Instructions to use rdrgzlng/Qwen3.5-0.8B-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use rdrgzlng/Qwen3.5-0.8B-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="rdrgzlng/Qwen3.5-0.8B-GGUF",
	filename="Qwen3.5-0.8B-Q4_K_M.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 rdrgzlng/Qwen3.5-0.8B-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 rdrgzlng/Qwen3.5-0.8B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf rdrgzlng/Qwen3.5-0.8B-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 rdrgzlng/Qwen3.5-0.8B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf rdrgzlng/Qwen3.5-0.8B-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 rdrgzlng/Qwen3.5-0.8B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf rdrgzlng/Qwen3.5-0.8B-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 rdrgzlng/Qwen3.5-0.8B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf rdrgzlng/Qwen3.5-0.8B-GGUF:Q4_K_M

Use Docker

docker model run hf.co/rdrgzlng/Qwen3.5-0.8B-GGUF:Q4_K_M

LM Studio
Jan
Ollama
How to use rdrgzlng/Qwen3.5-0.8B-GGUF with Ollama:
```
ollama run hf.co/rdrgzlng/Qwen3.5-0.8B-GGUF:Q4_K_M
```

Unsloth Studio

How to use rdrgzlng/Qwen3.5-0.8B-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 rdrgzlng/Qwen3.5-0.8B-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 rdrgzlng/Qwen3.5-0.8B-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for rdrgzlng/Qwen3.5-0.8B-GGUF to start chatting

How to use rdrgzlng/Qwen3.5-0.8B-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf rdrgzlng/Qwen3.5-0.8B-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": "rdrgzlng/Qwen3.5-0.8B-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use rdrgzlng/Qwen3.5-0.8B-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 rdrgzlng/Qwen3.5-0.8B-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 rdrgzlng/Qwen3.5-0.8B-GGUF:Q4_K_M

Run Hermes

hermes

Atomic Chat new
Docker Model Runner
How to use rdrgzlng/Qwen3.5-0.8B-GGUF with Docker Model Runner:
```
docker model run hf.co/rdrgzlng/Qwen3.5-0.8B-GGUF:Q4_K_M
```

Lemonade

How to use rdrgzlng/Qwen3.5-0.8B-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull rdrgzlng/Qwen3.5-0.8B-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Qwen3.5-0.8B-GGUF-Q4_K_M

List all available models

lemonade list

Qwen3.5-0.8B-GGUF / README.md

rdrgzlng

Upload folder using huggingface_hub

e8e652f verified about 2 months ago

preview code

Raw

History Blame Contribute Delete

904 Bytes

	---
	license: apache-2.0
	base_model: Qwen/Qwen3.5-0.8B
	language:
	- multilingual
	library_name: llama.cpp
	tags:
	- gguf
	- llama.cpp
	- quantized
	- multimodal
	- vision
	---

	# Qwen3.5-0.8B GGUF

	Conversión propia de `Qwen/Qwen3.5-0.8B` a GGUF para uso con `llama.cpp`.

	## Archivos

	- `Qwen3.5-0.8B-fp16.gguf`
	- `Qwen3.5-0.8B-Q4_K_M.gguf`

	## Notas

	El archivo `Q4_K_M` fue cuantizado con `llama.cpp`.
	Algunos tensores requirieron fallback automático a otros formatos de cuantización por restricciones de forma.

	## Características del modelo

	- Parámetros: 0.8B
	- Arquitectura: híbrida Gated DeltaNet + atención completa (6 × bloques alternados)
	- Contexto: 262 144 tokens (256K)
	- Multimodal: soporta entrada de texto e imagen
	- Idiomas: 201 idiomas y dialectos
	- Licencia: Apache 2.0

	## Uso con llama.cpp

	```bash
	./llama-cli -m Qwen3.5-0.8B-Q4_K_M.gguf -cnv
	```