Instructions to use Masterjp123/P1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Masterjp123/P1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="Masterjp123/P1")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("Masterjp123/P1") model = AutoModelForCausalLM.from_pretrained("Masterjp123/P1", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use Masterjp123/P1 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Masterjp123/P1" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Masterjp123/P1", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/Masterjp123/P1
- SGLang
How to use Masterjp123/P1 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 "Masterjp123/P1" \ --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": "Masterjp123/P1", "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 "Masterjp123/P1" \ --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": "Masterjp123/P1", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use Masterjp123/P1 with Docker Model Runner:
docker model run hf.co/Masterjp123/P1
| base_model: | |
| - NousResearch/Hermes-2-Pro-Llama-3-8B | |
| - Weyaxi/Einstein-v6.1-Llama3-8B | |
| - NousResearch/Meta-Llama-3-8B | |
| - asiansoul/Versatile-Llama-3-8B-1m | |
| library_name: transformers | |
| tags: | |
| - mergekit | |
| - merge | |
| # merged | |
| 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 [NousResearch/Meta-Llama-3-8B](https://huggingface.co/NousResearch/Meta-Llama-3-8B) as a base. | |
| ### Models Merged | |
| The following models were included in the merge: | |
| * [NousResearch/Hermes-2-Pro-Llama-3-8B](https://huggingface.co/NousResearch/Hermes-2-Pro-Llama-3-8B) | |
| * [Weyaxi/Einstein-v6.1-Llama3-8B](https://huggingface.co/Weyaxi/Einstein-v6.1-Llama3-8B) | |
| * [asiansoul/Versatile-Llama-3-8B-1m](https://huggingface.co/asiansoul/Versatile-Llama-3-8B-1m) | |
| ### Configuration | |
| The following YAML configuration was used to produce this model: | |
| ```yaml | |
| base_model: NousResearch/Meta-Llama-3-8B | |
| dtype: bfloat16 | |
| merge_method: dare_ties | |
| parameters: | |
| int8_mask: 1.0 | |
| slices: | |
| - sources: | |
| - layer_range: [0, 32] | |
| model: Weyaxi/Einstein-v6.1-Llama3-8B | |
| parameters: | |
| density: 0.1 | |
| weight: 1.0 | |
| - layer_range: [0, 32] | |
| model: asiansoul/Versatile-Llama-3-8B-1m | |
| parameters: | |
| density: 0.2 | |
| weight: 0.35 | |
| - layer_range: [0, 32] | |
| model: NousResearch/Hermes-2-Pro-Llama-3-8B | |
| parameters: | |
| density: 0.5 | |
| weight: 0.23 | |
| - layer_range: [0, 32] | |
| model: NousResearch/Meta-Llama-3-8B | |
| ``` | |