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
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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
```
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