Instructions to use dnhkng/RYS-XLarge with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use dnhkng/RYS-XLarge with Transformers:

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

pipe = pipeline("text-generation", model="dnhkng/RYS-XLarge")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("dnhkng/RYS-XLarge")
model = AutoModelForCausalLM.from_pretrained("dnhkng/RYS-XLarge")
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 dnhkng/RYS-XLarge with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/dnhkng/RYS-XLarge

SGLang

How to use dnhkng/RYS-XLarge 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 "dnhkng/RYS-XLarge" \
    --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": "dnhkng/RYS-XLarge",
		"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 "dnhkng/RYS-XLarge" \
        --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": "dnhkng/RYS-XLarge",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

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

This is a new kind of model optimization. It is based on a new method for the analysis of the functional role of layers within the transformer stack, and on layer duplication (self-merging) to increase intelligence.

No Weights were modified in this process!

Model improvement with layer duplication:

	Average	IFEval	BBH	MATH Lvl 5	GPQA	MUSR	MMLU-PRO
RYS Improvement	2.61%	-2.05%	2.51%	8.16%	2.58%	17.72%	0.31%

This model is based on MaziyarPanahi/calme-2.1-qwen2-72b, which in turn was tuned from Qwen2-72B. As this method is orthogonal to fine-tuning, the further finetune from MaziyarPanahi now has the top position: https://huggingface.co/MaziyarPanahi/calme-2.4-rys-78b

A paper on the technique is currently being written. Currently, all four top models on the leaderboard are based on the RYS method. Special thanks to my wife, for putting up with me coding in the basement for too many evenings and weekends for months!

This research was supported with hardware from the appliedAI Institute, whose goal is to generate and communicate high-quality knowledge about trustworthy AI.

Quickstart

Here is a code snippet with apply_chat_template to show you how to load the tokenizer and model and how to generate content.

from transformers import AutoModelForCausalLM, AutoTokenizer
device = "cuda" # the device to load the model onto

model = AutoModelForCausalLM.from_pretrained(
    "dnhkng/RYS-XLarge",
    torch_dtype="auto",
    device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained("dnhkng/RYS-XLarge")

prompt = "Give me a short introduction to large language model."
messages = [
    {"role": "system", "content": "You are a helpful assistant."},
    {"role": "user", "content": prompt}
]
text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(device)

generated_ids = model.generate(
    model_inputs.input_ids,
    max_new_tokens=512
)
generated_ids = [
    output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]

response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]

Open LLM Leaderboard Evaluation Results

Detailed results can be found here

Metric	Value
Avg.	44.75
IFEval (0-Shot)	79.96
BBH (3-Shot)	58.77
MATH Lvl 5 (4-Shot)	38.97
GPQA (0-shot)	17.90
MuSR (0-shot)	23.72
MMLU-PRO (5-shot)	49.20

ADVERTISING BREAK

I’m on the hunt for new challenges and a chance to dive into some exciting research opportunities. Oh, and did I mention I just snagged a top spot on the Open LLM leaderboard? 🎉

Profile

Innovation enthusiast, AI strategist, and interdisciplinary-tech nerd – that's me! With over a decade of experience in research and project management, my professional journey has been largely shaped by my passion for artificial intelligence and its potential to transform various industries. With a solid background in artificial intelligence and machine learning, coupled with a knack for innovation and problem-solving (and a healthy dose of curiosity), I'm excited to bring my skills to a new team.

Originally from Australia, where I earned my degrees in Organic Chemistry and Biochemistry, I moved to Germany in 2004. My academic pursuit continued with a Ph.D. in Chemistry at the Max Planck Institute of Biochemistry. Today, I leverage my robust educational background and diverse industry experience to drive AI innovations in a wide range of applications. Hobbies? Lots: I've also built the world's most powerful espresso machine and am working to bring GLaDOS to life.

I'm based out of Munich, Germany, but I would be interested in working remotely for a team with more compute than my ~~2x 4090s~~ 4x H100s (thanks appliedAI!) 🚀

Reach out via LinkedIn - Dr David Noel Ng

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Model tree for dnhkng/RYS-XLarge

Finetunes

4 models

Quantizations

5 models

Spaces using dnhkng/RYS-XLarge 4

Evaluation results

strict accuracy on IFEval (0-Shot)
Open LLM Leaderboard

79.960
normalized accuracy on BBH (3-Shot)
Open LLM Leaderboard

58.770
exact match on MATH Lvl 5 (4-Shot)
Open LLM Leaderboard

38.970
acc_norm on GPQA (0-shot)
Open LLM Leaderboard

17.900
acc_norm on MuSR (0-shot)
Open LLM Leaderboard

23.720
accuracy on MMLU-PRO (5-shot)
test set Open LLM Leaderboard

49.200