Instructions to use QuantFactory/NemoReRemix-12B-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use QuantFactory/NemoReRemix-12B-GGUF with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("QuantFactory/NemoReRemix-12B-GGUF", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use QuantFactory/NemoReRemix-12B-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 QuantFactory/NemoReRemix-12B-GGUF:Q4_K_M # Run inference directly in the terminal: llama cli -hf QuantFactory/NemoReRemix-12B-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 QuantFactory/NemoReRemix-12B-GGUF:Q4_K_M # Run inference directly in the terminal: llama cli -hf QuantFactory/NemoReRemix-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/NemoReRemix-12B-GGUF:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf QuantFactory/NemoReRemix-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/NemoReRemix-12B-GGUF:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf QuantFactory/NemoReRemix-12B-GGUF:Q4_K_M
Use Docker
docker model run hf.co/QuantFactory/NemoReRemix-12B-GGUF:Q4_K_M
- LM Studio
- Jan
- Ollama
How to use QuantFactory/NemoReRemix-12B-GGUF with Ollama:
ollama run hf.co/QuantFactory/NemoReRemix-12B-GGUF:Q4_K_M
- Unsloth Studio
How to use QuantFactory/NemoReRemix-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/NemoReRemix-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/NemoReRemix-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/NemoReRemix-12B-GGUF to start chatting
- Docker Model Runner
How to use QuantFactory/NemoReRemix-12B-GGUF with Docker Model Runner:
docker model run hf.co/QuantFactory/NemoReRemix-12B-GGUF:Q4_K_M
- Lemonade
How to use QuantFactory/NemoReRemix-12B-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull QuantFactory/NemoReRemix-12B-GGUF:Q4_K_M
Run and chat with the model
lemonade run user.NemoReRemix-12B-GGUF-Q4_K_M
List all available models
lemonade list
- Atomic Chat
Upload README.md with huggingface_hub
Browse files
README.md
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---
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base_model: []
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library_name: transformers
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tags:
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- mergekit
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- merge
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---
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# QuantFactory/NemoReRemix-12B-GGUF
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This is quantized version of [MarinaraSpaghetti/NemoReRemix-12B](https://huggingface.co/MarinaraSpaghetti/NemoReRemix-12B) created using llama.cpp
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# Original Model Card
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# Information
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## Details
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Improved NemoRemix for storytelling and roleplay. Plus, this one can also be used as a general assistant model. The prose is pretty much the same, but it was made smarter, thanks to the addition of the amazing Migtissera's Tess model. I yeeted out Gryphe's Pantheon-RP, though, because it was trained with asterisks in mind, unlike the rest of the models in the merge, which caused it to mess the formatting from time to time; this one doesn't do that anymore. Hooray! All credits and thanks go to the amazing Migtissera, MistralAI, Anthracite, Sao10K and ShuttleAI for their amazing models.
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## Instruct
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ChatML but Mistral Instruct should work too (theoretically). Important: remember to add <|im_end|> to custom stopping strings, otherwise it will appear in the output.
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```
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<|im_start|>system
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{system}<|im_end|>
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<|im_start|>user
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{message}<|im_end|>
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<|im_start|>assistant
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{response}<|im_end|>
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```
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## Parameters
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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.
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### Settings
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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.
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## GGUF
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https://huggingface.co/MarinaraSpaghetti/NemoReRemix-GGUF
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# NemoReRemix-12B
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This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit).
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## Merge Details
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### Merge Method
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This model was merged using the della_linear merge method using E:\mergekit\mistralaiMistral-Nemo-Base-2407 as a base.
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### Models Merged
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The following models were included in the merge:
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* E:\mergekit\Sao10K_MN-12B-Lyra-v1
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* E:\mergekit\mistralaiMistral-Nemo-Instruct-2407
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* E:\mergekit\migtissera_Tess-3-Mistral-Nemo
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* E:\mergekit\shuttleai_shuttle-2.5-mini
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* E:\mergekit\anthracite-org_magnum-12b-v2
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### Configuration
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The following YAML configuration was used to produce this model:
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```yaml
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models:
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- model: E:\mergekit\mistralaiMistral-Nemo-Instruct-2407
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parameters:
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weight: 0.1
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density: 0.4
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- model: E:\mergekit\Sao10K_MN-12B-Lyra-v1
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parameters:
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weight: 0.12
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density: 0.5
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- model: E:\mergekit\shuttleai_shuttle-2.5-mini
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parameters:
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weight: 0.2
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density: 0.6
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- model: E:\mergekit\migtissera_Tess-3-Mistral-Nemo
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parameters:
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weight: 0.25
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density: 0.7
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- model: E:\mergekit\anthracite-org_magnum-12b-v2
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parameters:
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weight: 0.33
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density: 0.8
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merge_method: della_linear
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base_model: E:\mergekit\mistralaiMistral-Nemo-Base-2407
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parameters:
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epsilon: 0.05
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lambda: 1
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dtype: bfloat16
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```
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# Ko-fi
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## Enjoying what I do? Consider donating here, thank you!
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https://ko-fi.com/spicy_marinara
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