Instructions to use ISTA-DASLab/Qwen3.8-Flash-Next-GSQ-RCO-Coder-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use ISTA-DASLab/Qwen3.8-Flash-Next-GSQ-RCO-Coder-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 ISTA-DASLab/Qwen3.8-Flash-Next-GSQ-RCO-Coder-GGUF:IQ1_M
# Run inference directly in the terminal:
llama cli -hf ISTA-DASLab/Qwen3.8-Flash-Next-GSQ-RCO-Coder-GGUF:IQ1_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf ISTA-DASLab/Qwen3.8-Flash-Next-GSQ-RCO-Coder-GGUF:IQ1_M
# Run inference directly in the terminal:
llama cli -hf ISTA-DASLab/Qwen3.8-Flash-Next-GSQ-RCO-Coder-GGUF:IQ1_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 ISTA-DASLab/Qwen3.8-Flash-Next-GSQ-RCO-Coder-GGUF:IQ1_M
# Run inference directly in the terminal:
./llama-cli -hf ISTA-DASLab/Qwen3.8-Flash-Next-GSQ-RCO-Coder-GGUF:IQ1_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 ISTA-DASLab/Qwen3.8-Flash-Next-GSQ-RCO-Coder-GGUF:IQ1_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf ISTA-DASLab/Qwen3.8-Flash-Next-GSQ-RCO-Coder-GGUF:IQ1_M

Use Docker

docker model run hf.co/ISTA-DASLab/Qwen3.8-Flash-Next-GSQ-RCO-Coder-GGUF:IQ1_M

LM Studio
Jan

vLLM

How to use ISTA-DASLab/Qwen3.8-Flash-Next-GSQ-RCO-Coder-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "ISTA-DASLab/Qwen3.8-Flash-Next-GSQ-RCO-Coder-GGUF"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ISTA-DASLab/Qwen3.8-Flash-Next-GSQ-RCO-Coder-GGUF",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Use Docker

docker model run hf.co/ISTA-DASLab/Qwen3.8-Flash-Next-GSQ-RCO-Coder-GGUF:IQ1_M

Ollama
How to use ISTA-DASLab/Qwen3.8-Flash-Next-GSQ-RCO-Coder-GGUF with Ollama:
```
ollama run hf.co/ISTA-DASLab/Qwen3.8-Flash-Next-GSQ-RCO-Coder-GGUF:IQ1_M
```
Unsloth Desktop

How to use ISTA-DASLab/Qwen3.8-Flash-Next-GSQ-RCO-Coder-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf ISTA-DASLab/Qwen3.8-Flash-Next-GSQ-RCO-Coder-GGUF:IQ1_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": "ISTA-DASLab/Qwen3.8-Flash-Next-GSQ-RCO-Coder-GGUF:IQ1_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use ISTA-DASLab/Qwen3.8-Flash-Next-GSQ-RCO-Coder-GGUF with Docker Model Runner:
```
docker model run hf.co/ISTA-DASLab/Qwen3.8-Flash-Next-GSQ-RCO-Coder-GGUF:IQ1_M
```

Lemonade

How to use ISTA-DASLab/Qwen3.8-Flash-Next-GSQ-RCO-Coder-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull ISTA-DASLab/Qwen3.8-Flash-Next-GSQ-RCO-Coder-GGUF:IQ1_M

Run and chat with the model

lemonade run user.Qwen3.8-Flash-Next-GSQ-RCO-Coder-GGUF-IQ1_M

List all available models

lemonade list

Hermes Agent

How to use ISTA-DASLab/Qwen3.8-Flash-Next-GSQ-RCO-Coder-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 ISTA-DASLab/Qwen3.8-Flash-Next-GSQ-RCO-Coder-GGUF:IQ1_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 ISTA-DASLab/Qwen3.8-Flash-Next-GSQ-RCO-Coder-GGUF:IQ1_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use ISTA-DASLab/Qwen3.8-Flash-Next-GSQ-RCO-Coder-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf ISTA-DASLab/Qwen3.8-Flash-Next-GSQ-RCO-Coder-GGUF:IQ1_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 "ISTA-DASLab/Qwen3.8-Flash-Next-GSQ-RCO-Coder-GGUF:IQ1_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"

Does its job but noticeably less impressive than the full IQ3_S in 0-shot prompt benchmarks

#16

by dandandelion - opened 1 day ago

Discussion

dandandelion

1 day ago

•

edited 1 day ago

In fact, it's quite far from the full IQ3_S tho it is still impressive that it is not broken even after half of the experts are pruned.

For comparison, this is what the IQ3_S does on the same prompts: https://huggingface.co/ISTA-DASLab/Qwen3.8-Flash-Next-GSQ-RCO-GGUF/discussions/32

Although it's quite a bit faster in strata, around 1.5x to be precise:

soyalemujica

about 8 hours ago

I think it's logical that it gives a worse pelican result, since the non-coder layers were removed, so there's less data to work with

Upload images, audio, and videos by dragging in the text input, pasting, or clicking here.

Tap or paste here to upload images

· Sign up or log in to comment