Instructions to use jeiku/Weekend_Project with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use jeiku/Weekend_Project with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="jeiku/Weekend_Project")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("jeiku/Weekend_Project") model = AutoModelForCausalLM.from_pretrained("jeiku/Weekend_Project", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use jeiku/Weekend_Project with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "jeiku/Weekend_Project" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "jeiku/Weekend_Project", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/jeiku/Weekend_Project
- SGLang
How to use jeiku/Weekend_Project 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 "jeiku/Weekend_Project" \ --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": "jeiku/Weekend_Project", "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 "jeiku/Weekend_Project" \ --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": "jeiku/Weekend_Project", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use jeiku/Weekend_Project with Docker Model Runner:
docker model run hf.co/jeiku/Weekend_Project
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base_model:
- ResplendentAI/Paradigm_7B
- ResplendentAI/Paradigm_7B
- jeiku/Theory_of_Mind_Roleplay_Mistral
- ResplendentAI/Paradigm_7B
- jeiku/Theory_of_Mind_Mistral
library_name: transformers
tags:
- mergekit
- merge
---
# Weekend
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 [ResplendentAI/Paradigm_7B](https://huggingface.co/ResplendentAI/Paradigm_7B) as a base.
### Models Merged
The following models were included in the merge:
* [ResplendentAI/Paradigm_7B](https://huggingface.co/ResplendentAI/Paradigm_7B) + [jeiku/Theory_of_Mind_Roleplay_Mistral](https://huggingface.co/jeiku/Theory_of_Mind_Roleplay_Mistral)
* [ResplendentAI/Paradigm_7B](https://huggingface.co/ResplendentAI/Paradigm_7B) + [jeiku/Theory_of_Mind_Mistral](https://huggingface.co/jeiku/Theory_of_Mind_Mistral)
### Configuration
The following YAML configuration was used to produce this model:
```yaml
merge_method: dare_ties
base_model: ResplendentAI/Paradigm_7B
parameters:
normalize: true
models:
- model: ResplendentAI/Paradigm_7B+jeiku/Theory_of_Mind_Roleplay_Mistral
parameters:
weight: 1
- model: ResplendentAI/Paradigm_7B+jeiku/Theory_of_Mind_Mistral
parameters:
weight: 1
dtype: float16
```
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