Instructions to use MarinaraSpaghetti/NemoReRemix-12B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use MarinaraSpaghetti/NemoReRemix-12B with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="MarinaraSpaghetti/NemoReRemix-12B")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("MarinaraSpaghetti/NemoReRemix-12B") model = AutoModelForCausalLM.from_pretrained("MarinaraSpaghetti/NemoReRemix-12B", device_map="auto") - Inference
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use MarinaraSpaghetti/NemoReRemix-12B with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "MarinaraSpaghetti/NemoReRemix-12B" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "MarinaraSpaghetti/NemoReRemix-12B", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/MarinaraSpaghetti/NemoReRemix-12B
- SGLang
How to use MarinaraSpaghetti/NemoReRemix-12B with SGLang:
Install from pip and serve model
# Install SGLang from pip: pip install sglang # Start the SGLang server: python3 -m sglang.launch_server \ --model-path "MarinaraSpaghetti/NemoReRemix-12B" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "MarinaraSpaghetti/NemoReRemix-12B", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker images
docker run --gpus all \ --shm-size 32g \ -p 30000:30000 \ -v ~/.cache/huggingface:/root/.cache/huggingface \ --env "HF_TOKEN=<secret>" \ --ipc=host \ lmsysorg/sglang:latest \ python3 -m sglang.launch_server \ --model-path "MarinaraSpaghetti/NemoReRemix-12B" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "MarinaraSpaghetti/NemoReRemix-12B", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use MarinaraSpaghetti/NemoReRemix-12B with Docker Model Runner:
docker model run hf.co/MarinaraSpaghetti/NemoReRemix-12B
File size: 3,119 Bytes
c15327a 9ebc7c2 c15327a | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 | ---
base_model: []
library_name: transformers
tags:
- mergekit
- merge
---


# Information
## Details
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.
## Instruct
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.
```
<|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/NemoReRemix-GGUF
# NemoReRemix-12B
This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit).
## Merge Details
### Merge Method
This model was merged using the della_linear merge method using E:\mergekit\mistralaiMistral-Nemo-Base-2407 as a base.
### Models Merged
The following models were included in the merge:
* E:\mergekit\Sao10K_MN-12B-Lyra-v1
* E:\mergekit\mistralaiMistral-Nemo-Instruct-2407
* E:\mergekit\migtissera_Tess-3-Mistral-Nemo
* E:\mergekit\shuttleai_shuttle-2.5-mini
* E:\mergekit\anthracite-org_magnum-12b-v2
### Configuration
The following YAML configuration was used to produce this model:
```yaml
models:
- model: E:\mergekit\mistralaiMistral-Nemo-Instruct-2407
parameters:
weight: 0.1
density: 0.4
- model: E:\mergekit\Sao10K_MN-12B-Lyra-v1
parameters:
weight: 0.12
density: 0.5
- model: E:\mergekit\shuttleai_shuttle-2.5-mini
parameters:
weight: 0.2
density: 0.6
- model: E:\mergekit\migtissera_Tess-3-Mistral-Nemo
parameters:
weight: 0.25
density: 0.7
- model: E:\mergekit\anthracite-org_magnum-12b-v2
parameters:
weight: 0.33
density: 0.8
merge_method: della_linear
base_model: E:\mergekit\mistralaiMistral-Nemo-Base-2407
parameters:
epsilon: 0.05
lambda: 1
dtype: bfloat16
```
# Ko-fi
## Enjoying what I do? Consider donating here, thank you!
https://ko-fi.com/spicy_marinara |