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

b6fc80a

verified ·

1 Parent(s): 3707c0e

Update README.md

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README.md +5 -5

README.md CHANGED Viewed

@@ -99,15 +99,15 @@ model-index:
 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
@@ -117,7 +117,7 @@ This research was supported with hardware from the [appliedAI Institute](https:/
 ## Quickstart
-Here is a code snippet with `apply_chat_template` to show you how to load the tokenizer and model and how to generate contents.
 ```python
 from transformers import AutoModelForCausalLM, AutoTokenizer

 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
 ## 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.
 ```python
 from transformers import AutoModelForCausalLM, AutoTokenizer