Instructions to use grimjim/MagTie-v1-12B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use grimjim/MagTie-v1-12B with Transformers:

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

pipe = pipeline("text-generation", model="grimjim/MagTie-v1-12B")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("grimjim/MagTie-v1-12B")
model = AutoModelForCausalLM.from_pretrained("grimjim/MagTie-v1-12B")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use grimjim/MagTie-v1-12B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "grimjim/MagTie-v1-12B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "grimjim/MagTie-v1-12B",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/grimjim/MagTie-v1-12B

SGLang

How to use grimjim/MagTie-v1-12B 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 "grimjim/MagTie-v1-12B" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "grimjim/MagTie-v1-12B",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

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 "grimjim/MagTie-v1-12B" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "grimjim/MagTie-v1-12B",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use grimjim/MagTie-v1-12B with Docker Model Runner:
```
docker model run hf.co/grimjim/MagTie-v1-12B
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

MagTie-v1-12B

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

We used a pretrained base model as the base for a DARE-TIES merge, compensating by boosting the weights and densities in order to retain more training from the contributing models.

Merge Details

Merge Method

This model was merged using the DARE TIES merge method using grimjim/mistralai-Mistral-Nemo-Base-2407 as a base.

Models Merged

The following models were included in the merge:

Configuration

The following YAML configuration was used to produce this model:

base_model: grimjim/mistralai-Mistral-Nemo-Base-2407
models:
  - model: grimjim/mistralai-Mistral-Nemo-Base-2407
  - model: inflatebot/MN-12B-Mag-Mell-R1
    parameters:
      weight: 0.85
      density: 0.75
  - model: Delta-Vector/Francois-Huali-12B
    parameters:
      weight: 0.85
      density: 0.75
  - model: grimjim/Magnolia-v3-12B
    parameters:
      weight: 0.85
      density: 0.75
merge_method: dare_ties
parameters:
  normalize: true
  int8_mask: true
dtype: bfloat16