Instructions to use DarkArtsForge/Magistaroth-24B-v1.2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use DarkArtsForge/Magistaroth-24B-v1.2 with Transformers:

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

pipe = pipeline("text-generation", model="DarkArtsForge/Magistaroth-24B-v1.2")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("DarkArtsForge/Magistaroth-24B-v1.2")
model = AutoModelForCausalLM.from_pretrained("DarkArtsForge/Magistaroth-24B-v1.2")
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]:]))

Inference
Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use DarkArtsForge/Magistaroth-24B-v1.2 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "DarkArtsForge/Magistaroth-24B-v1.2"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "DarkArtsForge/Magistaroth-24B-v1.2",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/DarkArtsForge/Magistaroth-24B-v1.2

SGLang

How to use DarkArtsForge/Magistaroth-24B-v1.2 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 "DarkArtsForge/Magistaroth-24B-v1.2" \
    --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": "DarkArtsForge/Magistaroth-24B-v1.2",
		"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 "DarkArtsForge/Magistaroth-24B-v1.2" \
        --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": "DarkArtsForge/Magistaroth-24B-v1.2",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use DarkArtsForge/Magistaroth-24B-v1.2 with Docker Model Runner:
```
docker model run hf.co/DarkArtsForge/Magistaroth-24B-v1.2
```

Naphula commited on Apr 25

Commit

b532436

verified ·

1 Parent(s): fc83d48

Update README.md

Browse files

Files changed (1) hide show

README.md +2 -0

README.md CHANGED Viewed

@@ -36,6 +36,8 @@ By testing the "threshold" to see where refusals were with each merge (when comb
 v1.2 scores lower at Q0 bench than v1 original, but higher than v1 MPOA. It should be superior to v1.1 which was a highly experimental `pdq` version.
 This model was merged using the following merge method: <a href="https://arxiv.org/abs/2406.11617">DELLA</a>
 ```yaml

 v1.2 scores lower at Q0 bench than v1 original, but higher than v1 MPOA. It should be superior to v1.1 which was a highly experimental `pdq` version.
+(A version with unablated PF 4.1 was also tested, it was smarter but had some refusals)
 This model was merged using the following merge method: <a href="https://arxiv.org/abs/2406.11617">DELLA</a>
 ```yaml