Instructions to use SerialKicked/QwentileLambda2.5-32B-Instruct-GGUF-IQ4_NL with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use SerialKicked/QwentileLambda2.5-32B-Instruct-GGUF-IQ4_NL with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="SerialKicked/QwentileLambda2.5-32B-Instruct-GGUF-IQ4_NL")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("SerialKicked/QwentileLambda2.5-32B-Instruct-GGUF-IQ4_NL", dtype="auto")

llama-cpp-python

How to use SerialKicked/QwentileLambda2.5-32B-Instruct-GGUF-IQ4_NL with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="SerialKicked/QwentileLambda2.5-32B-Instruct-GGUF-IQ4_NL",
	filename="QwentileLambda2.5-32B-Instruct-IQ4_NL.gguf",
)

llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": "What is the capital of France?"
		}
	]
)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use SerialKicked/QwentileLambda2.5-32B-Instruct-GGUF-IQ4_NL with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf SerialKicked/QwentileLambda2.5-32B-Instruct-GGUF-IQ4_NL:IQ4_NL
# Run inference directly in the terminal:
llama-cli -hf SerialKicked/QwentileLambda2.5-32B-Instruct-GGUF-IQ4_NL:IQ4_NL

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf SerialKicked/QwentileLambda2.5-32B-Instruct-GGUF-IQ4_NL:IQ4_NL
# Run inference directly in the terminal:
llama-cli -hf SerialKicked/QwentileLambda2.5-32B-Instruct-GGUF-IQ4_NL:IQ4_NL

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 SerialKicked/QwentileLambda2.5-32B-Instruct-GGUF-IQ4_NL:IQ4_NL
# Run inference directly in the terminal:
./llama-cli -hf SerialKicked/QwentileLambda2.5-32B-Instruct-GGUF-IQ4_NL:IQ4_NL

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 SerialKicked/QwentileLambda2.5-32B-Instruct-GGUF-IQ4_NL:IQ4_NL
# Run inference directly in the terminal:
./build/bin/llama-cli -hf SerialKicked/QwentileLambda2.5-32B-Instruct-GGUF-IQ4_NL:IQ4_NL

Use Docker

docker model run hf.co/SerialKicked/QwentileLambda2.5-32B-Instruct-GGUF-IQ4_NL:IQ4_NL

LM Studio
Jan

vLLM

How to use SerialKicked/QwentileLambda2.5-32B-Instruct-GGUF-IQ4_NL with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "SerialKicked/QwentileLambda2.5-32B-Instruct-GGUF-IQ4_NL"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "SerialKicked/QwentileLambda2.5-32B-Instruct-GGUF-IQ4_NL",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/SerialKicked/QwentileLambda2.5-32B-Instruct-GGUF-IQ4_NL:IQ4_NL

SGLang

How to use SerialKicked/QwentileLambda2.5-32B-Instruct-GGUF-IQ4_NL with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "SerialKicked/QwentileLambda2.5-32B-Instruct-GGUF-IQ4_NL" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "SerialKicked/QwentileLambda2.5-32B-Instruct-GGUF-IQ4_NL",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "SerialKicked/QwentileLambda2.5-32B-Instruct-GGUF-IQ4_NL" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "SerialKicked/QwentileLambda2.5-32B-Instruct-GGUF-IQ4_NL",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use SerialKicked/QwentileLambda2.5-32B-Instruct-GGUF-IQ4_NL with Ollama:
```
ollama run hf.co/SerialKicked/QwentileLambda2.5-32B-Instruct-GGUF-IQ4_NL:IQ4_NL
```

Unsloth Studio

How to use SerialKicked/QwentileLambda2.5-32B-Instruct-GGUF-IQ4_NL 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 SerialKicked/QwentileLambda2.5-32B-Instruct-GGUF-IQ4_NL 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 SerialKicked/QwentileLambda2.5-32B-Instruct-GGUF-IQ4_NL to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for SerialKicked/QwentileLambda2.5-32B-Instruct-GGUF-IQ4_NL to start chatting

How to use SerialKicked/QwentileLambda2.5-32B-Instruct-GGUF-IQ4_NL with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf SerialKicked/QwentileLambda2.5-32B-Instruct-GGUF-IQ4_NL:IQ4_NL

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": "SerialKicked/QwentileLambda2.5-32B-Instruct-GGUF-IQ4_NL:IQ4_NL"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use SerialKicked/QwentileLambda2.5-32B-Instruct-GGUF-IQ4_NL with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf SerialKicked/QwentileLambda2.5-32B-Instruct-GGUF-IQ4_NL:IQ4_NL

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 SerialKicked/QwentileLambda2.5-32B-Instruct-GGUF-IQ4_NL:IQ4_NL

Run Hermes

hermes

Docker Model Runner
How to use SerialKicked/QwentileLambda2.5-32B-Instruct-GGUF-IQ4_NL with Docker Model Runner:
```
docker model run hf.co/SerialKicked/QwentileLambda2.5-32B-Instruct-GGUF-IQ4_NL:IQ4_NL
```

Lemonade

How to use SerialKicked/QwentileLambda2.5-32B-Instruct-GGUF-IQ4_NL with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull SerialKicked/QwentileLambda2.5-32B-Instruct-GGUF-IQ4_NL:IQ4_NL

Run and chat with the model

lemonade run user.QwentileLambda2.5-32B-Instruct-GGUF-IQ4_NL-IQ4_NL

List all available models

lemonade list

IQ4_NL Quantized version of QwentileLambda2.5-32B-Instruct. A very good merge for multiple Qwen2.5 and QwQ fine-tunes.

I noticed that the IQ4_NL variant was missing in mradermacher's repo. So I'm filling the blank. It tends to behave better than Q4_K_S and Q4_K_M at slightly lower VRAM consumption.

For cards with 24GB of VRAM

IQ4_NL

It's of an ideal size to be run with 24GB VRAM at 16K to 20K context length.

Settings

Instruction Template: ChatML. You can also use CoT with ChatML-Thinker, but you need to prefill the thinking tag in that case.

Note: If your backend has a setting for it, disable the BoS token. It's set to disabled at the GGUF level, but no all backends recognize the flag.