Instructions to use QuantFactory/L3-Aethora-15B-V2-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use QuantFactory/L3-Aethora-15B-V2-GGUF with Transformers:

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

pipe = pipeline("text-generation", model="QuantFactory/L3-Aethora-15B-V2-GGUF")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("QuantFactory/L3-Aethora-15B-V2-GGUF", dtype="auto")

llama-cpp-python

How to use QuantFactory/L3-Aethora-15B-V2-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="QuantFactory/L3-Aethora-15B-V2-GGUF",
	filename="L3-Aethora-15B-V2.Q2_K.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 QuantFactory/L3-Aethora-15B-V2-GGUF with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf QuantFactory/L3-Aethora-15B-V2-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf QuantFactory/L3-Aethora-15B-V2-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf QuantFactory/L3-Aethora-15B-V2-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf QuantFactory/L3-Aethora-15B-V2-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 QuantFactory/L3-Aethora-15B-V2-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf QuantFactory/L3-Aethora-15B-V2-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 QuantFactory/L3-Aethora-15B-V2-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf QuantFactory/L3-Aethora-15B-V2-GGUF:Q4_K_M

Use Docker

docker model run hf.co/QuantFactory/L3-Aethora-15B-V2-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use QuantFactory/L3-Aethora-15B-V2-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "QuantFactory/L3-Aethora-15B-V2-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": "QuantFactory/L3-Aethora-15B-V2-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/QuantFactory/L3-Aethora-15B-V2-GGUF:Q4_K_M

SGLang

How to use QuantFactory/L3-Aethora-15B-V2-GGUF 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 "QuantFactory/L3-Aethora-15B-V2-GGUF" \
    --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": "QuantFactory/L3-Aethora-15B-V2-GGUF",
		"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 "QuantFactory/L3-Aethora-15B-V2-GGUF" \
        --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": "QuantFactory/L3-Aethora-15B-V2-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use QuantFactory/L3-Aethora-15B-V2-GGUF with Ollama:
```
ollama run hf.co/QuantFactory/L3-Aethora-15B-V2-GGUF:Q4_K_M
```

Unsloth Studio new

How to use QuantFactory/L3-Aethora-15B-V2-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 QuantFactory/L3-Aethora-15B-V2-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 QuantFactory/L3-Aethora-15B-V2-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for QuantFactory/L3-Aethora-15B-V2-GGUF to start chatting

Docker Model Runner
How to use QuantFactory/L3-Aethora-15B-V2-GGUF with Docker Model Runner:
```
docker model run hf.co/QuantFactory/L3-Aethora-15B-V2-GGUF:Q4_K_M
```

Lemonade

How to use QuantFactory/L3-Aethora-15B-V2-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull QuantFactory/L3-Aethora-15B-V2-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.L3-Aethora-15B-V2-GGUF-Q4_K_M

List all available models

lemonade list

QuantFactory/L3-Aethora-15B-V2-GGUF

This is quantized version of ZeusLabs/L3-Aethora-15B-V2 created using llama.cpp

Model Description

L3-Aethora-15B v2 Data Card

Presented by:

Creators: ZeusLabs

Dataset: Theskullery/Aether-Lite-V1.8.1

Trained: 4 x A100 for 17.5 hours on 125k samples

Sponsored by: Garg (@g4rg)

About L3-Aethora-15B v2:

 L3 = Llama3

L3-Aethora-15B v2 is an advanced language model built upon the Llama 3 architecture. It employs state-of-the-art training techniques and a curated dataset to deliver enhanced performance across a wide range of tasks.

(Thank you all for the interest! the model has surpassed 150k downloads on all formats!)

Quants:

GGUF:

@Mradermacher: L3-Aethora-15B-V2-GGUF
@Bullerwins: L3-Aethora-15B-V2-GGUF

IMatrix-GGUF:

@Mradermacher: L3-Aethora-15B-V2-i1-GGUF

GGUF-F16: (both f16.q6 and f16.q5 are smaller than q8 and perform as well as the pure f16)

@MZeroWw: L3-Aethora-15B-V2-GGUF-f16

EXL2:

@Bullerwins: L3-Aethora-15B-V2-EXL2

Training Process:

Base Model: elinas/Llama-3-15B-Instruct-zeroed
Training Duration: 17.5 hours on 4 x A100 GPUs
Training Method: LoRA (Low-Rank Adaptation)
Epochs: 4
Precision: BF16
Sequence Length: 8192 tokens

Model Capabilities:

The goal of L3-Aethora-15B v2 is to have an expanded proficiency across a wide spectrum of tasks with a focus in creative writing:

Creative Writing and Storytelling:
- Generates engaging narratives, poetry, and creative content
- Adapts writing style to various genres and tones
- Assists in plot development and character creation
General Intelligence:
- Engages in detailed discussions on medical topics and scientific concepts
- Explains complex scientific phenomena
- Assists in literature review and hypothesis generation
Instructional and Educational Content:
- Creates comprehensive tutorials and how-to guides
- Explains complex topics with clarity and appropriate depth
- Generates educational materials for various skill levels
Reasoning and Problem-Solving:
- Analyzes complex scenarios and provides logical solutions
- Engages in step-by-step problem-solving across various domains
- Offers multiple perspectives on challenging issues
Contextual Understanding and Adaptability:
- Maintains coherent, context-aware conversations across extended interactions
- Adapts communication style based on the user's preferences and needs
- Handles nuanced queries with appropriate depth and sensitivity

Dataset Creation Process:

The Aether-Lite-V1.8.1 dataset used for training L3-Aethora-15B v2 underwent a rigorous creation and curation process:

Data Collection: Aggregated from 12 diverse high-quality datasets, including:
- jondurbin/airoboros-3.2
- jtatman/medical-sci-instruct-100k-sharegpt
- Doctor-Shotgun/no-robots-sharegpt
- QuietImpostor/Sao10K-Claude-3-Opus-Instruct-15K-ShareGPT
- TheSkullery/WizardLM_evol_instruct_v2_Filtered_Fuzzy_Dedup_ShareGPT
- TheSkullery/Gryphe-Opus-WritingPrompts-merged
- Alignment-Lab-AI/RPGuild-sharegpt-filtered
- And others, providing a rich mix of instruction, creative writing, and specialized knowledge
Data Preprocessing:
- Language Detection: Utilized a FastText language model to ensure English-language content
- Text Sanitization: Cleaned and normalized text, removing or replacing problematic characters
- Phrase Filtering: Removed specific unwanted phrases and content types
Deduplication:
- Implemented advanced fuzzy deduplication with a 95% similarity threshold
- Utilized text embeddings and cosine similarity calculations for efficient comparison
- Removed 16,250 duplicate entries, ensuring dataset uniqueness
Data Balancing:
- Carefully sampled from each source dataset to maintain diversity
- Implemented data shuffling to ensure random distribution of samples

The final dataset comprises 125,119 high-quality, diverse samples, striking a balance between creativity, practical knowledge, and intellectual depth.

The full dataset used has been released to the public and is avalible for all (see presented section), any ideas or recomendations are always welcome to expand on the dataset further

Downloads last month: 176

GGUF

Model size

15B params

Architecture

llama

Hardware compatibility

2-bit

3-bit

4-bit

5-bit

6-bit

8-bit

Model tree for QuantFactory/L3-Aethora-15B-V2-GGUF

Base model

meta-llama/Meta-Llama-3-8B-Instruct

Finetuned

elinas/Llama-3-15B-Instruct-zeroed

Finetuned

SteelStorage/L3-Aethora-15B-V2

Quantized

(11)

this model

QuantFactory
/

L3-Aethora-15B-V2-GGUF