Instructions to use appvoid/cloud-09 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use appvoid/cloud-09 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="appvoid/cloud-09")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("appvoid/cloud-09") model = AutoModelForCausalLM.from_pretrained("appvoid/cloud-09") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use appvoid/cloud-09 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "appvoid/cloud-09" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "appvoid/cloud-09", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/appvoid/cloud-09
- SGLang
How to use appvoid/cloud-09 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/cloud-09" \ --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/cloud-09", "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/cloud-09" \ --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/cloud-09", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use appvoid/cloud-09 with Docker Model Runner:
docker model run hf.co/appvoid/cloud-09
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base_model:
- appvoid/palmer-005-core
- MihaiPopa-1/LFM2.5-350M-heretic
- squ11z1/claude-oss-350m
- mkurman/LiquidAI-LFM2.5-350M-SYNTH
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 [DARE TIES](https://arxiv.org/abs/2311.03099) merge method using [appvoid/palmer-005-core](https://huggingface.co/appvoid/palmer-005-core) as a base.
### Models Merged
The following models were included in the merge:
* [MihaiPopa-1/LFM2.5-350M-heretic](https://huggingface.co/MihaiPopa-1/LFM2.5-350M-heretic)
* [squ11z1/claude-oss-350m](https://huggingface.co/squ11z1/claude-oss-350m)
* [mkurman/LiquidAI-LFM2.5-350M-SYNTH](https://huggingface.co/mkurman/LiquidAI-LFM2.5-350M-SYNTH)
### Configuration
The following YAML configuration was used to produce this model:
```yaml
merge_method: dare_ties
base_model: appvoid/palmer-005-core
dtype: bfloat16
parameters:
normalize: false
int8_mask: true
models:
# slot_1_strongest
- model: squ11z1/claude-oss-350m
parameters:
weight: 0.28
density: 0.38
# slot_2_medium
- model: MihaiPopa-1/LFM2.5-350M-heretic
parameters:
weight: 0.22
density: 0.34
# slot_3_light
- model: mkurman/LiquidAI-LFM2.5-350M-SYNTH
parameters:
weight: 0.14
density: 0.28
```
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