Instructions to use QuantFactory/MN-12B-Lyra-v3-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use QuantFactory/MN-12B-Lyra-v3-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-v3-GGUF",
	filename="MN-12B-Lyra-v3.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-v3-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-v3-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf QuantFactory/MN-12B-Lyra-v3-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-v3-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf QuantFactory/MN-12B-Lyra-v3-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-v3-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf QuantFactory/MN-12B-Lyra-v3-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-v3-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf QuantFactory/MN-12B-Lyra-v3-GGUF:Q4_K_M

Use Docker

docker model run hf.co/QuantFactory/MN-12B-Lyra-v3-GGUF:Q4_K_M

LM Studio
Jan
Ollama
How to use QuantFactory/MN-12B-Lyra-v3-GGUF with Ollama:
```
ollama run hf.co/QuantFactory/MN-12B-Lyra-v3-GGUF:Q4_K_M
```

Unsloth Studio new

How to use QuantFactory/MN-12B-Lyra-v3-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-v3-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-v3-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-v3-GGUF to start chatting

Docker Model Runner
How to use QuantFactory/MN-12B-Lyra-v3-GGUF with Docker Model Runner:
```
docker model run hf.co/QuantFactory/MN-12B-Lyra-v3-GGUF:Q4_K_M
```

Lemonade

How to use QuantFactory/MN-12B-Lyra-v3-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull QuantFactory/MN-12B-Lyra-v3-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.MN-12B-Lyra-v3-GGUF-Q4_K_M

List all available models

lemonade list

QuantFactory/MN-12B-Lyra-v3-GGUF

This is quantized version of Sao10K/MN-12B-Lyra-v3 created using llama.cpp

Original Model Card

NEW V4!

fixes issues some were having?

lyra-v4

Automatic Approval

If you agree, please place future merges / derivatives under cc-by-nc-4.0 license. ty

Mistral-NeMo-12B-Lyra-v3, built on top of Lyra-v2a2, which itself was built upon Lyra-v2a1.

Model Versioning

Lyra-v1 [Merge of Custom Roleplay & Instruct Trains, on Different Formats]
  |
  | [Additional SFT on 10% of Previous Data, Mixed]
  v
Lyra-v2a1 
  |
  | [Low Rank SFT Step + Tokenizer Diddling]
  v
Lyra-v2a2
  |
  | [RL Step Performed on Multiturn Sets, Magpie-style Responses by Lyra-v2a2 for Rejected Data]
  v
Lyra-v3

This uses a custom ChatML-style prompting Format!

-> What can go wrong?

[INST]system
This is the system prompt.[/INST]
[INST]user
Instructions placed here.[/INST]
[INST]assistant
The model's response will be here.[/INST]

Why this? I had used the wrong configs by accident. The format was meant for an 8B pruned NeMo train, instead it went to this. Oops.

Recommended Samplers:

Temperature: 0.7 - 1.2
min_p: 0.1 - 0.2 # Crucial for NeMo

Recommended Stopping Strings:

<|im_end|>
</s>

Blame messed up Training Configs, oops?

Training Metrics:

- Trained on 4xH100 SXM for 6 Hours.
- Trained for 2 Epochs.
- Effective Global Batch Size: 128.
- Dataset Used: A custom, cleaned mix of Stheno-v3.4's Dataset, focused mainly on multiturn.

Extras

Image Source: AI-Generated with FLUX.1 Dev.

have a nice day.

Downloads last month: 27

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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