Instructions to use andrijdavid/Macaroni-v2-7b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use andrijdavid/Macaroni-v2-7b with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="andrijdavid/Macaroni-v2-7b")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("andrijdavid/Macaroni-v2-7b") model = AutoModelForCausalLM.from_pretrained("andrijdavid/Macaroni-v2-7b") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use andrijdavid/Macaroni-v2-7b with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "andrijdavid/Macaroni-v2-7b" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "andrijdavid/Macaroni-v2-7b", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/andrijdavid/Macaroni-v2-7b
- SGLang
How to use andrijdavid/Macaroni-v2-7b 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 "andrijdavid/Macaroni-v2-7b" \ --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": "andrijdavid/Macaroni-v2-7b", "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 "andrijdavid/Macaroni-v2-7b" \ --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": "andrijdavid/Macaroni-v2-7b", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use andrijdavid/Macaroni-v2-7b with Docker Model Runner:
docker model run hf.co/andrijdavid/Macaroni-v2-7b
| base_model: | |
| - flemmingmiguel/MBX-7B-v3 | |
| - mlabonne/OmniBeagle-7B | |
| - mistralai/Mistral-7B-v0.1 | |
| - vanillaOVO/supermario_v4 | |
| tags: | |
| - mergekit | |
| - merge | |
| license: apache-2.0 | |
| language: | |
| - en | |
| # Macaroni V2 7B | |
| This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit). | |
| ## Merge Details | |
| ### Merge Method | |
| This model was merged using the [DARE](https://arxiv.org/abs/2311.03099) [TIES](https://arxiv.org/abs/2306.01708) merge method using [mistralai/Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1) as a base. | |
| ### Models Merged | |
| The following models were included in the merge: | |
| * [flemmingmiguel/MBX-7B-v3](https://huggingface.co/flemmingmiguel/MBX-7B-v3) | |
| * [mlabonne/OmniBeagle-7B](https://huggingface.co/mlabonne/OmniBeagle-7B) | |
| * [vanillaOVO/supermario_v4](https://huggingface.co/vanillaOVO/supermario_v4) | |
| ### Configuration | |
| The following YAML configuration was used to produce this model: | |
| ```yaml | |
| models: | |
| - model: mistralai/Mistral-7B-v0.1 | |
| # no parameters necessary for base model | |
| - model: flemmingmiguel/MBX-7B-v3 | |
| parameters: | |
| density: 0.7 | |
| weight: 0.5 | |
| - model: vanillaOVO/supermario_v4 | |
| parameters: | |
| density: 0.7 | |
| weight: 0.3 | |
| - model: mlabonne/OmniBeagle-7B | |
| parameters: | |
| density: 0.5 | |
| weight: 0.6 | |
| merge_method: dare_ties | |
| base_model: mistralai/Mistral-7B-v0.1 | |
| parameters: | |
| int8_mask: true | |
| normalize: true | |
| dtype: float16 | |
| ``` |