Instructions to use QuantFactory/MN-12B-Lyra-v4-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use QuantFactory/MN-12B-Lyra-v4-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="QuantFactory/MN-12B-Lyra-v4-GGUF",
	filename="MN-12B-Lyra-v4.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/MN-12B-Lyra-v4-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/MN-12B-Lyra-v4-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf QuantFactory/MN-12B-Lyra-v4-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/MN-12B-Lyra-v4-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf QuantFactory/MN-12B-Lyra-v4-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/MN-12B-Lyra-v4-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf QuantFactory/MN-12B-Lyra-v4-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/MN-12B-Lyra-v4-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf QuantFactory/MN-12B-Lyra-v4-GGUF:Q4_K_M

Use Docker

docker model run hf.co/QuantFactory/MN-12B-Lyra-v4-GGUF:Q4_K_M

LM Studio
Jan
Ollama
How to use QuantFactory/MN-12B-Lyra-v4-GGUF with Ollama:
```
ollama run hf.co/QuantFactory/MN-12B-Lyra-v4-GGUF:Q4_K_M
```

Unsloth Studio new

How to use QuantFactory/MN-12B-Lyra-v4-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/MN-12B-Lyra-v4-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/MN-12B-Lyra-v4-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/MN-12B-Lyra-v4-GGUF to start chatting

Pi new

How to use QuantFactory/MN-12B-Lyra-v4-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf QuantFactory/MN-12B-Lyra-v4-GGUF:Q4_K_M

Configure the model in Pi

# Install Pi:
npm install -g @mariozechner/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "QuantFactory/MN-12B-Lyra-v4-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use QuantFactory/MN-12B-Lyra-v4-GGUF with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf QuantFactory/MN-12B-Lyra-v4-GGUF:Q4_K_M

Configure Hermes

# Install Hermes:
curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash
hermes setup
# Point Hermes at the local server:
hermes config set model.provider custom
hermes config set model.base_url http://127.0.0.1:8080/v1
hermes config set model.default QuantFactory/MN-12B-Lyra-v4-GGUF:Q4_K_M

Run Hermes

hermes

Docker Model Runner
How to use QuantFactory/MN-12B-Lyra-v4-GGUF with Docker Model Runner:
```
docker model run hf.co/QuantFactory/MN-12B-Lyra-v4-GGUF:Q4_K_M
```

Lemonade

How to use QuantFactory/MN-12B-Lyra-v4-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull QuantFactory/MN-12B-Lyra-v4-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.MN-12B-Lyra-v4-GGUF-Q4_K_M

List all available models

lemonade list

QuantFactory/MN-12B-Lyra-v4-GGUF

This is quantized version of Sao10K/MN-12B-Lyra-v4 created using llama.cpp

Original Model Card

Mistral-NeMo-12B-Lyra-v4, a variation of Lyra-v4a1, layered over Lyra-v3, which was built on top of Lyra-v2a2, which itself was built upon Lyra-v2a1.

Model Versioning

[See Previous Models]
  |
Lyra-v4a1
  |
  ------------> Lyra-v4 [Seperate RL Step targeting Instruct and Coherency over Base Nemo instead of SFT First, Result is Merged with Lyra-v4a1, fixes most quant-based issues. Somehow.]

This uses ChatML, or any of its variants which were included in previous versions.


<|im_start|>system
This is the system prompt.<|im_end|>
<|im_start|>user
Instructions placed here.<|im_end|>
<|im_start|>assistant
The model's response will be here.<|im_end|>
--------------------------------------------------
[INST]system
This is another system prompt.[/INST]
[INST]user
Your instructions placed here.[/INST]
[INST]assistant
The model's response will be here.[/INST]

Recommended Samplers:

Temperature: 0.6 - 1 # Make sure min_p is set before Temperature in Sampler Orders
min_p: 0.1 - 0.2 # Crucial for NeMo

Recommended Stopping Strings:

<|im_end|>
</s>
[/INST]

Notes

- I think I fixed the extra token stuff some users seem to be facing, while retaining everything else? It's some error alright.
- If you're using XML tags, you may see weird malformed stopping strings. Just add them to your current list. and move on.
- Its pretty nice, imo. I've been messing around with it a lot.
- Make sure the ChatML template is correct, I think there's some issues with the one used in SillyTavern which might cause improper replies?

Downloads last month: 256

GGUF

Model size

12B params

Architecture

llama

Hardware compatibility

2-bit

3-bit

4-bit

5-bit

6-bit

8-bit

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