Instructions to use Nohobby/MS-Schisandra-22B-v0.2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Nohobby/MS-Schisandra-22B-v0.2 with Transformers:

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

pipe = pipeline("text-generation", model="Nohobby/MS-Schisandra-22B-v0.2")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForMultimodalLM

tokenizer = AutoTokenizer.from_pretrained("Nohobby/MS-Schisandra-22B-v0.2")
model = AutoModelForMultimodalLM.from_pretrained("Nohobby/MS-Schisandra-22B-v0.2")
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 Settings

vLLM

How to use Nohobby/MS-Schisandra-22B-v0.2 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Nohobby/MS-Schisandra-22B-v0.2"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Nohobby/MS-Schisandra-22B-v0.2",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Nohobby/MS-Schisandra-22B-v0.2

SGLang

How to use Nohobby/MS-Schisandra-22B-v0.2 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 "Nohobby/MS-Schisandra-22B-v0.2" \
    --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": "Nohobby/MS-Schisandra-22B-v0.2",
		"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 "Nohobby/MS-Schisandra-22B-v0.2" \
        --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": "Nohobby/MS-Schisandra-22B-v0.2",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Nohobby/MS-Schisandra-22B-v0.2 with Docker Model Runner:
```
docker model run hf.co/Nohobby/MS-Schisandra-22B-v0.2
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Schisandra

Many thanks to the authors of the models used!

Overview

Main uses: RP, Storywriting

Prompt format: Mistral-V3

An intelligent model that is attentive to details and has a low-slop writing style. This time with a stable tokenizer.

Oh, and it now contains 10 finetunes! Not sure if some of them actually contribute to the output, but it's nice to see the numbers growing.

Quants

GGUF: Static | Imatrix

exl2: 4.65bpw 5.5bpw 6.5bpw

Settings

My SillyTavern preset: https://huggingface.co/Nohobby/MS-Schisandra-22B-v0.2/resolve/main/ST-formatting-Schisandra.json

Merge Details

Merging steps

Step1

(Config partially taken from here)

base_model: spow12/ChatWaifu_v2.0_22B
parameters:
  int8_mask: true
  rescale: true
  normalize: false
dtype: bfloat16
tokenizer_source: base
merge_method: della
models:
  - model: Envoid/Mistral-Small-NovusKyver
    parameters:
      density: [0.35, 0.65, 0.5, 0.65, 0.35]
      epsilon: [0.1, 0.1, 0.25, 0.1, 0.1]
      lambda: 0.85
      weight: [-0.01891, 0.01554, -0.01325, 0.01791, -0.01458]
  - model: rAIfle/Acolyte-22B
    parameters:
      density: [0.6, 0.4, 0.5, 0.4, 0.6]
      epsilon: [0.1, 0.1, 0.25, 0.1, 0.1]
      lambda: 0.85
      weight: [0.01847, -0.01468, 0.01503, -0.01822, 0.01459]

Step2

(Config partially taken from here)

base_model: InferenceIllusionist/SorcererLM-22B
parameters:
  int8_mask: true
  rescale: true
  normalize: false
dtype: bfloat16
tokenizer_source: base
merge_method: della
models:
  - model: crestf411/MS-sunfall-v0.7.0
    parameters:
      density: [0.35, 0.65, 0.5, 0.65, 0.35]
      epsilon: [0.1, 0.1, 0.25, 0.1, 0.1]
      lambda: 0.85
      weight: [-0.01891, 0.01554, -0.01325, 0.01791, -0.01458]
  - model: anthracite-org/magnum-v4-22b
    parameters:
      density: [0.6, 0.4, 0.5, 0.4, 0.6]
      epsilon: [0.1, 0.1, 0.25, 0.1, 0.1]
      lambda: 0.85
      weight: [0.01847, -0.01468, 0.01503, -0.01822, 0.01459]

SchisandraVA2

(Config taken from here)

merge_method: della_linear
dtype: bfloat16
parameters:
  normalize: true
  int8_mask: true
tokenizer_source: base
base_model: TheDrummer/UnslopSmall-22B-v1
models:
    - model: ArliAI/Mistral-Small-22B-ArliAI-RPMax-v1.1
      parameters:
        density: 0.55
        weight: 1
    - model: Gryphe/Pantheon-RP-Pure-1.6.2-22b-Small
      parameters:
        density: 0.55
        weight: 1
    - model: Step1
      parameters:
        density: 0.55
        weight: 1
    - model: allura-org/MS-Meadowlark-22B
      parameters:
        density: 0.55
        weight: 1
    - model: Step2
      parameters:
        density: 0.55
        weight: 1

Schisandra-v0.2

dtype: bfloat16
tokenizer_source: base
merge_method: della_linear
parameters:
  density: 0.5
base_model: SchisandraVA2
models:
  - model: unsloth/Mistral-Small-Instruct-2409
    parameters:
      weight:
        - filter: v_proj
          value: [0, 0, 1, 1, 1, 1, 1, 1, 1, 0, 0]
        - filter: o_proj
          value: [1, 0, 1, 0, 0, 0, 0, 0, 1, 1, 1]
        - filter: up_proj
          value: [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]
        - filter: gate_proj
          value: [0, 0, 1, 1, 1, 1, 1, 1, 1, 0, 0]
        - filter: down_proj
          value: [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
        - value: 0
  - model: SchisandraVA2
    parameters:
      weight:
        - filter: v_proj
          value: [1, 1, 0, 0, 0, 0, 0, 0, 0, 1, 1]
        - filter: o_proj
          value: [0, 1, 0, 1, 1, 1, 1, 1, 0, 0, 0]
        - filter: up_proj
          value: [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
        - filter: gate_proj
          value: [1, 1, 0, 0, 0, 0, 0, 0, 0, 1, 1]
        - filter: down_proj
          value: [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]
        - value: 1