Instructions to use namepd/Qwen3-VL-8B-Instruct-Q4_K_M-layers with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use namepd/Qwen3-VL-8B-Instruct-Q4_K_M-layers 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 namepd/Qwen3-VL-8B-Instruct-Q4_K_M-layers:F16
# Run inference directly in the terminal:
llama cli -hf namepd/Qwen3-VL-8B-Instruct-Q4_K_M-layers:F16

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf namepd/Qwen3-VL-8B-Instruct-Q4_K_M-layers:F16
# Run inference directly in the terminal:
llama cli -hf namepd/Qwen3-VL-8B-Instruct-Q4_K_M-layers: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 namepd/Qwen3-VL-8B-Instruct-Q4_K_M-layers:F16
# Run inference directly in the terminal:
./llama-cli -hf namepd/Qwen3-VL-8B-Instruct-Q4_K_M-layers: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 namepd/Qwen3-VL-8B-Instruct-Q4_K_M-layers:F16
# Run inference directly in the terminal:
./build/bin/llama-cli -hf namepd/Qwen3-VL-8B-Instruct-Q4_K_M-layers:F16

Use Docker

docker model run hf.co/namepd/Qwen3-VL-8B-Instruct-Q4_K_M-layers:F16

LM Studio
Jan

vLLM

How to use namepd/Qwen3-VL-8B-Instruct-Q4_K_M-layers with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "namepd/Qwen3-VL-8B-Instruct-Q4_K_M-layers"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "namepd/Qwen3-VL-8B-Instruct-Q4_K_M-layers",
		"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/namepd/Qwen3-VL-8B-Instruct-Q4_K_M-layers:F16

Ollama
How to use namepd/Qwen3-VL-8B-Instruct-Q4_K_M-layers with Ollama:
```
ollama run hf.co/namepd/Qwen3-VL-8B-Instruct-Q4_K_M-layers:F16
```

Unsloth Studio

How to use namepd/Qwen3-VL-8B-Instruct-Q4_K_M-layers 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 namepd/Qwen3-VL-8B-Instruct-Q4_K_M-layers 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 namepd/Qwen3-VL-8B-Instruct-Q4_K_M-layers to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for namepd/Qwen3-VL-8B-Instruct-Q4_K_M-layers to start chatting

How to use namepd/Qwen3-VL-8B-Instruct-Q4_K_M-layers with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf namepd/Qwen3-VL-8B-Instruct-Q4_K_M-layers: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": "namepd/Qwen3-VL-8B-Instruct-Q4_K_M-layers:F16"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use namepd/Qwen3-VL-8B-Instruct-Q4_K_M-layers with Docker Model Runner:
```
docker model run hf.co/namepd/Qwen3-VL-8B-Instruct-Q4_K_M-layers:F16
```

Lemonade

How to use namepd/Qwen3-VL-8B-Instruct-Q4_K_M-layers with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull namepd/Qwen3-VL-8B-Instruct-Q4_K_M-layers:F16

Run and chat with the model

lemonade run user.Qwen3-VL-8B-Instruct-Q4_K_M-layers-F16

List all available models

lemonade list

Hermes Agent

How to use namepd/Qwen3-VL-8B-Instruct-Q4_K_M-layers with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf namepd/Qwen3-VL-8B-Instruct-Q4_K_M-layers: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 namepd/Qwen3-VL-8B-Instruct-Q4_K_M-layers:F16

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use namepd/Qwen3-VL-8B-Instruct-Q4_K_M-layers with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf namepd/Qwen3-VL-8B-Instruct-Q4_K_M-layers:F16

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 "namepd/Qwen3-VL-8B-Instruct-Q4_K_M-layers:F16" \
  --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"

Qwen3VL-8B-Instruct-Q4_K_M

Distributed GGUF inference package for Mesh LLM

GGUF layer package for running Qwen3VL-8B-Instruct-Q4_K_M across a local Mesh LLM cluster.

This package is derived from Qwen/Qwen3-VL-8B-Instruct-GGUF and keeps the original GGUF distribution split into per-layer artifacts for distributed inference.

