Instructions to use MarinaraSpaghetti/NemoRemix-12B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use MarinaraSpaghetti/NemoRemix-12B with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="MarinaraSpaghetti/NemoRemix-12B")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("MarinaraSpaghetti/NemoRemix-12B")
model = AutoModelForCausalLM.from_pretrained("MarinaraSpaghetti/NemoRemix-12B")

Inference
Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use MarinaraSpaghetti/NemoRemix-12B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "MarinaraSpaghetti/NemoRemix-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/NemoRemix-12B",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/MarinaraSpaghetti/NemoRemix-12B

SGLang

How to use MarinaraSpaghetti/NemoRemix-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/NemoRemix-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/NemoRemix-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/NemoRemix-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/NemoRemix-12B",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use MarinaraSpaghetti/NemoRemix-12B with Docker Model Runner:
```
docker model run hf.co/MarinaraSpaghetti/NemoRemix-12B
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

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