Instructions to use QuantFactory/NemoRemix-12B-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use QuantFactory/NemoRemix-12B-GGUF with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("QuantFactory/NemoRemix-12B-GGUF", dtype="auto") - llama-cpp-python
How to use QuantFactory/NemoRemix-12B-GGUF with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="QuantFactory/NemoRemix-12B-GGUF", filename="NemoRemix-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/NemoRemix-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/NemoRemix-12B-GGUF:Q4_K_M # Run inference directly in the terminal: llama-cli -hf QuantFactory/NemoRemix-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/NemoRemix-12B-GGUF:Q4_K_M # Run inference directly in the terminal: llama-cli -hf QuantFactory/NemoRemix-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/NemoRemix-12B-GGUF:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf QuantFactory/NemoRemix-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/NemoRemix-12B-GGUF:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf QuantFactory/NemoRemix-12B-GGUF:Q4_K_M
Use Docker
docker model run hf.co/QuantFactory/NemoRemix-12B-GGUF:Q4_K_M
- LM Studio
- Jan
- Ollama
How to use QuantFactory/NemoRemix-12B-GGUF with Ollama:
ollama run hf.co/QuantFactory/NemoRemix-12B-GGUF:Q4_K_M
- Unsloth Studio new
How to use QuantFactory/NemoRemix-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/NemoRemix-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/NemoRemix-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/NemoRemix-12B-GGUF to start chatting
- Docker Model Runner
How to use QuantFactory/NemoRemix-12B-GGUF with Docker Model Runner:
docker model run hf.co/QuantFactory/NemoRemix-12B-GGUF:Q4_K_M
- Lemonade
How to use QuantFactory/NemoRemix-12B-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull QuantFactory/NemoRemix-12B-GGUF:Q4_K_M
Run and chat with the model
lemonade run user.NemoRemix-12B-GGUF-Q4_K_M
List all available models
lemonade list
QuantFactory/NemoRemix-12B-GGUF
This is quantized version of MarinaraSpaghetti/NemoRemix-12B created using llama.cpp
Original Model Card
Try NemoReRemix here!
https://huggingface.co/MarinaraSpaghetti/NemoReRemix-12B
Information
Details
New merge of NeMo based models, thankfully this time with ChatML format. My goal was to create a smart and universal roleplaying model that is stable on higher contexts. So far seems to be better than my best Nemomix attempts, especially on the 64k+ context I've been using. All credits and thanks go to the amazing Gryphe, MistralAI, Anthracite, Sao10K and ShuttleAI for their amazing models.
Instruct
ChatML but Mistral Instruct should work too (theoretically).
<|im_start|>system
{system}<|im_end|>
<|im_start|>user
{message}<|im_end|>
<|im_start|>assistant
{response}<|im_end|>
Parameters
I recommend running Temperature 1.0-1.2 with 0.1 Top A or 0.01-0.1 Min P, and with 0.8/1.75/2/0 DRY. Also works with lower Temperatures below 1.0. Nothing more needed.
Settings
You can use my exact settings from here (use the ones from the ChatML Base/Customized folder): https://huggingface.co/MarinaraSpaghetti/SillyTavern-Settings/tree/main.
GGUF
https://huggingface.co/MarinaraSpaghetti/NemoRemix-12B-GGUF
NemoRemix-v4.0-12B
This is a merge of pre-trained language models created using mergekit.
Merge Details
Merge Method
This model was merged using the della_linear merge method using F:\mergekit\mistralaiMistral-Nemo-Base-2407 as a base.
Models Merged
The following models were included in the merge:
- F:\mergekit\mistralaiMistral-Nemo-Instruct-2407
- F:\mergekit\Gryphe_Pantheon-RP-1.5-12b-Nemo
- F:\mergekit\shuttleai_shuttle-2.5-mini
- F:\mergekit\Sao10K_MN-12B-Lyra-v1
- F:\mergekit\anthracite-org_magnum-12b-v2
Configuration
The following YAML configuration was used to produce this model:
models:
- model: F:\mergekit\Gryphe_Pantheon-RP-1.5-12b-Nemo
parameters:
weight: 0.1
density: 0.3
- model: F:\mergekit\mistralaiMistral-Nemo-Instruct-2407
parameters:
weight: 0.12
density: 0.4
- model: F:\mergekit\Sao10K_MN-12B-Lyra-v1
parameters:
weight: 0.2
density: 0.5
- model: F:\mergekit\shuttleai_shuttle-2.5-mini
parameters:
weight: 0.25
density: 0.6
- model: F:\mergekit\anthracite-org_magnum-12b-v2
parameters:
weight: 0.33
density: 0.8
merge_method: della_linear
base_model: F:\mergekit\mistralaiMistral-Nemo-Base-2407
parameters:
epsilon: 0.05
lambda: 1
dtype: bfloat16
Ko-fi
Enjoying what I do? Consider donating here, thank you!
- Downloads last month
- 51
2-bit
3-bit
4-bit
5-bit
6-bit
8-bit

