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

Libraries

How to use dnhkng/RYS-Medium with Transformers:

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

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

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("dnhkng/RYS-Medium", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("dnhkng/RYS-Medium", trust_remote_code=True)
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-Medium with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "dnhkng/RYS-Medium"
# 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-Medium",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

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

SGLang

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

Docker Model Runner
How to use dnhkng/RYS-Medium with Docker Model Runner:
```
docker model run hf.co/dnhkng/RYS-Medium
```

Adding Evaluation Results

by leaderboard-pr-bot - opened Aug 19, 2024

base: refs/heads/main

←

from: refs/pr/2

Discussion Files changed

+32

-0

Files changed (1) hide show

README.md +32 -0

README.md CHANGED Viewed

@@ -12,6 +12,9 @@ model-index:
       args:
         num_few_shot: 0
     metrics:
     - type: inst_level_strict_acc and prompt_level_strict_acc
       value: 44.06
       name: strict accuracy
@@ -27,6 +30,9 @@ model-index:
       args:
         num_few_shot: 3
     metrics:
     - type: acc_norm
       value: 47.73
       name: normalized accuracy
@@ -42,6 +48,9 @@ model-index:
       args:
         num_few_shot: 4
     metrics:
     - type: exact_match
       value: 7.78
       name: exact match
@@ -57,6 +66,9 @@ model-index:
       args:
         num_few_shot: 0
     metrics:
     - type: acc_norm
       value: 10.4
       name: acc_norm
@@ -72,6 +84,9 @@ model-index:
       args:
         num_few_shot: 0
     metrics:
     - type: acc_norm
       value: 8.73
       name: acc_norm
@@ -89,6 +104,9 @@ model-index:
       args:
         num_few_shot: 5
     metrics:
     - type: acc
       value: 36.96
       name: accuracy
@@ -172,3 +190,17 @@ ___________________________________
 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 🚀
 #### Reach out via [LinkedIn - Dr David Noel Ng](https://www.linkedin.com/in/dnhkng)

       args:
         num_few_shot: 0
     metrics:
+    - type: inst_level_strict_acc and prompt_level_strict_acc
+      value: 44.06
+      name: strict accuracy
     - type: inst_level_strict_acc and prompt_level_strict_acc
       value: 44.06
       name: strict accuracy
       args:
         num_few_shot: 3
     metrics:
+    - type: acc_norm
+      value: 47.73
+      name: normalized accuracy
     - type: acc_norm
       value: 47.73
       name: normalized accuracy
       args:
         num_few_shot: 4
     metrics:
+    - type: exact_match
+      value: 7.78
+      name: exact match
     - type: exact_match
       value: 7.78
       name: exact match
       args:
         num_few_shot: 0
     metrics:
+    - type: acc_norm
+      value: 10.4
+      name: acc_norm
     - type: acc_norm
       value: 10.4
       name: acc_norm
       args:
         num_few_shot: 0
     metrics:
+    - type: acc_norm
+      value: 8.73
+      name: acc_norm
     - type: acc_norm
       value: 8.73
       name: acc_norm
       args:
         num_few_shot: 5
     metrics:
+    - type: acc
+      value: 36.96
+      name: accuracy
     - type: acc
       value: 36.96
       name: accuracy
 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 🚀
 #### Reach out via [LinkedIn - Dr David Noel Ng](https://www.linkedin.com/in/dnhkng)
+# [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard)
+Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_dnhkng__RYS-Medium)
+|      Metric       |Value|
+|-------------------|----:|
+|Avg.               |25.94|
+|IFEval (0-Shot)    |44.06|
+|BBH (3-Shot)       |47.73|
+|MATH Lvl 5 (4-Shot)| 7.78|
+|GPQA (0-shot)      |10.40|
+|MuSR (0-shot)      | 8.73|
+|MMLU-PRO (5-shot)  |36.96|