Instructions to use bartowski/labradorite-13b-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 bartowski/labradorite-13b-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 bartowski/labradorite-13b-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf bartowski/labradorite-13b-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 bartowski/labradorite-13b-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf bartowski/labradorite-13b-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 bartowski/labradorite-13b-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf bartowski/labradorite-13b-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 bartowski/labradorite-13b-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf bartowski/labradorite-13b-GGUF:Q4_K_M

Use Docker

docker model run hf.co/bartowski/labradorite-13b-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use bartowski/labradorite-13b-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "bartowski/labradorite-13b-GGUF"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "bartowski/labradorite-13b-GGUF",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/bartowski/labradorite-13b-GGUF:Q4_K_M

Ollama
How to use bartowski/labradorite-13b-GGUF with Ollama:
```
ollama run hf.co/bartowski/labradorite-13b-GGUF:Q4_K_M
```
Unsloth Desktop
Docker Model Runner
How to use bartowski/labradorite-13b-GGUF with Docker Model Runner:
```
docker model run hf.co/bartowski/labradorite-13b-GGUF:Q4_K_M
```

Lemonade

How to use bartowski/labradorite-13b-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull bartowski/labradorite-13b-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.labradorite-13b-GGUF-Q4_K_M

List all available models

lemonade list

Atomic Chat

labradorite-13b-GGUF / README.md

bartowski

Llamacpp quants

0969b3f verified over 2 years ago

preview code

Raw

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	---
	pipeline_tag: text-generation
	tags:
	- labradorite
	- llama
	- llama-2
	- ibm
	- lab
	- labrador
	- merlinite
	license: llama2
	license_link: https://ai.meta.com/llama/license/
	language:
	- en
	quantized_by: bartowski
	---

	## Llamacpp Quantizations of labradorite-13b

	Using <a href="https://github.com/ggerganov/llama.cpp/commit/fa974646e1a2024fc7dc9e6f27cf1f2f5d4a3763">llama.cpp commit fa97464</a> for quantization.

	Original model: https://huggingface.co/ibm/labradorite-13b

	Download a file (not the whole branch) from below:

	\| Filename \| Quant type \| File Size \| Description \|
	\| -------- \| ---------- \| --------- \| ----------- \|
	\| [labradorite-13b-Q8_0.gguf](https://huggingface.co/bartowski/labradorite-13b-GGUF/blob/main/labradorite-13b-Q8_0.gguf) \| Q8_0 \| 13.83GB \| Extremely high quality, generally unneeded but max available quant. \|
	\| [labradorite-13b-Q6_K.gguf](https://huggingface.co/bartowski/labradorite-13b-GGUF/blob/main/labradorite-13b-Q6_K.gguf) \| Q6_K \| 10.67GB \| Very high quality, near perfect, recommended. \|
	\| [labradorite-13b-Q5_K_M.gguf](https://huggingface.co/bartowski/labradorite-13b-GGUF/blob/main/labradorite-13b-Q5_K_M.gguf) \| Q5_K_M \| 9.22GB \| High quality, very usable. \|
	\| [labradorite-13b-Q5_K_S.gguf](https://huggingface.co/bartowski/labradorite-13b-GGUF/blob/main/labradorite-13b-Q5_K_S.gguf) \| Q5_K_S \| 8.97GB \| High quality, very usable. \|
	\| [labradorite-13b-Q5_0.gguf](https://huggingface.co/bartowski/labradorite-13b-GGUF/blob/main/labradorite-13b-Q5_0.gguf) \| Q5_0 \| 8.97GB \| High quality, older format, generally not recommended. \|
	\| [labradorite-13b-Q4_K_M.gguf](https://huggingface.co/bartowski/labradorite-13b-GGUF/blob/main/labradorite-13b-Q4_K_M.gguf) \| Q4_K_M \| 7.86GB \| Good quality, similar to 4.25 bpw. \|
	\| [labradorite-13b-Q4_K_S.gguf](https://huggingface.co/bartowski/labradorite-13b-GGUF/blob/main/labradorite-13b-Q4_K_S.gguf) \| Q4_K_S \| 7.42GB \| Slightly lower quality with small space savings. \|
	\| [labradorite-13b-Q4_0.gguf](https://huggingface.co/bartowski/labradorite-13b-GGUF/blob/main/labradorite-13b-Q4_0.gguf) \| Q4_0 \| 7.36GB \| Decent quality, older format, generally not recommended. \|
	\| [labradorite-13b-Q3_K_L.gguf](https://huggingface.co/bartowski/labradorite-13b-GGUF/blob/main/labradorite-13b-Q3_K_L.gguf) \| Q3_K_L \| 6.92GB \| Lower quality but usable, good for low RAM availability. \|
	\| [labradorite-13b-Q3_K_M.gguf](https://huggingface.co/bartowski/labradorite-13b-GGUF/blob/main/labradorite-13b-Q3_K_M.gguf) \| Q3_K_M \| 6.33GB \| Even lower quality. \|
	\| [labradorite-13b-Q3_K_S.gguf](https://huggingface.co/bartowski/labradorite-13b-GGUF/blob/main/labradorite-13b-Q3_K_S.gguf) \| Q3_K_S \| 5.65GB \| Low quality, not recommended. \|
	\| [labradorite-13b-Q2_K.gguf](https://huggingface.co/bartowski/labradorite-13b-GGUF/blob/main/labradorite-13b-Q2_K.gguf) \| Q2_K \| 4.85GB \| Extremely low quality, not recommended.

	Want to support my work? Visit my ko-fi page here: https://ko-fi.com/bartowski