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

Libraries

How to use sethuiyer/Llamaverse-3.1-8B-Instruct with Transformers:

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

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

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("sethuiyer/Llamaverse-3.1-8B-Instruct")
model = AutoModelForCausalLM.from_pretrained("sethuiyer/Llamaverse-3.1-8B-Instruct")
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]:]))

Inference
Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use sethuiyer/Llamaverse-3.1-8B-Instruct with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "sethuiyer/Llamaverse-3.1-8B-Instruct"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "sethuiyer/Llamaverse-3.1-8B-Instruct",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/sethuiyer/Llamaverse-3.1-8B-Instruct

SGLang

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

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

Llamaverse 8B

A Unified Multidisciplinary Language Model
Llamaverse-3.1-8B-Instruct is a state-of-the-art language model built on the foundation of MathCoder2-Llama-3-8B, pretrained on MathCode-Pile. This dataset, which embeds mathematical reasoning steps in natural language and code, provides a rock-solid foundation for advanced logical reasoning. By merging MathCoder2 with 10 specialized models using the Model Stock merge method, Llamaverse-3.1-8B-Instruct becomes an unparalleled polymath, excelling in mathematics, biomedical diagnostics, storytelling, coding, and more.

This model was merged using the Model Stock merge method using MathGenie/MathCoder2-Llama-3-8B as a base.

This model works amazing with the Divine Intellect preset!

Models Merged

The following models were included in the merge:

Model Stock Merge Ensures balanced integration of diverse expertise without dominance by any single model.

Multidisciplinary Expertise

Llamaverse-3.1-8B-Instruct integrates the strengths of 10 specialized models, including:

ruslandev/llama-3-8b-samantha: Empathetic and human-like interaction capabilities.
nvidia/OpenMath2-Llama3.1-8B: Advanced mathematical problem-solving.
NeverSleep/Llama-3-Lumimaid-8B: Creative storytelling and roleplay.
TsinghuaC3I/Llama-3.1-8B-UltraMedical: Clinical-grade biomedical insights.
tohur/natsumura-storytelling-rp: Immersive narrative generation.
Skywork/Skywork-o1-Open-Llama-3.1-8B: Reflective reasoning and complex problem-solving.
Undi95/Llama-3-LewdPlay-8B: Unconventional creativity and boundary-pushing dialogue.
Locutusque/Hercules-6.1-Llama-3.1-8B: Expertise in physics, biology, chemistry, and engineering.
RefuelAI/Llama-3-Refueled: Robust NLP capabilities for classification and entity extraction.
rombodawg/Llama-3-8B-Instruct-Coder: Efficient coding and software development.

Ethical Considerations

Llamaverse-3.1-8B-Instruct must be used responsibly. Users should:

Avoid deploying the model in high-stakes scenarios without human oversight.
Be mindful of potential biases and ethical concerns, particularly in sensitive applications.
Use the model’s creative and unconventional capabilities responsibly.

Configuration

The following YAML configuration was used to produce this model:


models:
  - model: nvidia/OpenMath2-Llama3.1-8B
  - model: Locutusque/Hercules-6.1-Llama-3.1-8B
  - model: RefuelAI/Llama-3-Refueled
  - model: Undi95/Llama-3-LewdPlay-8B
  - model: ruslandev/llama-3-8b-samantha
  - model: rombodawg/Llama-3-8B-Instruct-Coder
  - model: Skywork/Skywork-o1-Open-Llama-3.1-8B
  - model: NeverSleep/Llama-3-Lumimaid-8B-v0.1-OAS
  - model: TsinghuaC3I/Llama-3.1-8B-UltraMedical
  - model: tohur/natsumura-storytelling-rp-1.0-llama-3.1-8b
  
merge_method: model_stock
base_model: MathGenie/MathCoder2-Llama-3-8B
dtype: bfloat16

Built by merging models from MathGenie, ruslandev, nvidia, NeverSleep, TsinghuaC3I, tohur, Skywork, Undi95, Locutusque, RefuelAI, and rombodawg. Released under the Llama 3.1 Community License.