Instructions to use Yobenboben/MN-12B-Paskuda with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Yobenboben/MN-12B-Paskuda with Transformers:

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

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

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Yobenboben/MN-12B-Paskuda")
model = AutoModelForCausalLM.from_pretrained("Yobenboben/MN-12B-Paskuda")
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 Yobenboben/MN-12B-Paskuda with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/Yobenboben/MN-12B-Paskuda

SGLang

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

Docker Model Runner
How to use Yobenboben/MN-12B-Paskuda with Docker Model Runner:
```
docker model run hf.co/Yobenboben/MN-12B-Paskuda
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

merge

This is a merge of pre-trained language models created using mergekit.

Merge Details

Merge Method

This model was merged using the Model Breadcrumbs merge method using OddTheGreat/Sinner_12B_V.1 as a base.

Models Merged

The following models were included in the merge:

Configuration

The following YAML configuration was used to produce this model:

models:
  - model: OddTheGreat/Sinner_12B_V.1
    parameters:
      weight: [0.9, 1, 0.8, 0.65, 0.2, 0.05, 0.1, 0.35, 0.65, 0.8]
  - model: BeaverAI/MN-2407-DSK-QwQify-v0.1-12B
    parameters:
      weight: [0.1, 10e-5, 10e-5, 0.1, 0.5, 0.5, 0.5, 0.35, 0.1, 10e-5]
      density: [0.35, 1, 1, 0.35, 0.35, 0.5, 0.5, 0.6, 0.9, 1]
      gamma: [5e-4, 5e-3, 5e-3, 5e-4, 5e-4, 0.01, 0.01, 0.01, 5e-3, 5e-4]
  - model: Nitral-AI/Mag-Mell-Reasoner-12B
    parameters:
      weight: [10e-5, 10e-5, 0.2, 0.25, 0.3, 0.45, 0.4, 0.3, 0.25, 0.2]
      density: [1, 1, 0.9, 0.9, 0.85, 0.85, 0.8, 0.85, 0.9, 0.9]
      gamma: 0.03
merge_method: breadcrumbs
base_model: OddTheGreat/Sinner_12B_V.1
parameters:
  int8_mask: false
  rescale: true
  normalize: false
dtype: float32
out_dtype: bfloat16
tokenizer_source: BeaverAI/MN-2407-DSK-QwQify-v0.1-12B

Downloads last month: 1

Safetensors

Model size

12B params

Tensor type

BF16

Model tree for Yobenboben/MN-12B-Paskuda

BeaverAI/MN-2407-DSK-QwQify-v0.1-12B

Nitral-AI/Mag-Mell-Reasoner-12B

Merge model

this model

Quantizations

3 models

Paper for Yobenboben/MN-12B-Paskuda

Model Breadcrumbs: Scaling Multi-Task Model Merging with Sparse Masks

Paper • 2312.06795 • Published Dec 11, 2023 • 2