Instructions to use XeroCodes/xenith-3b-gguf with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries
PEFT
How to use XeroCodes/xenith-3b-gguf with PEFT:
```
Task type is invalid.
```

How to use XeroCodes/xenith-3b-gguf with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="XeroCodes/xenith-3b-gguf",
	filename="xenith-3b-f16.gguf",
)

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

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use XeroCodes/xenith-3b-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/xenith-3b-gguf:F16
# Run inference directly in the terminal:
llama-cli -hf XeroCodes/xenith-3b-gguf:F16

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf XeroCodes/xenith-3b-gguf:F16
# Run inference directly in the terminal:
llama-cli -hf XeroCodes/xenith-3b-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/xenith-3b-gguf:F16
# Run inference directly in the terminal:
./llama-cli -hf XeroCodes/xenith-3b-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/xenith-3b-gguf:F16
# Run inference directly in the terminal:
./build/bin/llama-cli -hf XeroCodes/xenith-3b-gguf:F16

Use Docker

docker model run hf.co/XeroCodes/xenith-3b-gguf:F16

LM Studio
Jan

vLLM

How to use XeroCodes/xenith-3b-gguf with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "XeroCodes/xenith-3b-gguf"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "XeroCodes/xenith-3b-gguf",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/XeroCodes/xenith-3b-gguf:F16

Ollama
How to use XeroCodes/xenith-3b-gguf with Ollama:
```
ollama run hf.co/XeroCodes/xenith-3b-gguf:F16
```

Unsloth Studio new

How to use XeroCodes/xenith-3b-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/xenith-3b-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/xenith-3b-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/xenith-3b-gguf to start chatting

Docker Model Runner
How to use XeroCodes/xenith-3b-gguf with Docker Model Runner:
```
docker model run hf.co/XeroCodes/xenith-3b-gguf:F16
```

Lemonade

How to use XeroCodes/xenith-3b-gguf with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull XeroCodes/xenith-3b-gguf:F16

Run and chat with the model

lemonade run user.xenith-3b-gguf-F16

List all available models

lemonade list

Xenith-3B

Xenith-3B is a fine-tuned language model based on the microsoft/Phi-3-mini-4k-instruct model. It has been specifically trained on the AlignmentLab-AI/alpaca-cot-collection dataset, which focuses on chain-of-thought reasoning and instruction following.

Model Overview

Model Name: Xenith-3B
Base Model: microsoft/Phi-3-mini-4k-instruct
Fine-Tuned On: AlignmentLab-AI/alpaca-cot-collection
Model Size: 3 Billion parameters
Architecture: Transformer-based LLM

Training Details

Objective: Fine-tune the base model to enhance its performance on tasks requiring complex reasoning and multi-step problem-solving.
Training Duration: 10 epochs
Batch Size: 8
Learning Rate: 3e-5
Optimizer: AdamW
Hardware Used: 2x NVIDIA L4 GPUs

Performance

Xenith-3B excels in tasks that require:

Chain-of-thought reasoning
Instruction following
Contextual understanding
Complex problem-solving
The model has shown significant improvements in these areas compared to the base model.

Downloads last month: 15

GGUF

Model size

4B params

Architecture

llama

Hardware compatibility

8-bit

16-bit

32-bit

Model tree for XeroCodes/xenith-3b-gguf

Base model

microsoft/Phi-3-mini-4k-instruct

Adapter

(840)

this model

XeroCodes
/

xenith-3b-gguf

Xenith-3B

Model Overview

Training Details

Performance

Model tree for XeroCodes/xenith-3b-gguf

Dataset used to train XeroCodes/xenith-3b-gguf