Instructions to use appvoid/v-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use appvoid/v-base with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="appvoid/v-base")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("appvoid/v-base") model = AutoModelForCausalLM.from_pretrained("appvoid/v-base") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use appvoid/v-base with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "appvoid/v-base" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "appvoid/v-base", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/appvoid/v-base
- SGLang
How to use appvoid/v-base 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 "appvoid/v-base" \ --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": "appvoid/v-base", "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 "appvoid/v-base" \ --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": "appvoid/v-base", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use appvoid/v-base with Docker Model Runner:
docker model run hf.co/appvoid/v-base
| base_model: | |
| - vihangd/DopeyTinyLlama-1.1B-v1 | |
| - appvoid/palmer-003 | |
| - raidhon/coven_tiny_1.1b_32k_orpo_alpha | |
| library_name: transformers | |
| tags: | |
| - mergekit | |
| - merge | |
| # merge | |
| 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 [TIES](https://arxiv.org/abs/2306.01708) merge method using [appvoid/palmer-003](https://huggingface.co/appvoid/palmer-003) as a base. | |
| ### Models Merged | |
| The following models were included in the merge: | |
| * [vihangd/DopeyTinyLlama-1.1B-v1](https://huggingface.co/vihangd/DopeyTinyLlama-1.1B-v1) | |
| * [raidhon/coven_tiny_1.1b_32k_orpo_alpha](https://huggingface.co/raidhon/coven_tiny_1.1b_32k_orpo_alpha) | |
| ### Configuration | |
| The following YAML configuration was used to produce this model: | |
| ```yaml | |
| models: | |
| - model: vihangd/DopeyTinyLlama-1.1B-v1 | |
| parameters: | |
| density: 0.5 | |
| weight: 0.5 | |
| - model: raidhon/coven_tiny_1.1b_32k_orpo_alpha | |
| parameters: | |
| density: 0.5 | |
| weight: 0.5 | |
| merge_method: ties | |
| base_model: appvoid/palmer-003 | |
| parameters: | |
| normalize: false | |
| int8_mask: true | |
| dtype: float16 | |
| ``` | |