Instructions to use XeroCodes/xero-2b-gguf with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries
PEFT
How to use XeroCodes/xero-2b-gguf with PEFT:
```
Task type is invalid.
```

How to use XeroCodes/xero-2b-gguf with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="XeroCodes/xero-2b-gguf",
	filename="xero-2b-f16.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 XeroCodes/xero-2b-gguf with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf XeroCodes/xero-2b-gguf:F16
# Run inference directly in the terminal:
llama-cli -hf XeroCodes/xero-2b-gguf:F16

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf XeroCodes/xero-2b-gguf:F16
# Run inference directly in the terminal:
llama-cli -hf XeroCodes/xero-2b-gguf: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 XeroCodes/xero-2b-gguf:F16
# Run inference directly in the terminal:
./llama-cli -hf XeroCodes/xero-2b-gguf: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 XeroCodes/xero-2b-gguf:F16
# Run inference directly in the terminal:
./build/bin/llama-cli -hf XeroCodes/xero-2b-gguf:F16

Use Docker

docker model run hf.co/XeroCodes/xero-2b-gguf:F16

LM Studio
Jan

vLLM

How to use XeroCodes/xero-2b-gguf with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "XeroCodes/xero-2b-gguf"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "XeroCodes/xero-2b-gguf",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/XeroCodes/xero-2b-gguf:F16

Ollama
How to use XeroCodes/xero-2b-gguf with Ollama:
```
ollama run hf.co/XeroCodes/xero-2b-gguf:F16
```

Unsloth Studio

How to use XeroCodes/xero-2b-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 XeroCodes/xero-2b-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 XeroCodes/xero-2b-gguf to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for XeroCodes/xero-2b-gguf to start chatting

Docker Model Runner
How to use XeroCodes/xero-2b-gguf with Docker Model Runner:
```
docker model run hf.co/XeroCodes/xero-2b-gguf:F16
```

Lemonade

How to use XeroCodes/xero-2b-gguf with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull XeroCodes/xero-2b-gguf:F16

Run and chat with the model

lemonade run user.xero-2b-gguf-F16

List all available models

lemonade list

Xero-2B

Xero-2B is a fine-tuned language model based on google/gemma-2b-it, optimized to excel as a conversational agent. This model has been fine-tuned on the yahma/alpaca-cleaned dataset to enhance its conversational abilities and comprehension, making it an excellent choice for applications requiring advanced dialogue capabilities and understanding.

Overview

Xero-2B builds upon the strong foundation of the Gemma-2B-Instruct model, enhancing its conversational skills and ability to understand and respond to complex queries. The fine-tuning process with the yahma/alpaca-cleaned dataset ensures that Xero-2B can handle diverse and intricate conversations with ease.

Features

Enhanced Conversational Abilities: Improved performance in generating natural and coherent dialogues.
Better Understanding: Enhanced comprehension skills, allowing for more accurate and context-aware responses.
Versatile Dialogue Management: Capable of handling a wide range of topics and maintaining context over extended conversations.

Downloads last month: 16

GGUF

Model size

3B params

Architecture

gemma

Hardware compatibility

8-bit

16-bit

32-bit

Model tree for XeroCodes/xero-2b-gguf

Base model

google/gemma-2b-it

Adapter

(674)

this model

XeroCodes
/

xero-2b-gguf

Xero-2B

Overview

Features

Model tree for XeroCodes/xero-2b-gguf

Dataset used to train XeroCodes/xero-2b-gguf