin/llama-cli -hf codebasic/Qwen3-8B-GGUF:F16

Use Docker

docker model run hf.co/codebasic/Qwen3-8B-GGUF:F16

LM Studio
Jan
Ollama
How to use codebasic/Qwen3-8B-GGUF with Ollama:
```
ollama run hf.co/codebasic/Qwen3-8B-GGUF:F16
```

Unsloth Studio new

How to use codebasic/Qwen3-8B-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 codebasic/Qwen3-8B-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 codebasic/Qwen3-8B-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for codebasic/Qwen3-8B-GGUF to start chatting

Pi new

How to use codebasic/Qwen3-8B-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf codebasic/Qwen3-8B-GGUF:F16

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": "codebasic/Qwen3-8B-GGUF:F16"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use codebasic/Qwen3-8B-GGUF with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf codebasic/Qwen3-8B-GGUF:F16

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 codebasic/Qwen3-8B-GGUF:F16

Run Hermes

hermes

Docker Model Runner
How to use codebasic/Qwen3-8B-GGUF with Docker Model Runner:
```
docker model run hf.co/codebasic/Qwen3-8B-GGUF:F16
```

Lemonade

How to use codebasic/Qwen3-8B-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull codebasic/Qwen3-8B-GGUF:F16

Run and chat with the model

lemonade run user.Qwen3-8B-GGUF-F16

List all available models

lemonade list

lseongjoo commited on Aug 12, 2025

Commit

ae5ff0a

verified ·

1 Parent(s): d05b710

Update README.md (#1)

Browse files

- Update README.md (4d75911661d1dd272841d730de6a1eec948f5671)

Files changed (1) hide show

README.md +40 -3

README.md CHANGED Viewed

@@ -1,3 +1,40 @@
----
-license: apache-2.0
----

+---
+license: apache-2.0
+base_model:
+- Qwen/Qwen3-8B
+tags:
+- gguf
+- llama.cpp
+- qwen
+- quantization
+---
+# Qwen3-8B-GGUF
+## 🤖 코드베이직 제공
+이 모델은 **코드베이직(codebasic)**에서 GGUF 포맷으로 변환·배포하였습니다.
+이 리포지토리는 [Qwen3-8B](https://huggingface.co/Qwen/Qwen3-8B) 모델을 여러 GGUF 양자화 버전으로 제공합니다.
+llama.cpp, text-generation-webui, koboldcpp 등 GGUF 포맷을 지원하는 다양한 환경에서 사용할 수 있습니다.
+---
+## 📂 제공 파일
+| 파일명 | 양자화 방식 | 메모리 요구량(대략) | 설명 |
+|--------|------------|----------------------|------|
+| `Qwen3-8B-F16.gguf`  | FP16 (비양자화) | ~16GB | 원본 FP16 가중치 (GPU/고사양 환경) |
+| `Qwen3-8B-Q8_0.gguf` | Q8_0 | ~9GB | 고품질 양자화, 거의 FP16 수준의 정확도 |
+> 💡 메모리 요구량은 추정치이며, 환경에 따라 다를 수 있습니다.
+---
+## 🚀 사용 방법
+### 1. Docker (llama.cpp Q8_0 예시)
+```bash
+docker run -v /path/to/models:/models \
+    ghcr.io/ggml-org/llama.cpp:full \
+    --run -m /models/Qwen3-8B/Qwen3-8B-Q8_0.gguf \
+    -p "언어 모델 소개"