Instructions to use grimjim/Magnolia-v2-12B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use grimjim/Magnolia-v2-12B with Transformers:

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

pipe = pipeline("text-generation", model="grimjim/Magnolia-v2-12B")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("grimjim/Magnolia-v2-12B")
model = AutoModelForCausalLM.from_pretrained("grimjim/Magnolia-v2-12B")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use grimjim/Magnolia-v2-12B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "grimjim/Magnolia-v2-12B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "grimjim/Magnolia-v2-12B",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/grimjim/Magnolia-v2-12B

SGLang

How to use grimjim/Magnolia-v2-12B 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/Magnolia-v2-12B" \
    --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/Magnolia-v2-12B",
		"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/Magnolia-v2-12B" \
        --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/Magnolia-v2-12B",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use grimjim/Magnolia-v2-12B with Docker Model Runner:
```
docker model run hf.co/grimjim/Magnolia-v2-12B
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Magnolia-v2-12B

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

This model essentially a rebuild of v1, using task arithmetic instead of SLERP. Of note, Mistral Nemo Base was used as the base model for task arithmetic, rather than Instruct or another fine-tune. Furthermore, max_position_embeddings was reduced to 131072, down from 1000000, as the model was only trained on up to 131072. Tested with temperature 1.0 and minP 0.01; temperature can be reduced (briefly tested at 0.45) if the model is too aggressively creative/hallucinatory.

Merge Details

Merge Method

This model was merged using the task arithmetic 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
dtype: bfloat16
merge_method: task_arithmetic
parameters:
  normalize: false
slices:
- sources:
  - layer_range: [0, 40]
    model: grimjim/mistralai-Mistral-Nemo-Base-2407
  - layer_range: [0, 40]
    model: grimjim/mistralai-Mistral-Nemo-Instruct-2407
    parameters:
      weight: 0.9
  - layer_range: [0, 40]
    model: grimjim/magnum-consolidatum-v1-12b
    parameters:
      weight: 0.1