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", device_map="auto")
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 Settings

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
```

dnhkng commited on Sep 12, 2024

Commit

3707c0e

verified ·

1 Parent(s): a28de8f

Update README.md

Browse files

Files changed (1) hide show

README.md +14 -3

README.md CHANGED Viewed

@@ -97,10 +97,21 @@ model-index:
       name: Open LLM Leaderboard
 ---
-This is a new kind of model optimization.
-This model is based on MaziyarPanahi/calme-2.1-qwen2-72b, which was tuned from Qwen2-72B.
-A paper on the technique is currently being written.  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](https://www.appliedai-institute.de/en/), whose goal is to generate and communicate high-quality knowledge about trustworthy AI.

       name: Open LLM Leaderboard
 ---
+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 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](https://www.appliedai-institute.de/en/), whose goal is to generate and communicate high-quality knowledge about trustworthy AI.