Instructions to use bunnycore/Synesthesia-3.1-task_arithmetic with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use bunnycore/Synesthesia-3.1-task_arithmetic with Transformers:

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

pipe = pipeline("text-generation", model="bunnycore/Synesthesia-3.1-task_arithmetic")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("bunnycore/Synesthesia-3.1-task_arithmetic")
model = AutoModelForCausalLM.from_pretrained("bunnycore/Synesthesia-3.1-task_arithmetic")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use bunnycore/Synesthesia-3.1-task_arithmetic with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "bunnycore/Synesthesia-3.1-task_arithmetic"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "bunnycore/Synesthesia-3.1-task_arithmetic",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/bunnycore/Synesthesia-3.1-task_arithmetic

SGLang

How to use bunnycore/Synesthesia-3.1-task_arithmetic 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 "bunnycore/Synesthesia-3.1-task_arithmetic" \
    --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": "bunnycore/Synesthesia-3.1-task_arithmetic",
		"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 "bunnycore/Synesthesia-3.1-task_arithmetic" \
        --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": "bunnycore/Synesthesia-3.1-task_arithmetic",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use bunnycore/Synesthesia-3.1-task_arithmetic with Docker Model Runner:
```
docker model run hf.co/bunnycore/Synesthesia-3.1-task_arithmetic
```

Synesthesia-3.1-task_arithmetic / README.md

bunnycore

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	---
	base_model:
	- Replete-AI/Replete-LLM-V2-Llama-3.1-8b
	- Solshine/reflection-llama-3.1-8B-Solshine-trainround3-16bit
	- unsloth/Meta-Llama-3.1-8B
	- bunnycore/HyperLlama-3.1-8B
	- Replete-AI/Replete-Coder-V2-Llama-3.1-8b
	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 [task arithmetic](https://arxiv.org/abs/2212.04089) merge method using [unsloth/Meta-Llama-3.1-8B](https://huggingface.co/unsloth/Meta-Llama-3.1-8B) as a base.

	### Models Merged

	The following models were included in the merge:
	* [Replete-AI/Replete-LLM-V2-Llama-3.1-8b](https://huggingface.co/Replete-AI/Replete-LLM-V2-Llama-3.1-8b)
	* [Solshine/reflection-llama-3.1-8B-Solshine-trainround3-16bit](https://huggingface.co/Solshine/reflection-llama-3.1-8B-Solshine-trainround3-16bit)
	* [bunnycore/HyperLlama-3.1-8B](https://huggingface.co/bunnycore/HyperLlama-3.1-8B)
	* [Replete-AI/Replete-Coder-V2-Llama-3.1-8b](https://huggingface.co/Replete-AI/Replete-Coder-V2-Llama-3.1-8b)

	### Configuration

	The following YAML configuration was used to produce this model:

	```yaml
	models:
	- model: Replete-AI/Replete-LLM-V2-Llama-3.1-8b
	parameters:
	density: 0.5
	weight: 0.5
	- model: bunnycore/HyperLlama-3.1-8B
	parameters:
	density: 0.5
	weight: 0.5
	- model: Replete-AI/Replete-Coder-V2-Llama-3.1-8b
	parameters:
	density: 0.5
	weight: 0.5
	- model: Solshine/reflection-llama-3.1-8B-Solshine-trainround3-16bit
	parameters:
	density: 0.5
	weight: 0.5

	merge_method: task_arithmetic
	base_model: unsloth/Meta-Llama-3.1-8B
	parameters:
	normalize: false
	int8_mask: true
	dtype: float16
	```