Instructions to use grimjim/Magnolia-v1-12B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use grimjim/Magnolia-v1-12B with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="grimjim/Magnolia-v1-12B")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("grimjim/Magnolia-v1-12B") model = AutoModelForCausalLM.from_pretrained("grimjim/Magnolia-v1-12B") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use grimjim/Magnolia-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-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-v1-12B", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/grimjim/Magnolia-v1-12B
- SGLang
How to use grimjim/Magnolia-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-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-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-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-v1-12B", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use grimjim/Magnolia-v1-12B with Docker Model Runner:
docker model run hf.co/grimjim/Magnolia-v1-12B
Magnolia-v1-12B
This repo contains a merge of pre-trained language models created using mergekit. The base is a merge of two models trained for variety in text generation. Instruct was added in at low weight in order to increase the steerability of the model; safety has consequently been reinforced.
Tested at temperature 0.7 and minP 0.01, with ChatML prompting.
Mistral Nemo models tend to have repetition issues in general. For this model at least, various issues can be mitigated somewhat with additional sysprompting, e.g.:
No passage shall exceed 10 lines of text, with turns limited to a maximum of 5 lines per speaker to ensure snappy and engaging dialog and action.
Ensure that all punctuation rules are adhered to without the introduction of spurious intervening spaces.
Avoid redundant phrasing and maintain forward narrative progression by utilizing varied sentence structure, alternative word choices, and active voice.
Employ descriptive details judiciously, ensuring they serve a purpose in advancing the story or revealing character or touching upon setting.
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:
models:
- model: grimjim/mistralai-Mistral-Nemo-Instruct-2407
- model: grimjim/magnum-consolidatum-v1-12b
merge_method: slerp
base_model: grimjim/mistralai-Mistral-Nemo-Instruct-2407
parameters:
t:
- value: 0.1
dtype: bfloat16
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