Instructions to use T145/ZEUS-8B-V27 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use T145/ZEUS-8B-V27 with Transformers:

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

pipe = pipeline("text-generation", model="T145/ZEUS-8B-V27")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("T145/ZEUS-8B-V27")
model = AutoModelForCausalLM.from_pretrained("T145/ZEUS-8B-V27", device_map="auto")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=256)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use T145/ZEUS-8B-V27 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "T145/ZEUS-8B-V27"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "T145/ZEUS-8B-V27",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/T145/ZEUS-8B-V27

SGLang

How to use T145/ZEUS-8B-V27 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 "T145/ZEUS-8B-V27" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "T145/ZEUS-8B-V27",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

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 "T145/ZEUS-8B-V27" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "T145/ZEUS-8B-V27",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use T145/ZEUS-8B-V27 with Docker Model Runner:
```
docker model run hf.co/T145/ZEUS-8B-V27
```

ZEUS-8B-V27 / mergekit_config.yml

T145

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	base_model: unsloth/Meta-Llama-3.1-8B-Instruct
	dtype: bfloat16
	merge_method: dare_ties
	parameters:
	int8_mask: 1.0
	normalize: 1.0
	random_seed: 145.0
	slices:
	- sources:
	- layer_range: [0, 32]
	model: unsloth/Llama-3.1-Storm-8B
	parameters:
	density: 0.94
	weight: 0.35
	- layer_range: [0, 32]
	model: arcee-ai/Llama-3.1-SuperNova-Lite
	parameters:
	density: 0.92
	weight: 0.26
	- layer_range: [0, 32]
	model: VAGOsolutions/Llama-3.1-SauerkrautLM-8b-Instruct
	parameters:
	density: 0.91
	weight:
	- filter: layers.20.
	value: 0.0
	- filter: layers.21.
	value: 0.0
	- filter: layers.22.
	value: 0.0
	- filter: layers.23.
	value: 0.0
	- filter: layers.24.
	value: 0.0
	- filter: layers.25.
	value: 0.0
	- filter: layers.26.
	value: 0.0
	- filter: layers.27.
	value: 0.0
	- value: 0.2
	- layer_range: [0, 32]
	model: output\strawberry-patch
	parameters:
	density: 0.92
	weight:
	- filter: layers.20.
	value: 0.2
	- filter: layers.21.
	value: 0.2
	- filter: layers.22.
	value: 0.2
	- filter: layers.23.
	value: 0.2
	- filter: layers.24.
	value: 0.2
	- filter: layers.25.
	value: 0.2
	- filter: layers.26.
	value: 0.2
	- filter: layers.27.
	value: 0.2
	- value: 0.0
	- layer_range: [0, 32]
	model: Orenguteng/Llama-3.1-8B-Lexi-Uncensored-V2
	parameters:
	density: 0.93
	weight: 0.19
	- layer_range: [0, 32]
	model: unsloth/Meta-Llama-3.1-8B-Instruct
	tokenizer:
	tokens:
	<\|begin_of_text\|>:
	force: true
	source: unsloth/Meta-Llama-3.1-8B-Instruct
	<\|eot_id\|>:
	force: true
	source: unsloth/Meta-Llama-3.1-8B-Instruct
	<\|finetune_right_pad_id\|>:
	force: true
	source: unsloth/Meta-Llama-3.1-8B-Instruct