Instructions to use yanolja/YanoljaNEXT-EEVE-Instruct-10.8B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use yanolja/YanoljaNEXT-EEVE-Instruct-10.8B with Transformers:

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

pipe = pipeline("text-generation", model="yanolja/YanoljaNEXT-EEVE-Instruct-10.8B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("yanolja/YanoljaNEXT-EEVE-Instruct-10.8B")
model = AutoModelForCausalLM.from_pretrained("yanolja/YanoljaNEXT-EEVE-Instruct-10.8B", 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 yanolja/YanoljaNEXT-EEVE-Instruct-10.8B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "yanolja/YanoljaNEXT-EEVE-Instruct-10.8B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "yanolja/YanoljaNEXT-EEVE-Instruct-10.8B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/yanolja/YanoljaNEXT-EEVE-Instruct-10.8B

SGLang

How to use yanolja/YanoljaNEXT-EEVE-Instruct-10.8B 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 "yanolja/YanoljaNEXT-EEVE-Instruct-10.8B" \
    --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": "yanolja/YanoljaNEXT-EEVE-Instruct-10.8B",
		"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 "yanolja/YanoljaNEXT-EEVE-Instruct-10.8B" \
        --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": "yanolja/YanoljaNEXT-EEVE-Instruct-10.8B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use yanolja/YanoljaNEXT-EEVE-Instruct-10.8B with Docker Model Runner:
```
docker model run hf.co/yanolja/YanoljaNEXT-EEVE-Instruct-10.8B
```

seungduk

leaderboard-pr-bot commited on Mar 7, 2024

Commit

665c18a

verified ·

1 Parent(s): fb3f5e8

Adding Evaluation Results (#2)

Browse files

- Adding Evaluation Results (c210b47f87a50915cf6fda8694d1114193b2eb2b)

Co-authored-by: Open LLM Leaderboard PR Bot <leaderboard-pr-bot@users.noreply.huggingface.co>

Files changed (1) hide show

README.md +15 -2

README.md CHANGED Viewed

@@ -1,8 +1,8 @@
 ---
 license: apache-2.0
-base_model: yanolja/EEVE-Korean-10.8B-v1.0
 tags:
 - generated_from_trainer
 model-index:
 - name: yanolja/EEVE-Korean-Instruct-10.8B-v1.0
   results: []
@@ -115,4 +115,17 @@ Assistant:
       archivePrefix={arXiv},
       primaryClass={cs.CL}
 }
-```

 ---
 license: apache-2.0
 tags:
 - generated_from_trainer
+base_model: yanolja/EEVE-Korean-10.8B-v1.0
 model-index:
 - name: yanolja/EEVE-Korean-Instruct-10.8B-v1.0
   results: []
       archivePrefix={arXiv},
       primaryClass={cs.CL}
 }
+```
+# [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)
+Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_yanolja__EEVE-Korean-Instruct-10.8B-v1.0)
+|             Metric              |Value|
+|---------------------------------|----:|
+|Avg.                             |66.48|
+|AI2 Reasoning Challenge (25-Shot)|64.85|
+|HellaSwag (10-Shot)              |83.04|
+|MMLU (5-Shot)                    |64.23|
+|TruthfulQA (0-shot)              |54.09|
+|Winogrande (5-shot)              |81.93|
+|GSM8k (5-shot)                   |50.72|