Instructions to use IHaveNoClueAndIMustPost/Qwen3.8-Flash-Next-Simple-Q3_K with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use IHaveNoClueAndIMustPost/Qwen3.8-Flash-Next-Simple-Q3_K 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 IHaveNoClueAndIMustPost/Qwen3.8-Flash-Next-Simple-Q3_K:Q4_0
# Run inference directly in the terminal:
llama cli -hf IHaveNoClueAndIMustPost/Qwen3.8-Flash-Next-Simple-Q3_K:Q4_0

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf IHaveNoClueAndIMustPost/Qwen3.8-Flash-Next-Simple-Q3_K:Q4_0
# Run inference directly in the terminal:
llama cli -hf IHaveNoClueAndIMustPost/Qwen3.8-Flash-Next-Simple-Q3_K:Q4_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 IHaveNoClueAndIMustPost/Qwen3.8-Flash-Next-Simple-Q3_K:Q4_0
# Run inference directly in the terminal:
./llama-cli -hf IHaveNoClueAndIMustPost/Qwen3.8-Flash-Next-Simple-Q3_K:Q4_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 IHaveNoClueAndIMustPost/Qwen3.8-Flash-Next-Simple-Q3_K:Q4_0
# Run inference directly in the terminal:
./build/bin/llama-cli -hf IHaveNoClueAndIMustPost/Qwen3.8-Flash-Next-Simple-Q3_K:Q4_0

Use Docker

docker model run hf.co/IHaveNoClueAndIMustPost/Qwen3.8-Flash-Next-Simple-Q3_K:Q4_0

Llama new
LM Studio
Jan
Ollama
How to use IHaveNoClueAndIMustPost/Qwen3.8-Flash-Next-Simple-Q3_K with Ollama:
```
ollama run hf.co/IHaveNoClueAndIMustPost/Qwen3.8-Flash-Next-Simple-Q3_K:Q4_0
```
Unsloth Desktop

How to use IHaveNoClueAndIMustPost/Qwen3.8-Flash-Next-Simple-Q3_K with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf IHaveNoClueAndIMustPost/Qwen3.8-Flash-Next-Simple-Q3_K:Q4_0

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": "IHaveNoClueAndIMustPost/Qwen3.8-Flash-Next-Simple-Q3_K:Q4_0"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use IHaveNoClueAndIMustPost/Qwen3.8-Flash-Next-Simple-Q3_K with Docker Model Runner:
```
docker model run hf.co/IHaveNoClueAndIMustPost/Qwen3.8-Flash-Next-Simple-Q3_K:Q4_0
```

Lemonade

How to use IHaveNoClueAndIMustPost/Qwen3.8-Flash-Next-Simple-Q3_K with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull IHaveNoClueAndIMustPost/Qwen3.8-Flash-Next-Simple-Q3_K:Q4_0

Run and chat with the model

lemonade run user.Qwen3.8-Flash-Next-Simple-Q3_K-Q4_0

List all available models

lemonade list

Hermes Agent

How to use IHaveNoClueAndIMustPost/Qwen3.8-Flash-Next-Simple-Q3_K with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf IHaveNoClueAndIMustPost/Qwen3.8-Flash-Next-Simple-Q3_K:Q4_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 IHaveNoClueAndIMustPost/Qwen3.8-Flash-Next-Simple-Q3_K:Q4_0

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use IHaveNoClueAndIMustPost/Qwen3.8-Flash-Next-Simple-Q3_K with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf IHaveNoClueAndIMustPost/Qwen3.8-Flash-Next-Simple-Q3_K:Q4_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 "IHaveNoClueAndIMustPost/Qwen3.8-Flash-Next-Simple-Q3_K:Q4_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"

A simpler quantization mix trying to balance speed, size and performance on older hardware. Full Q8 n-gram pipeline.

Size
114,245 MiB · 5.42 bpw · 6 shards (core 60.9 GiB + 50.7 GiB table on disk).
Approx 65-70GiB total of RAM and/or VRAM needed for 131K context and Q8/F16 cache with n-gram offloading enabled.
MTP not included, use whichever standalone version fits best. Though currently --ngram-mod drafting and variants seems to work better in most cases.
Addendum: Added a standalone MTP GGUF in simple Q4_0 format. With F16 k/v cache it seems to give a noticeable, but not overwhelming, boost. However prompt processing takes a bit of a hit so depending if your weakest link is PP or TG it might be better to just stick to basic n-gram draft.

Recipe
(base Q8_0): signal path → Q8_0 · gate/up → Q3_K (edge blk 0–3 / 40–47 → IQ4_NL) · down → IQ4_NL · n-gram table → Q8_0.

PPL
4.6587 ± 0.028 (WikiText-2, 512 ctx) = +1.49% vs the 4.5903 full BF16.

Downloads last month: 97

GGUF

Model size

177B params

Architecture

qwen4exp

Hardware compatibility

4-bit

View +1 variant

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Model tree for IHaveNoClueAndIMustPost/Qwen3.8-Flash-Next-Simple-Q3_K

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Qwen/Qwen3.8-Flash-Next

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