Instructions to use djuna/MS-Nudion-22B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use djuna/MS-Nudion-22B with Transformers:

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

pipe = pipeline("text-generation", model="djuna/MS-Nudion-22B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("djuna/MS-Nudion-22B")
model = AutoModelForCausalLM.from_pretrained("djuna/MS-Nudion-22B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use djuna/MS-Nudion-22B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "djuna/MS-Nudion-22B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "djuna/MS-Nudion-22B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/djuna/MS-Nudion-22B

SGLang

How to use djuna/MS-Nudion-22B 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 "djuna/MS-Nudion-22B" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "djuna/MS-Nudion-22B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

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 "djuna/MS-Nudion-22B" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "djuna/MS-Nudion-22B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use djuna/MS-Nudion-22B with Docker Model Runner:
```
docker model run hf.co/djuna/MS-Nudion-22B
```

MS-Nudion-22B / README.md

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metadata

base_model:
  - SteelSkull/MSM-MS-Cydrion-22B
  - knifeayumu/Cydonia-v1.3-Magnum-v4-22B
library_name: transformers
tags:
  - mergekit
  - merge

merge

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

Merge Details

Merge Method

This model was merged using the SLERP merge method.

Models Merged

The following models were included in the merge:

Configuration

The following YAML configuration was used to produce this model:

models:
  - model: knifeayumu/Cydonia-v1.3-Magnum-v4-22B
  - model: SteelSkull/MSM-MS-Cydrion-22B
merge_method: slerp
base_model: SteelSkull/MSM-MS-Cydrion-22B
dtype: bfloat16
parameters:
  t: [0.030, 0.056, 0.093, 0.144, 0.224, 0.312, 0.409, 0.525, 0.614, 0.589, 0.559, 0.521, 0.437, 0.339, 0.230] # V shaped curve: Hermes for input & output, WizardMath in the middle layers