Instructions to use T145/Llama-3.1-8B-Instruct-Zeus with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use T145/Llama-3.1-8B-Instruct-Zeus with Transformers:

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

pipe = pipeline("text-generation", model="T145/Llama-3.1-8B-Instruct-Zeus")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("T145/Llama-3.1-8B-Instruct-Zeus")
model = AutoModelForCausalLM.from_pretrained("T145/Llama-3.1-8B-Instruct-Zeus")
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/Llama-3.1-8B-Instruct-Zeus with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "T145/Llama-3.1-8B-Instruct-Zeus"
# 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/Llama-3.1-8B-Instruct-Zeus",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/T145/Llama-3.1-8B-Instruct-Zeus

SGLang

How to use T145/Llama-3.1-8B-Instruct-Zeus 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/Llama-3.1-8B-Instruct-Zeus" \
    --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/Llama-3.1-8B-Instruct-Zeus",
		"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/Llama-3.1-8B-Instruct-Zeus" \
        --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/Llama-3.1-8B-Instruct-Zeus",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use T145/Llama-3.1-8B-Instruct-Zeus with Docker Model Runner:
```
docker model run hf.co/T145/Llama-3.1-8B-Instruct-Zeus
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

A newer version of this model is available: T145/ZEUS-8B-V22

ZEUS

Taking inspiration from Dampfinchen/Llama-3.1-8B-Ultra-Instruct and brucethemoose, the goal of this merge is to create an abliterated, conversational AI within 8B parameters that's coherent over long conversations. Using "Ultra-Instruct" as a baseline (which has problems with grammar and coherent conversations), preliminary results seem to show these goals are met. Expect responses in the Markdown format by default.

Merge Details

Merge Method

This model was merged using the DARE TIES merge method using meta-llama/Llama-3.1-8B-Instruct as a base.

Models Merged

The following models were included in the merge:

Configuration

The following YAML configuration was used to produce this model:

base_model: meta-llama/Llama-3.1-8B-Instruct
dtype: bfloat16
merge_method: dare_ties
parameters:
  int8_mask: 1.0
slices:
- sources:
  - layer_range: [0, 32]
    model: akjindal53244/Llama-3.1-Storm-8B
    parameters:
      density: 0.7
      weight: 0.2
  - layer_range: [0, 32]
    model: arcee-ai/Llama-3.1-SuperNova-Lite
    parameters:
      density: 0.7
      weight: 0.3
  - layer_range: [0, 32]
    model: Orenguteng/Llama-3.1-8B-Lexi-Uncensored-V2
    parameters:
      density: 0.7
      weight: 0.5
  - layer_range: [0, 32]
    model: meta-llama/Llama-3.1-8B-Instruct
tokenizer_source: meta-llama/Llama-3.1-8B-Instruct