Instructions to use brittlewis12/Beagle14-7B-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use brittlewis12/Beagle14-7B-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="brittlewis12/Beagle14-7B-GGUF",
	filename="beagle14-7b.Q2_K.gguf",
)

output = llm(
	"Once upon a time,",
	max_tokens=512,
	echo=True
)
print(output)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use brittlewis12/Beagle14-7B-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 brittlewis12/Beagle14-7B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf brittlewis12/Beagle14-7B-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 brittlewis12/Beagle14-7B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf brittlewis12/Beagle14-7B-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 brittlewis12/Beagle14-7B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf brittlewis12/Beagle14-7B-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 brittlewis12/Beagle14-7B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf brittlewis12/Beagle14-7B-GGUF:Q4_K_M

Use Docker

docker model run hf.co/brittlewis12/Beagle14-7B-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use brittlewis12/Beagle14-7B-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "brittlewis12/Beagle14-7B-GGUF"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "brittlewis12/Beagle14-7B-GGUF",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/brittlewis12/Beagle14-7B-GGUF:Q4_K_M

Ollama
How to use brittlewis12/Beagle14-7B-GGUF with Ollama:
```
ollama run hf.co/brittlewis12/Beagle14-7B-GGUF:Q4_K_M
```

Unsloth Studio

How to use brittlewis12/Beagle14-7B-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 brittlewis12/Beagle14-7B-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 brittlewis12/Beagle14-7B-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for brittlewis12/Beagle14-7B-GGUF to start chatting

Atomic Chat new
Docker Model Runner
How to use brittlewis12/Beagle14-7B-GGUF with Docker Model Runner:
```
docker model run hf.co/brittlewis12/Beagle14-7B-GGUF:Q4_K_M
```

Lemonade

How to use brittlewis12/Beagle14-7B-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull brittlewis12/Beagle14-7B-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Beagle14-7B-GGUF-Q4_K_M

List all available models

lemonade list

Beagle14-7B GGUF

Original model: Beagle14-7B Model creator: Maxime Labonne

This repo contains GGUF format model files for Maxime Labonne’s Beagle14-7B.

Beagle14-7B is a merge of the following models using LazyMergekit:

What is GGUF?

GGUF is a file format for representing AI models. It is the third version of the format, introduced by the llama.cpp team on August 21st 2023. It is a replacement for GGML, which is no longer supported by llama.cpp. Converted using llama.cpp build 1879 (revision 3e5ca79)

Prompt template: Zephyr

Zephyr-style appears to work well!

<|system|>
{{system_message}}</s>
<|user|>
{{prompt}}</s>
<|assistant|>

Download & run with cnvrs on iPhone, iPad, and Mac!

cnvrs is the best app for private, local AI on your device:

create & save Characters with custom system prompts & temperature settings
download and experiment with any GGUF model you can find on HuggingFace!
make it your own with custom Theme colors
powered by Metal ⚡️ & Llama.cpp, with haptics during response streaming!
try it out yourself today, on Testflight!
follow cnvrs on twitter to stay up to date

Original Model Evaluations:

The evaluation was performed by the model’s creator using LLM AutoEval on Nous suite, as reported from mlabonne’s alternative leaderboard, YALL: Yet Another LLM Leaderboard.

Model	AGIEval	GPT4All	TruthfulQA	Bigbench	Average
Beagle14-7B	44.38	76.53	69.44	47.25	59.4
OpenHermes-2.5-Mistral-7B	42.75	72.99	52.99	40.94	52.42
NeuralHermes-2.5-Mistral-7B	43.67	73.24	55.37	41.76	53.51
Nous-Hermes-2-SOLAR-10.7B	47.79	74.69	55.92	44.84	55.81
Marcoro14-7B-slerp	44.66	76.24	64.15	45.64	57.67
CatMarcoro14-7B-slerp	45.21	75.91	63.81	47.31	58.06

Downloads last month: 52

GGUF

Model size

7B params

Architecture

llama

Hardware compatibility

2-bit

3-bit

4-bit

5-bit

6-bit

Model tree for brittlewis12/Beagle14-7B-GGUF

Base model

mlabonne/Beagle14-7B

Quantized

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