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

Libraries

How to use YaraPaola/Qwen3-8B-Jailbroken-GGUF with Transformers:

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("YaraPaola/Qwen3-8B-Jailbroken-GGUF", dtype="auto")

llama-cpp-python

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

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="YaraPaola/Qwen3-8B-Jailbroken-GGUF",
	filename="Qwen3-8B-Jailbroken.IQ4_XS.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 YaraPaola/Qwen3-8B-Jailbroken-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 YaraPaola/Qwen3-8B-Jailbroken-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf YaraPaola/Qwen3-8B-Jailbroken-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf YaraPaola/Qwen3-8B-Jailbroken-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf YaraPaola/Qwen3-8B-Jailbroken-GGUF: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 YaraPaola/Qwen3-8B-Jailbroken-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf YaraPaola/Qwen3-8B-Jailbroken-GGUF: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 YaraPaola/Qwen3-8B-Jailbroken-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf YaraPaola/Qwen3-8B-Jailbroken-GGUF:Q4_K_M

Use Docker

docker model run hf.co/YaraPaola/Qwen3-8B-Jailbroken-GGUF:Q4_K_M

LM Studio
Jan
Ollama
How to use YaraPaola/Qwen3-8B-Jailbroken-GGUF with Ollama:
```
ollama run hf.co/YaraPaola/Qwen3-8B-Jailbroken-GGUF:Q4_K_M
```

Unsloth Studio

How to use YaraPaola/Qwen3-8B-Jailbroken-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 YaraPaola/Qwen3-8B-Jailbroken-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 YaraPaola/Qwen3-8B-Jailbroken-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

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

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

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf YaraPaola/Qwen3-8B-Jailbroken-GGUF: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": "YaraPaola/Qwen3-8B-Jailbroken-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use YaraPaola/Qwen3-8B-Jailbroken-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 YaraPaola/Qwen3-8B-Jailbroken-GGUF: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 YaraPaola/Qwen3-8B-Jailbroken-GGUF:Q4_K_M

Run Hermes

hermes

Atomic Chat new

OpenClaw new

How to use YaraPaola/Qwen3-8B-Jailbroken-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf YaraPaola/Qwen3-8B-Jailbroken-GGUF:Q4_K_M

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 "YaraPaola/Qwen3-8B-Jailbroken-GGUF:Q4_K_M" \
  --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 YaraPaola/Qwen3-8B-Jailbroken-GGUF with Docker Model Runner:
```
docker model run hf.co/YaraPaola/Qwen3-8B-Jailbroken-GGUF:Q4_K_M
```

Lemonade

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

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull YaraPaola/Qwen3-8B-Jailbroken-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Qwen3-8B-Jailbroken-GGUF-Q4_K_M

List all available models

lemonade list

YaraPaola

mradermacher commited on 3 days ago

Commit

fa7f0a1

0 Parent(s):

Duplicate from mradermacher/Qwen3-8B-Jailbroken-GGUF

Browse files

Co-authored-by: team mradermacher <mradermacher@users.noreply.huggingface.co>

Files changed (14) hide show

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+---
+base_model: cooperleong00/Qwen3-8B-Jailbroken
+language:
+- zho
+- eng
+- fra
+- spa
+- por
+- deu
+- ita
+- rus
+- jpn
+- kor
+- vie
+- tha
+- ara
+library_name: transformers
+quantized_by: mradermacher
+---
+## About
+<!-- ### quantize_version: 2 -->
+<!-- ### output_tensor_quantised: 1 -->
+<!-- ### convert_type: hf -->
+<!-- ### vocab_type:  -->
+<!-- ### tags:  -->
+static quants of https://huggingface.co/cooperleong00/Qwen3-8B-Jailbroken
+<!-- provided-files -->
+weighted/imatrix quants are available at https://huggingface.co/mradermacher/Qwen3-8B-Jailbroken-i1-GGUF
+## Usage
+If you are unsure how to use GGUF files, refer to one of [TheBloke's
+READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for
+more details, including on how to concatenate multi-part files.
+## Provided Quants
+(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)
+| Link | Type | Size/GB | Notes |
+|:-----|:-----|--------:|:------|
+| [GGUF](https://huggingface.co/mradermacher/Qwen3-8B-Jailbroken-GGUF/resolve/main/Qwen3-8B-Jailbroken.Q2_K.gguf) | Q2_K | 3.4 |  |
+| [GGUF](https://huggingface.co/mradermacher/Qwen3-8B-Jailbroken-GGUF/resolve/main/Qwen3-8B-Jailbroken.Q3_K_S.gguf) | Q3_K_S | 3.9 |  |
+| [GGUF](https://huggingface.co/mradermacher/Qwen3-8B-Jailbroken-GGUF/resolve/main/Qwen3-8B-Jailbroken.Q3_K_M.gguf) | Q3_K_M | 4.2 | lower quality |
+| [GGUF](https://huggingface.co/mradermacher/Qwen3-8B-Jailbroken-GGUF/resolve/main/Qwen3-8B-Jailbroken.Q3_K_L.gguf) | Q3_K_L | 4.5 |  |
+| [GGUF](https://huggingface.co/mradermacher/Qwen3-8B-Jailbroken-GGUF/resolve/main/Qwen3-8B-Jailbroken.IQ4_XS.gguf) | IQ4_XS | 4.7 |  |
+| [GGUF](https://huggingface.co/mradermacher/Qwen3-8B-Jailbroken-GGUF/resolve/main/Qwen3-8B-Jailbroken.Q4_K_S.gguf) | Q4_K_S | 4.9 | fast, recommended |
+| [GGUF](https://huggingface.co/mradermacher/Qwen3-8B-Jailbroken-GGUF/resolve/main/Qwen3-8B-Jailbroken.Q4_K_M.gguf) | Q4_K_M | 5.1 | fast, recommended |
+| [GGUF](https://huggingface.co/mradermacher/Qwen3-8B-Jailbroken-GGUF/resolve/main/Qwen3-8B-Jailbroken.Q5_K_S.gguf) | Q5_K_S | 5.8 |  |
+| [GGUF](https://huggingface.co/mradermacher/Qwen3-8B-Jailbroken-GGUF/resolve/main/Qwen3-8B-Jailbroken.Q5_K_M.gguf) | Q5_K_M | 6.0 |  |
+| [GGUF](https://huggingface.co/mradermacher/Qwen3-8B-Jailbroken-GGUF/resolve/main/Qwen3-8B-Jailbroken.Q6_K.gguf) | Q6_K | 6.8 | very good quality |
+| [GGUF](https://huggingface.co/mradermacher/Qwen3-8B-Jailbroken-GGUF/resolve/main/Qwen3-8B-Jailbroken.Q8_0.gguf) | Q8_0 | 8.8 | fast, best quality |
+| [GGUF](https://huggingface.co/mradermacher/Qwen3-8B-Jailbroken-GGUF/resolve/main/Qwen3-8B-Jailbroken.f16.gguf) | f16 | 16.5 | 16 bpw, overkill |
+Here is a handy graph by ikawrakow comparing some lower-quality quant
+types (lower is better):
+![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png)
+And here are Artefact2's thoughts on the matter:
+https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
+## FAQ / Model Request
+See https://huggingface.co/mradermacher/model_requests for some answers to
+questions you might have and/or if you want some other model quantized.
+## Thanks
+I thank my company, [nethype GmbH](https://www.nethype.de/), for letting
+me use its servers and providing upgrades to my workstation to enable
+this work in my free time.
+<!-- end -->