Instructions to use QuantFactory/Nemomix-v2.0-12B-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use QuantFactory/Nemomix-v2.0-12B-GGUF with Transformers:

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("QuantFactory/Nemomix-v2.0-12B-GGUF", dtype="auto")

llama-cpp-python

How to use QuantFactory/Nemomix-v2.0-12B-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="QuantFactory/Nemomix-v2.0-12B-GGUF",
	filename="Nemomix-v2.0-12B.Q2_K.gguf",
)

output = llm(
	"Once upon a time,",
	max_tokens=512,
	echo=True
)
print(output)

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use QuantFactory/Nemomix-v2.0-12B-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/Nemomix-v2.0-12B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf QuantFactory/Nemomix-v2.0-12B-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/Nemomix-v2.0-12B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf QuantFactory/Nemomix-v2.0-12B-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/Nemomix-v2.0-12B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf QuantFactory/Nemomix-v2.0-12B-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/Nemomix-v2.0-12B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf QuantFactory/Nemomix-v2.0-12B-GGUF:Q4_K_M

Use Docker

docker model run hf.co/QuantFactory/Nemomix-v2.0-12B-GGUF:Q4_K_M

LM Studio
Jan
Ollama
How to use QuantFactory/Nemomix-v2.0-12B-GGUF with Ollama:
```
ollama run hf.co/QuantFactory/Nemomix-v2.0-12B-GGUF:Q4_K_M
```

Unsloth Studio new

How to use QuantFactory/Nemomix-v2.0-12B-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/Nemomix-v2.0-12B-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/Nemomix-v2.0-12B-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/Nemomix-v2.0-12B-GGUF to start chatting

Docker Model Runner
How to use QuantFactory/Nemomix-v2.0-12B-GGUF with Docker Model Runner:
```
docker model run hf.co/QuantFactory/Nemomix-v2.0-12B-GGUF:Q4_K_M
```

Lemonade

How to use QuantFactory/Nemomix-v2.0-12B-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull QuantFactory/Nemomix-v2.0-12B-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Nemomix-v2.0-12B-GGUF-Q4_K_M

List all available models

lemonade list

QuantFactory/Nemomix-v2.0-12B-GGUF

This is quantized version of MarinaraSpaghetti/Nemomix-v2.0-12B created using llama.cpp

Original Model Card

V4.0 is the best one, use that one.

Information

Description

My main goal is to merge the smartness of the base Instruct Nemo with the better prose from the different roleplaying fine-tunes. This is version v0.2, still to be tested. Not sure if it's better than v1.0. All credits and thanks go to Intervitens, Mistralai, and NeverSleep for providing amazing models used in the merge.

Instruct

Mistral Instruct.

<s>[INST] {system} [/INST]{assistant}</s>[INST] {user} [/INST]

Settings

Lower Temperature of 0.35 recommended, although I had luck with Temperatures above one (1.0-1.2) if you crank up the Min P (0.01-0.1). Run with base DRY of 0.8/1.75/2/0 and you're good to go.

GGUF

https://huggingface.co/MarinaraSpaghetti/Nemomix-v2.0-12B-GGUF

Other Versions

V1: https://huggingface.co/MarinaraSpaghetti/Nemomix-v1.0-12B

V2: https://huggingface.co/MarinaraSpaghetti/Nemomix-v2.0-12B

V3: https://huggingface.co/MarinaraSpaghetti/Nemomix-v3.0-12B

V4: https://huggingface.co/MarinaraSpaghetti/Nemomix-v4.0-12B

Nemomix-v2.0-12B

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

Merge Details

Merge Method

This model was merged using the Model Stock merge method using F:\mergekit\mistralaiMistral-Nemo-Base-2407 as a base.

Models Merged

The following models were included in the merge:

F:\mergekit\intervitens_mini-magnum-12b-v1.1
F:\mergekit\NeverSleep_Lumimaid-v0.2-12B
F:\mergekit\mistralaiMistral-Nemo-Instruct-2407

Configuration

The following YAML configuration was used to produce this model:

models:
  - model: F:\mergekit\NeverSleep_Lumimaid-v0.2-12B
  - model: F:\mergekit\intervitens_mini-magnum-12b-v1.1
  - model: F:\mergekit\mistralaiMistral-Nemo-Instruct-2407
merge_method: model_stock
base_model: F:\mergekit\mistralaiMistral-Nemo-Base-2407
parameters:
  filter_wise: false
dtype: bfloat16

Ko-fi

Enjoying what I do? Consider donating here, thank you!

https://ko-fi.com/spicy_marinara

Downloads last month: 22

GGUF

Model size

12B params

Architecture

llama

Hardware compatibility

2-bit

3-bit

4-bit

5-bit

6-bit

8-bit

Inference Providers NEW

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

Paper for QuantFactory/Nemomix-v2.0-12B-GGUF

Model Stock: All we need is just a few fine-tuned models

Paper • 2403.19522 • Published Mar 28, 2024 • 15