Instructions to use mradermacher/Qwen3.8-Flash-Next-Uncensored-i1-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use mradermacher/Qwen3.8-Flash-Next-Uncensored-i1-GGUF with Transformers:

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("mradermacher/Qwen3.8-Flash-Next-Uncensored-i1-GGUF", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use mradermacher/Qwen3.8-Flash-Next-Uncensored-i1-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 mradermacher/Qwen3.8-Flash-Next-Uncensored-i1-GGUF:IQ3_M
# Run inference directly in the terminal:
llama cli -hf mradermacher/Qwen3.8-Flash-Next-Uncensored-i1-GGUF:IQ3_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf mradermacher/Qwen3.8-Flash-Next-Uncensored-i1-GGUF:IQ3_M
# Run inference directly in the terminal:
llama cli -hf mradermacher/Qwen3.8-Flash-Next-Uncensored-i1-GGUF:IQ3_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 mradermacher/Qwen3.8-Flash-Next-Uncensored-i1-GGUF:IQ3_M
# Run inference directly in the terminal:
./llama-cli -hf mradermacher/Qwen3.8-Flash-Next-Uncensored-i1-GGUF:IQ3_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 mradermacher/Qwen3.8-Flash-Next-Uncensored-i1-GGUF:IQ3_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf mradermacher/Qwen3.8-Flash-Next-Uncensored-i1-GGUF:IQ3_M

Use Docker

docker model run hf.co/mradermacher/Qwen3.8-Flash-Next-Uncensored-i1-GGUF:IQ3_M

LM Studio
Jan
Ollama
How to use mradermacher/Qwen3.8-Flash-Next-Uncensored-i1-GGUF with Ollama:
```
ollama run hf.co/mradermacher/Qwen3.8-Flash-Next-Uncensored-i1-GGUF:IQ3_M
```
Unsloth Desktop

How to use mradermacher/Qwen3.8-Flash-Next-Uncensored-i1-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf mradermacher/Qwen3.8-Flash-Next-Uncensored-i1-GGUF:IQ3_M

Configure the model in Pi

# Install Pi:
npm install -g @earendil-works/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "mradermacher/Qwen3.8-Flash-Next-Uncensored-i1-GGUF:IQ3_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use mradermacher/Qwen3.8-Flash-Next-Uncensored-i1-GGUF with Docker Model Runner:
```
docker model run hf.co/mradermacher/Qwen3.8-Flash-Next-Uncensored-i1-GGUF:IQ3_M
```

Lemonade

How to use mradermacher/Qwen3.8-Flash-Next-Uncensored-i1-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull mradermacher/Qwen3.8-Flash-Next-Uncensored-i1-GGUF:IQ3_M

Run and chat with the model

lemonade run user.Qwen3.8-Flash-Next-Uncensored-i1-GGUF-IQ3_M

List all available models

lemonade list

Hermes Agent

How to use mradermacher/Qwen3.8-Flash-Next-Uncensored-i1-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 mradermacher/Qwen3.8-Flash-Next-Uncensored-i1-GGUF:IQ3_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 mradermacher/Qwen3.8-Flash-Next-Uncensored-i1-GGUF:IQ3_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use mradermacher/Qwen3.8-Flash-Next-Uncensored-i1-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf mradermacher/Qwen3.8-Flash-Next-Uncensored-i1-GGUF:IQ3_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 "mradermacher/Qwen3.8-Flash-Next-Uncensored-i1-GGUF:IQ3_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"

About

weighted/imatrix quants of https://huggingface.co/orcarouter/Qwen3.8-Flash-Next-Uncensored

For a convenient overview and download list, visit our model page for this model.

static quants are available at https://huggingface.co/mradermacher/Qwen3.8-Flash-Next-Uncensored-GGUF

This is a vision model - mmproj files (if any) will be in the static repository.

Usage

If you are unsure how to use GGUF files, refer to one of TheBloke's READMEs 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	imatrix	0.7	imatrix file (for creating your own quants)
GGUF	i1-Q2_K	80.5	IQ3_XXS probably better
GGUF	i1-Q3_K_S	88.9	IQ3_XS probably better
GGUF	i1-IQ3_S	89.0	beats Q3_K*
GGUF	i1-IQ3_M	89.6
GGUF	i1-Q3_K_M	94.2	IQ3_S probably better
GGUF	i1-Q3_K_L	96.8	IQ3_M probably better
GGUF	i1-IQ4_XS	97.6
PART 1 PART 2 PART 3	i1-Q4_0	100.4	fast, low quality
PART 1 PART 2 PART 3	i1-Q4_1	111.1
PART 1 PART 2 PART 3	i1-Q4_K_S	111.9	optimal size/speed/quality
PART 1 PART 2 PART 3	i1-Q4_K_M	119.3	fast, recommended
PART 1 PART 2 PART 3	i1-Q5_K_S	127.8
PART 1 PART 2 PART 3	i1-Q5_K_M	134.2
PART 1 PART 2 PART 3 PART 4	i1-Q6_K	167.7	practically like static Q6_K

Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better):

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, for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. Additional thanks to @nicoboss for giving me access to his private supercomputer, enabling me to provide many more imatrix quants, at much higher quality, than I would otherwise be able to.

Downloads last month: 9,098

GGUF

Model size

177B params

Architecture

qwen4exp

Hardware compatibility

2-bit

3-bit

4-bit

View +1 variant

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Model tree for mradermacher/Qwen3.8-Flash-Next-Uncensored-i1-GGUF

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Qwen/Qwen3.8-Flash-Next

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orcarouter/Qwen3.8-Flash-Next-Uncensored

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Qwen3.8-Flash-Next-Uncensored-i1-GGUF

About

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Thanks

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