Instructions to use gelukuMLG/Llama-3-Cat-Instruct-15B-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use gelukuMLG/Llama-3-Cat-Instruct-15B-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 gelukuMLG/Llama-3-Cat-Instruct-15B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf gelukuMLG/Llama-3-Cat-Instruct-15B-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 gelukuMLG/Llama-3-Cat-Instruct-15B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf gelukuMLG/Llama-3-Cat-Instruct-15B-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 gelukuMLG/Llama-3-Cat-Instruct-15B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf gelukuMLG/Llama-3-Cat-Instruct-15B-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 gelukuMLG/Llama-3-Cat-Instruct-15B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf gelukuMLG/Llama-3-Cat-Instruct-15B-GGUF:Q4_K_M

Use Docker

docker model run hf.co/gelukuMLG/Llama-3-Cat-Instruct-15B-GGUF:Q4_K_M

LM Studio
Jan
Ollama
How to use gelukuMLG/Llama-3-Cat-Instruct-15B-GGUF with Ollama:
```
ollama run hf.co/gelukuMLG/Llama-3-Cat-Instruct-15B-GGUF:Q4_K_M
```

Unsloth Studio

How to use gelukuMLG/Llama-3-Cat-Instruct-15B-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 gelukuMLG/Llama-3-Cat-Instruct-15B-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 gelukuMLG/Llama-3-Cat-Instruct-15B-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for gelukuMLG/Llama-3-Cat-Instruct-15B-GGUF to start chatting

Docker Model Runner
How to use gelukuMLG/Llama-3-Cat-Instruct-15B-GGUF with Docker Model Runner:
```
docker model run hf.co/gelukuMLG/Llama-3-Cat-Instruct-15B-GGUF:Q4_K_M
```

Lemonade

How to use gelukuMLG/Llama-3-Cat-Instruct-15B-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull gelukuMLG/Llama-3-Cat-Instruct-15B-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Llama-3-Cat-Instruct-15B-GGUF-Q4_K_M

List all available models

lemonade list

Atomic Chat

Compute for this merge was provided by KoboldAI.

Important: Because this model is based on Cat-8B-Instruct-V1 it has the stop sequence issues. Make sure to add `</s>` as a stop Sequence in whatever backend or ui you are using.

The following models were used in this recipe:

Recipe used:

merge_method: passthrough
dtype: bfloat16
vocab_type: bpe
slices:
- sources:
  - layer_range: [0, 24]
    model: TheSkullery/llama-3-cat-8b-instruct-v1
- sources:
  - layer_range: [8, 24]
    model: TheSkullery/llama-3-cat-8b-instruct-v1
    parameters:
      scale:
      - filter: o_proj
        value: 0.0
      - filter: down_proj
        value: 0.0
      - value: 1.0
- sources:
  - layer_range: [8, 24]
    model: TheSkullery/llama-3-cat-8b-instruct-v1
    parameters:
      scale:
      - filter: o_proj
        value: 0.0
      - filter: down_proj
        value: 0.0
      - value: 1.0
- sources:
  - layer_range: [24, 32]
    model: TheSkullery/llama-3-cat-8b-instruct-v1
name: LLaMa-3-Cat-Instruct-Unhealed-15B
---

merge_method: task_arithmetic
dtype: bfloat16
vocab_type: bpe
base_model: elinas/Llama-3-15B-Instruct-zeroed
models:
  - model: elinas/Llama-3-15B-Instruct-zeroed-ft
    parameters:
      weight: 1.0
  - model: LLaMa-3-Cat-Instruct-Unhealed-15B
    parameters:
      weight: 1.0

Downloads last month: 72

GGUF

Model size

15B params

Architecture

llama

Hardware compatibility

4-bit

5-bit

6-bit

8-bit

View +1 variant

Inference Providers NEW

This model isn't deployed by any Inference Provider. 🙋 Ask for provider support

Compute for this merge was provided by KoboldAI.

Important: Because this model is based on Cat-8B-Instruct-V1 it has the stop sequence issues. Make sure to add </s> as a stop Sequence in whatever backend or ui you are using.

Important: Because this model is based on Cat-8B-Instruct-V1 it has the stop sequence issues. Make sure to add `</s>` as a stop Sequence in whatever backend or ui you are using.