Instructions to use Statuo/MS-Cydonian-Gutenberg-22b-4bpw with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Statuo/MS-Cydonian-Gutenberg-22b-4bpw with Transformers:

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

pipe = pipeline("text-generation", model="Statuo/MS-Cydonian-Gutenberg-22b-4bpw")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Statuo/MS-Cydonian-Gutenberg-22b-4bpw")
model = AutoModelForCausalLM.from_pretrained("Statuo/MS-Cydonian-Gutenberg-22b-4bpw")
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 Statuo/MS-Cydonian-Gutenberg-22b-4bpw with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Statuo/MS-Cydonian-Gutenberg-22b-4bpw"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Statuo/MS-Cydonian-Gutenberg-22b-4bpw",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Statuo/MS-Cydonian-Gutenberg-22b-4bpw

SGLang

How to use Statuo/MS-Cydonian-Gutenberg-22b-4bpw 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 "Statuo/MS-Cydonian-Gutenberg-22b-4bpw" \
    --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": "Statuo/MS-Cydonian-Gutenberg-22b-4bpw",
		"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 "Statuo/MS-Cydonian-Gutenberg-22b-4bpw" \
        --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": "Statuo/MS-Cydonian-Gutenberg-22b-4bpw",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Statuo/MS-Cydonian-Gutenberg-22b-4bpw with Docker Model Runner:
```
docker model run hf.co/Statuo/MS-Cydonian-Gutenberg-22b-4bpw
```

A merge of Cydonia came out and while I'm happy with Cydonia as a current daily-driver kind of model, I'd be remiss if I didn't keep an eye out for something new that might be fun.

This is the EXL2 4bpw Quant of this model
For the 8bpw version, go here.
For the 6bpw version, go here.

A merge of Cydonia and Mistral Small Gutenberg.

This will hopefully make it and even better story teller.

Mistral or ChatML format.

Much appreciation to the original model creators The Drummer and nbeerbower.

https://ko-fi.com/dazzlingxeno

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:

base_model: nbeerbower/Mistral-Small-Gutenberg-Doppel-22B
dtype: bfloat16
merge_method: slerp
parameters:
  t:
  - filter: self_attn
    value: [0.0, 0.5, 0.3, 0.7, 1.0]
  - filter: mlp
    value: [1.0, 0.5, 0.7, 0.3, 0.0]
  - value: 0.5
slices:
- sources:
  - layer_range: [0, 56]
    model: TheDrummer/Cydonia-22B-v1
  - layer_range: [0, 56]
    model: nbeerbower/Mistral-Small-Gutenberg-Doppel-22B

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Model tree for Statuo/MS-Cydonian-Gutenberg-22b-4bpw

TheDrummer/Cydonia-22B-v1

nbeerbower/Mistral-Small-Gutenberg-Doppel-22B

Merge model

this model