Highlights

Run locally	Pool multiple machines	OpenAI-compatible	Package variant
Private inference on your hardware	Split layers across peers	Serve `/v1/chat/completions` locally	`Q4_K_M` layer package

Model Overview

Property	Value
Source model	Qwen/Qwen3-VL-8B-Instruct-GGUF
Model id	`Qwen/Qwen3-VL-8B-Instruct-GGUF:Q4_K_M`
Family	Qwen3
Parameter scale	8B
Quantization	`Q4_K_M`
Layer count	36
Activation width	4096
Package size	6.7 GB
Source file	`Qwen3VL-8B-Instruct-Q4_K_M.gguf`
Package repo	namepd/Qwen3-VL-8B-Instruct-Q4_K_M-layers
License	`apache-2.0` from Qwen/Qwen3-VL-8B-Instruct-GGUF

Recommended Use

Local and private inference with Mesh LLM.
Multi-machine serving when the full GGUF is too large for one host.
OpenAI-compatible chat/completions workflows through Mesh LLM's local API.

For upstream architecture details, chat template guidance, sampling recommendations, license terms, and benchmark notes, see the source model card: Qwen/Qwen3-VL-8B-Instruct-GGUF.

Quickstart

# Run this on each machine that should contribute memory/compute.
mesh-llm serve --model "namepd/Qwen3-VL-8B-Instruct-Q4_K_M-layers" --split

# Check the mesh and discover the OpenAI-compatible model name.
curl -s http://localhost:3131/api/status
curl -s http://localhost:3131/v1/models

# Send an OpenAI-compatible chat request.
curl -s http://localhost:3131/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model": "Qwen/Qwen3-VL-8B-Instruct-GGUF:Q4_K_M",
    "messages": [{"role": "user", "content": "Write a tiny hello-world function in Rust."}],
    "max_tokens": 128
  }'

Package Variant

Property	Value
Format	`layer-package`
Canonical source ref	`Qwen/Qwen3-VL-8B-Instruct-GGUF@f982a07559d4a2f6c8744d840bf6fccab30eea96/Qwen3VL-8B-Instruct-Q4_K_M.gguf`
Source revision	`f982a07559d4a2f6c8744d840bf6fccab30eea96`
Source SHA-256	`67d1659bfe71b89d50b45a4ad1a9e5b997e5bb16ce5da66a6a6167abd569e9e2`
Skippy ABI	`0.1.38`
Package manifest SHA-256	`dd6e6a120672480455a98eab01b05ef2994db9f68f53df050f3f9fb863dd011c`

What Is Included

Artifact	Path	Contents	SHA-256
Manifest	`model-package.json`	Package schema, source identity, checksums	`dd6e6a120672480455a98eab01b05ef2994db9f68f53df050f3f9fb863dd011c`
Metadata	`shared/metadata.gguf`	0 tensors, 5.7 MB	`9feef8d599a3ede981d6254ed23f45191439f4c3e9f8fda763d9827bcac76b27`
Embeddings	`shared/embeddings.gguf`	1 tensors, 339.5 MB	`df24139105af8289aa14a281dfdc6758c9fbab38b3e6d21749ae39d5e2a42c93`
Output head	`shared/output.gguf`	2 tensors, 492.5 MB	`a8fcd68146baac9ca49efea4d89d6eb59dbf21343988f4fa80402ee42a0af974`
Transformer layers	`layers/layer-*.gguf`	36 layer artifacts, 396 tensors, 4.1 GB	`see model-package.json`
Projector	`projectors/mmproj-Qwen3VL-8B-Instruct-F16.gguf`	mmproj projector, 1.1 GB	`ca524100ebf825c9a870db1c580d03879e0da0ab2541697e2458e64891cf9d38`
Projector	`projectors/mmproj-Qwen3VL-8B-Instruct-Q8_0.gguf`	mmproj projector, 717.4 MB	`c6ba85508d82f42590e6eb77d5340369ab6fecf107a7561d809523d8aa5f3bfd`

Validation

Generated by the Mesh LLM HF Jobs splitter from mesh-llm ref main. Each artifact is checksummed as it is written, uploaded to this repository, and removed from the job workspace before the next artifact is produced.

skippy-model-package write-package "/source/Qwen3VL-8B-Instruct-Q4_K_M.gguf" --out-dir "/tmp/meshllm-layer-job-namepd_Qwen3-VL-8B-Instruct-Q4_K_M-layers-232/package"

Model tree for namepd/Qwen3-VL-8B-Instruct-Q4_K_M-layers

Base model

Qwen/Qwen3-VL-8B-Instruct

Quantized

Qwen/Qwen3-VL-8B-Instruct-GGUF

Quantized

(1)

this model

namepd
/

Qwen3-VL-8B-Instruct-Q4_K_M-layers

Qwen3VL-8B-Instruct-Q4_K_M

Highlights

Model Overview

Recommended Use

Quickstart

Package Variant

What Is Included

Validation

Links

Model tree for namepd/Qwen3-VL-8B-Instruct-Q4_K_M-layers