Instructions to use T145/ZEUS-8B-V30 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use T145/ZEUS-8B-V30 with Transformers:

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

pipe = pipeline("text-generation", model="T145/ZEUS-8B-V30")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("T145/ZEUS-8B-V30")
model = AutoModelForCausalLM.from_pretrained("T145/ZEUS-8B-V30")
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=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use T145/ZEUS-8B-V30 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "T145/ZEUS-8B-V30"
# 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-V30",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/T145/ZEUS-8B-V30

SGLang

How to use T145/ZEUS-8B-V30 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-V30" \
    --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-V30",
		"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-V30" \
        --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-V30",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use T145/ZEUS-8B-V30 with Docker Model Runner:
```
docker model run hf.co/T145/ZEUS-8B-V30
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

ZEUS 8B V30

This model is a merge of the following pre-trained and finetuned LLMs, created using mergekit.

Merge Configuration

The following YAML configuration was used to produce this model:

base_model: T145/KRONOS-8B-V1-P1
dtype: bfloat16
merge_method: dare_ties
name: ZEUS-8B-V30
parameters:
  int8_mask: 1.0
  normalize: 1.0
  random_seed: 145
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: 0.2
  - 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: T145/KRONOS-8B-V1-P1
tokenizer:
  source: union
  tokens:
    <|begin_of_text|>:
      force: true
      source: T145/KRONOS-8B-V1-P1
    <|eot_id|>:
      force: true
      source: T145/KRONOS-8B-V1-P1