Instructions to use DarkArtsForge/Magistaroth-24B-v1.1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use DarkArtsForge/Magistaroth-24B-v1.1 with Transformers:

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

pipe = pipeline("text-generation", model="DarkArtsForge/Magistaroth-24B-v1.1")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("DarkArtsForge/Magistaroth-24B-v1.1")
model = AutoModelForCausalLM.from_pretrained("DarkArtsForge/Magistaroth-24B-v1.1")
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]:]))

Inference
Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use DarkArtsForge/Magistaroth-24B-v1.1 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "DarkArtsForge/Magistaroth-24B-v1.1"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "DarkArtsForge/Magistaroth-24B-v1.1",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/DarkArtsForge/Magistaroth-24B-v1.1

SGLang

How to use DarkArtsForge/Magistaroth-24B-v1.1 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 "DarkArtsForge/Magistaroth-24B-v1.1" \
    --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": "DarkArtsForge/Magistaroth-24B-v1.1",
		"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 "DarkArtsForge/Magistaroth-24B-v1.1" \
        --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": "DarkArtsForge/Magistaroth-24B-v1.1",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use DarkArtsForge/Magistaroth-24B-v1.1 with Docker Model Runner:
```
docker model run hf.co/DarkArtsForge/Magistaroth-24B-v1.1
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

⚠️ Warning: This model can produce narratives and RP that contain violent and graphic erotic content. Adjust your system prompt accordingly, and use Mistral Tekken chat template.

🌌 Magistaroth 24B v1.1

Merge Method

A custom merge method known as pdq has been invented. Instead of using its own yaml, it acts as a post-merge processor which applies directly to the merged model using the original yaml. pdq aims to enhance creativity by re-scanning the original donor models, encouraging them to explore the 'dark matter' regions of the vectors to synergistically augment the merged base with more unique novelty. For Magistaroth v1.1, I tested both the v1 Della → PDQ → MPOA and Della → MPOA → PDQ.

It turns out that both are very creative, and the MPOA → PDQ is interesting because it doesn't re-introduce any refusals, however, PDQ → MPOA is much smarter. The difference in Q0 bench reflects this (9451 vs 12648). Scale 1.2 was the ablation threshold required to disable refusals. This has resulted in the most creative, detailed, and uncensored variant of the configurations tested.

Bugs

A small risk of increased artifacts (missing spaces, word misspelled or repeated) might be noticed due to pdq pushing the limits of what's possible with transformers. These are rare and can be edited out if needed.