Instructions to use PracticeLLM/Custom-KoLLM-13B-v3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use PracticeLLM/Custom-KoLLM-13B-v3 with Transformers:

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

pipe = pipeline("text-generation", model="PracticeLLM/Custom-KoLLM-13B-v3")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("PracticeLLM/Custom-KoLLM-13B-v3")
model = AutoModelForCausalLM.from_pretrained("PracticeLLM/Custom-KoLLM-13B-v3")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use PracticeLLM/Custom-KoLLM-13B-v3 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "PracticeLLM/Custom-KoLLM-13B-v3"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "PracticeLLM/Custom-KoLLM-13B-v3",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/PracticeLLM/Custom-KoLLM-13B-v3

SGLang

How to use PracticeLLM/Custom-KoLLM-13B-v3 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 "PracticeLLM/Custom-KoLLM-13B-v3" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "PracticeLLM/Custom-KoLLM-13B-v3",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

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 "PracticeLLM/Custom-KoLLM-13B-v3" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "PracticeLLM/Custom-KoLLM-13B-v3",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use PracticeLLM/Custom-KoLLM-13B-v3 with Docker Model Runner:
```
docker model run hf.co/PracticeLLM/Custom-KoLLM-13B-v3
```

kyujinpy commited on Nov 26, 2023

Commit

b740421

1 Parent(s): 8cedaa4

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@@ -29,11 +29,23 @@ license: cc-by-nc-sa-4.0
 | Model | Average | Ko-ARC | Ko-HellaSwag | Ko-MMLU | Ko-TruthfulQA | Ko-CommonGen V2 |
 | --- | --- | --- | --- | --- | --- | --- |
-| ⭐My custom LLM 13B-v1⭐ | 50.19 | 45.99 | 56.93 | 41.78 | 41.66 | **64.58** |
-| ⭐My custom LLM 13B v2⭐ | 48.28 | 45.73 | 56.97 | 38.77 | 38.75 | 61.16 |
-| ⭐My custom LLM 13B v3⭐ | 46.40 | 44.71 | 56.89 | 40.86 | 44.22 | 45.34 |
 # Implementation Code
 ```python
 ### KO-Platypus

 | Model | Average | Ko-ARC | Ko-HellaSwag | Ko-MMLU | Ko-TruthfulQA | Ko-CommonGen V2 |
 | --- | --- | --- | --- | --- | --- | --- |
+| ⭐My custom LLM 13B-v1⭐ | **50.19** | **45.99** | 56.93 | **41.78** | 41.66 | **64.58** |
+| ⭐My custom LLM 13B-v2⭐ | 48.28 | 45.73 | **56.97** | 38.77 | 38.75 | 61.16 |
+| **⭐My custom LLM 13B-v3⭐** | 46.40 | 44.71 | 56.89 | 40.86 | **44.22** | 45.34 |
+---
+# Model comparisons2
+> AI-Harness evaluation; [link](https://github.com/Beomi/ko-lm-evaluation-harness)
+| Model | Copa | Copa | HellaSwag | HellaSwag | BoolQ | BoolQ | Sentineg | Sentineg |
+| --- | --- | --- | --- | --- | --- | --- | --- | --- |
+|  | 0-shot | 5-shot | 0-shot | 5-shot | 0-shot | 5-shot | 0-shot | 5-shot |
+| ⭐My custom LLM 13B-v1⭐ | 0.7987 | 0.8269 | 0.4994 | 0.5660 | 0.3343 | 0.5060 | **0.6984** | 0.9723 |
+| ⭐My custom LLM 13B-v2⭐ | 0.7938 | 0.8209 | 0.4978 | 0.4893 | 0.3343 | 0.5614 | 0.6283 | 0.9773 |
+| **⭐My custom LLM 13B-v3⭐** | **0.8107** | 0.8359 | **0.5176** | 0.5182 | **0.6702** | 0.7851 | 0.5241 | 0.9698 |
+| [beomi/llama-2-koen-13b](https://huggingface.co/beomi/llama-2-koen-13b) | 0.7768 | 0.8128 | 0.4999 | 0.5127 | 0.3988 | 0.7038 | 0.5870 | 0.9748 |
+---
 # Implementation Code
 ```python
 ### KO-Platypus