Instructions to use TitleOS/ExperimentOne with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use TitleOS/ExperimentOne with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="TitleOS/ExperimentOne")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("TitleOS/ExperimentOne") model = AutoModelForCausalLM.from_pretrained("TitleOS/ExperimentOne") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use TitleOS/ExperimentOne with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "TitleOS/ExperimentOne" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "TitleOS/ExperimentOne", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/TitleOS/ExperimentOne
- SGLang
How to use TitleOS/ExperimentOne 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 "TitleOS/ExperimentOne" \ --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": "TitleOS/ExperimentOne", "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 "TitleOS/ExperimentOne" \ --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": "TitleOS/ExperimentOne", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use TitleOS/ExperimentOne with Docker Model Runner:
docker model run hf.co/TitleOS/ExperimentOne
| base_model: | |
| - mistralai/Mistral-7B-v0.1 | |
| - cognitivecomputations/dolphin-2.8-mistral-7b-v02 | |
| - NousResearch/Hermes-2-Pro-Mistral-7B | |
| library_name: transformers | |
| tags: | |
| - mergekit | |
| - merge | |
| license: apache-2.0 | |
| # ExperimentOne (Mistral-Hermes-Dolphin-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 [Model Stock](https://arxiv.org/abs/2403.19522) 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: | |
| * [cognitivecomputations/dolphin-2.8-mistral-7b-v02](https://huggingface.co/cognitivecomputations/dolphin-2.8-mistral-7b-v02) | |
| * [NousResearch/Hermes-2-Pro-Mistral-7B](https://huggingface.co/NousResearch/Hermes-2-Pro-Mistral-7B) | |
| ### Configuration | |
| The following YAML configuration was used to produce this model: | |
| ```yaml | |
| models: | |
| - model: mistralai/Mistral-7B-v0.1 | |
| - model: NousResearch/Hermes-2-Pro-Mistral-7B | |
| - model: cognitivecomputations/dolphin-2.8-mistral-7b-v02 | |
| merge_method: model_stock | |
| base_model: mistralai/Mistral-7B-v0.1 | |
| dtype: bfloat16 | |
| ``` |