Instructions to use leo911kim/Exodia-7B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use leo911kim/Exodia-7B with Transformers:

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

pipe = pipeline("text-generation", model="leo911kim/Exodia-7B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("leo911kim/Exodia-7B")
model = AutoModelForCausalLM.from_pretrained("leo911kim/Exodia-7B")
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 leo911kim/Exodia-7B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "leo911kim/Exodia-7B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "leo911kim/Exodia-7B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/leo911kim/Exodia-7B

SGLang

How to use leo911kim/Exodia-7B 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 "leo911kim/Exodia-7B" \
    --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": "leo911kim/Exodia-7B",
		"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 "leo911kim/Exodia-7B" \
        --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": "leo911kim/Exodia-7B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use leo911kim/Exodia-7B with Docker Model Runner:
```
docker model run hf.co/leo911kim/Exodia-7B
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Master of Merging

The Large Language Model, or LLM, represents a groundbreaking advancement in the realm of artificial intelligence. By fusing together insights and data from various individual models, the LLM is designed to harness the best of each while mitigating their individual weaknesses. This amalgamation allows the LLM to demonstrate unparalleled capability in understanding context, generating accurate content, and adapting to diverse tasks. The integrated approach ensures that users benefit from increased accuracy, wider knowledge coverage, and a more nuanced understanding of both structured and unstructured data. Essentially, the LLM epitomizes the next step in the evolution of AI, bringing about a model that is greater than the sum of its parts.

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