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

Libraries

How to use allura-org/MS-Meadowlark-22B with Transformers:

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

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

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("allura-org/MS-Meadowlark-22B")
model = AutoModelForCausalLM.from_pretrained("allura-org/MS-Meadowlark-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 Settings

vLLM

How to use allura-org/MS-Meadowlark-22B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "allura-org/MS-Meadowlark-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": "allura-org/MS-Meadowlark-22B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/allura-org/MS-Meadowlark-22B

SGLang

How to use allura-org/MS-Meadowlark-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 "allura-org/MS-Meadowlark-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": "allura-org/MS-Meadowlark-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 "allura-org/MS-Meadowlark-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": "allura-org/MS-Meadowlark-22B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

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

MS-Meadowlark-22B

Big thanks to @inflatebot for the image.

A roleplay and storywriting model based on Mistral Small 22B.

GGUF models: https://huggingface.co/mradermacher/MS-Meadowlark-22B-GGUF/

EXL2 models: https://huggingface.co/CalamitousFelicitousness/MS-Meadowlark-22B-exl2

Datasets used in this model:

Dampfinchen/Creative_Writing_Multiturn at 16k
Fizzarolli/rosier-dataset + Alfitaria/body-inflation-org at 16k
ToastyPigeon/SpringDragon at 8k

Each dataset was trained separately onto Mistral Small Instruct, and then the component models were merged along with nbeerbower/Mistral-Small-Gutenberg-Doppel-22B to create Meadowlark.

I tried different blends of the component models, and this one seems to be the most stable while retaining creativity and unpredictability added by the trained data.

Instruct Format

Rosier/bodyinf and SpringDragon were trained in completion format. This model should work with Kobold Lite in Adventure Mode and Story Mode.

Creative_Writing_Multiturn and Gutenberg-Doppel were trained using the official instruct format of Mistral Small Instruct:

<s>[INST] {User message}[/INST] {Assistant response}</s>

This is the Mistral Small V2&V3 preset in SillyTavern and Kobold Lite.

For SillyTavern in particular I've had better luck getting good output from Mistral Small using a custom instruct template that formats the assembled context as a single user turn. This prevents SillyTavern from confusing the model by assembling user/assistant turns in a nonstandard way. Note: This preset is not compatible with Stepped Thinking, use the Mistral V2&V3 preset for that.