Instructions to use Vortex5/Clockwork-Flower-24B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Vortex5/Clockwork-Flower-24B with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="Vortex5/Clockwork-Flower-24B")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Vortex5/Clockwork-Flower-24B")
model = AutoModelForCausalLM.from_pretrained("Vortex5/Clockwork-Flower-24B", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Vortex5/Clockwork-Flower-24B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Vortex5/Clockwork-Flower-24B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Vortex5/Clockwork-Flower-24B",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/Vortex5/Clockwork-Flower-24B

SGLang

How to use Vortex5/Clockwork-Flower-24B 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 "Vortex5/Clockwork-Flower-24B" \
    --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": "Vortex5/Clockwork-Flower-24B",
		"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 "Vortex5/Clockwork-Flower-24B" \
        --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": "Vortex5/Clockwork-Flower-24B",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use Vortex5/Clockwork-Flower-24B with Docker Model Runner:
```
docker model run hf.co/Vortex5/Clockwork-Flower-24B
```

Clockwork-Flower-24B

File size: 1,086 Bytes

---
base_model:
- OddTheGreat/Cogwheel_24b_V.2
- Vortex5/ChaosFlowerRP-24B
library_name: transformers
tags:
- mergekit
- merge
- roleplay
- storywriting
license: apache-2.0
---
# Clockwork-Flower-24B

Clockwork-Flower-24B is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit).

![image/png](https://cdn-uploads.huggingface.co/production/uploads/6669a3a617b838fda45637b8/qT446OD33eL88CYgHzBpt.png)

## Merge Details
### Merge Method

This model was merged using the [SLERP](https://en.wikipedia.org/wiki/Slerp) merge method.

### Models Merged

The following models were included in the merge:
* [OddTheGreat/Cogwheel_24b_V.2](https://huggingface.co/OddTheGreat/Cogwheel_24b_V.2)
* [Vortex5/ChaosFlowerRP-24B](https://huggingface.co/Vortex5/ChaosFlowerRP-24B)

### Configuration

The following YAML configuration was used to produce this model:

```yaml

models:
  - model: Vortex5/ChaosFlowerRP-24B
  - model: OddTheGreat/Cogwheel_24b_V.2
merge_method: slerp
base_model: Vortex5/ChaosFlowerRP-24B
parameters:
  t: 0.5
dtype: bfloat16

```