Instructions to use mrcuddle/Magcap-Adonis-12B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use mrcuddle/Magcap-Adonis-12B with Transformers:

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

pipe = pipeline("text-generation", model="mrcuddle/Magcap-Adonis-12B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("mrcuddle/Magcap-Adonis-12B")
model = AutoModelForCausalLM.from_pretrained("mrcuddle/Magcap-Adonis-12B")
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 mrcuddle/Magcap-Adonis-12B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "mrcuddle/Magcap-Adonis-12B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "mrcuddle/Magcap-Adonis-12B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/mrcuddle/Magcap-Adonis-12B

SGLang

How to use mrcuddle/Magcap-Adonis-12B 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 "mrcuddle/Magcap-Adonis-12B" \
    --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": "mrcuddle/Magcap-Adonis-12B",
		"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 "mrcuddle/Magcap-Adonis-12B" \
        --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": "mrcuddle/Magcap-Adonis-12B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use mrcuddle/Magcap-Adonis-12B with Docker Model Runner:
```
docker model run hf.co/mrcuddle/Magcap-Adonis-12B
```

Magcap-Adonis-12B / README.md

mrcuddle

End of training

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	---
	library_name: transformers
	base_model: mrcuddle/NemoMix-Magcap-12B
	tags:
	- axolotl
	- generated_from_trainer
	datasets:
	- mrcuddle/adonis_nsfw_alpaca
	model-index:
	- name: Magcap-Adonis-12B
	results: []
	---

	<!-- This model card has been generated automatically according to the information the Trainer had access to. You
	should probably proofread and complete it, then remove this comment. -->

	[<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl)
	<details><summary>See axolotl config</summary>

	axolotl version: `0.12.0.dev0`
	```yaml
	base_model: mrcuddle/NemoMix-Magcap-12B
	tokenizer_type: AutoTokenizer
	hub_model_id: mrcuddle/Magcap-Adonis-12B
	strict: false

	datasets:
	- path: mrcuddle/adonis_nsfw_alpaca
	type: alpaca
	streaming: false

	output_dir: ./mistral-12b-adonis


	sequence_len: 2048
	sample_packing: true
	pad_to_sequence_len: true


	eval_sample_packing: False
	gradient_accumulation_steps: 2
	micro_batch_size: 2
	num_epochs: 4
	optimizer: paged_adamw_8bit
	lr_scheduler: cosine
	learning_rate: 0.00005

	train_on_inputs: false
	group_by_length: false

	bf16: auto
	fp16:
	past_model_outputs: false

	gradient_checkpointing: true
	save_steps: 500
	special_tokens:
	bos_token: "<s>"
	eos_token: "</s>"
	pad_token: "</s>"
	```

	</details><br>

	# Magcap-Adonis-12B

	This model is a fine-tuned version of [mrcuddle/NemoMix-Magcap-12B](https://huggingface.co/mrcuddle/NemoMix-Magcap-12B) on the mrcuddle/adonis_nsfw_alpaca dataset.

	## Model description

	More information needed

	## Intended uses & limitations

	More information needed

	## Training and evaluation data

	More information needed

	## Training procedure

	### Training hyperparameters

	The following hyperparameters were used during training:
	- learning_rate: 5e-05
	- train_batch_size: 2
	- eval_batch_size: 2
	- seed: 42
	- gradient_accumulation_steps: 2
	- total_train_batch_size: 4
	- optimizer: Use OptimizerNames.PAGED_ADAMW_8BIT with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
	- lr_scheduler_type: cosine
	- training_steps: 32

	### Training results



	### Framework versions

	- Transformers 4.53.2
	- Pytorch 2.7.1+cu126
	- Datasets 4.0.0
	- Tokenizers 0.21.2