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
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

See axolotl config

axolotl version: 0.12.0.dev0

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>"

Magcap-Adonis-12B

This model is a fine-tuned version of 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

Downloads last month: 16

Safetensors

Model size

12B params

Tensor type

F32

BF16

Model tree for mrcuddle/Magcap-Adonis-12B

Base model

mrcuddle/NemoMix-Magcap-12B

Finetuned

(1)

this model

Quantizations

3 models

mrcuddle
/

Magcap-Adonis-12B

Magcap-Adonis-12B

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

Model tree for mrcuddle/Magcap-Adonis-12B

Dataset used to train mrcuddle/Magcap-Adonis-12B