Instructions to use QuantFactory/L3-OVA-Test-8B-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use QuantFactory/L3-OVA-Test-8B-GGUF with Transformers:

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("QuantFactory/L3-OVA-Test-8B-GGUF", dtype="auto")

llama-cpp-python

How to use QuantFactory/L3-OVA-Test-8B-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="QuantFactory/L3-OVA-Test-8B-GGUF",
	filename="L3-OVA-Test-8B.Q2_K.gguf",
)

llm.create_chat_completion(
	messages = "No input example has been defined for this model task."
)

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use QuantFactory/L3-OVA-Test-8B-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-OVA-Test-8B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf QuantFactory/L3-OVA-Test-8B-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-OVA-Test-8B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf QuantFactory/L3-OVA-Test-8B-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-OVA-Test-8B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf QuantFactory/L3-OVA-Test-8B-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-OVA-Test-8B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf QuantFactory/L3-OVA-Test-8B-GGUF:Q4_K_M

Use Docker

docker model run hf.co/QuantFactory/L3-OVA-Test-8B-GGUF:Q4_K_M

LM Studio
Jan
Ollama
How to use QuantFactory/L3-OVA-Test-8B-GGUF with Ollama:
```
ollama run hf.co/QuantFactory/L3-OVA-Test-8B-GGUF:Q4_K_M
```

Unsloth Studio new

How to use QuantFactory/L3-OVA-Test-8B-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-OVA-Test-8B-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-OVA-Test-8B-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-OVA-Test-8B-GGUF to start chatting

Docker Model Runner
How to use QuantFactory/L3-OVA-Test-8B-GGUF with Docker Model Runner:
```
docker model run hf.co/QuantFactory/L3-OVA-Test-8B-GGUF:Q4_K_M
```

Lemonade

How to use QuantFactory/L3-OVA-Test-8B-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull QuantFactory/L3-OVA-Test-8B-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.L3-OVA-Test-8B-GGUF-Q4_K_M

List all available models

lemonade list

QuantFactory/L3-OVA-Test-8B-GGUF

This is quantized version of Casual-Autopsy/L3-OVA-Test-8B created using llama.cpp

Original Model Card

merge

This is a merge of pre-trained language models created using mergekit.

Merge Details

Merge Method

This model was merged using the TIES merge method using meta-llama/Meta-Llama-3-8B-Instruct as a base.

Models Merged

The following models were included in the merge:

Configuration

The following YAML configuration was used to produce this model:

models:
  - model: meta-llama/Meta-Llama-3-8B-Instruct
  - model: meta-llama/Meta-Llama-3-8B-Instruct
    parameters:
      density: 0.5
      weight: 0.5
  - model: ChaoticNeutrals/Templar_v1_8B
    parameters:
      density: 0.75
      weight: [0.25, 0.0625, 0.0625, 0.0625, 0.0625]
  - model: ChaoticNeutrals/Hathor_RP-v.01-L3-8B
    parameters:
      density: 0.75
      weight: [0.0625, 0.25, 0.0625, 0.0625, 0.0625]
  - model: Sao10K/L3-8B-Stheno-v3.2
    parameters:
      density: 0.75
      weight: [0.0625, 0.0625, 0.25, 0.0625, 0.0625]
  - model: ChaoticNeutrals/Poppy_Porpoise-1.4-L3-8B
    parameters:
      density: 0.75
      weight: [0.0625, 0.0625, 0.0625, 0.25, 0.0625]
  - model: ChaoticNeutrals/Domain-Fusion-L3-8B
    parameters:
      density: 0.75
      weight: [0.0625, 0.0625, 0.0625, 0.0625, 0.25]
  - model: ChaoticNeutrals/Templar_v1_8B
    parameters:
      density: 0.25
      weight: [-0.125, -0.125, -0.125, -0.125, -0.5]
  - model: ChaoticNeutrals/Hathor_RP-v.01-L3-8B
    parameters:
      density: 0.25
      weight: [-0.125, -0.125, -0.5, -0.125, -0.125]
  - model: Sao10K/L3-8B-Stheno-v3.2
    parameters:
      density: 0.25
      weight: [-0.125, -0.5, -0.125, -0.125, -0.125]
  - model: ChaoticNeutrals/Poppy_Porpoise-1.4-L3-8B
    parameters:
      density: 0.25
      weight: [-0.125, -0.125, -0.125, -0.5, -0.125]
  - model: ChaoticNeutrals/Domain-Fusion-L3-8B
    parameters:
      density: 0.25
      weight: [-0.5, -0.125, -0.125, -0.125, -0.125]
merge_method: ties
base_model: meta-llama/Meta-Llama-3-8B-Instruct
parameters:
  normalize: false
  int8_mask: true
dtype: bfloat16