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

Libraries

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

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

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

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

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

Inference
Notebooks
Google Colab
Kaggle
Local Apps

vLLM

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

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "grimjim/Magnolia-Mell-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/Magnolia-Mell-v1-12B",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

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

SGLang

How to use grimjim/Magnolia-Mell-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/Magnolia-Mell-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/Magnolia-Mell-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/Magnolia-Mell-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/Magnolia-Mell-v1-12B",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

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

Magnolia-Mell-v1-12B

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

An asymmetric gradient SLERP was used to lightly apply MN-12B-Mag-Mell-R1 to Magnolia-v3-12B.

Tested for narrative text completion with temperature=1.0 and minP=0.02. Coherence is fairly high, though there may be occasional slips. If repetition is a problem, raising temperature briefly may help. The model appears to tolerate temperature=2.0 even.

Merge Details

Merge Method

This model was merged using the SLERP merge method.

Models Merged

The following models were included in the merge:

Configuration

The following YAML configuration was used to produce this model:

base_model: grimjim/Magnolia-v3-12B
dtype: bfloat16
merge_method: slerp
slices:
- sources:
  - model: grimjim/Magnolia-v3-12B
    layer_range: [0,40]
  - model: inflatebot/MN-12B-Mag-Mell-R1
    layer_range: [0,40]
parameters:
  t:
    - filter: self_attn
      value: [0.0,0.09]
    - filter: mlp
      value: [0.09,0.0]
    - value: [0.0,0.09]

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Safetensors

Model size

12B params

Tensor type

BF16

Model tree for grimjim/Magnolia-Mell-v1-12B

grimjim/Magnolia-v3-12B

inflatebot/MN-12B-Mag-Mell-R1

Merge model

this model

Merges

1 model

Quantizations

3 models