Instructions to use PracticeLLM/Custom-KoLLM-13B-v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use PracticeLLM/Custom-KoLLM-13B-v1 with Transformers:

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

pipe = pipeline("text-generation", model="PracticeLLM/Custom-KoLLM-13B-v1")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("PracticeLLM/Custom-KoLLM-13B-v1")
model = AutoModelForCausalLM.from_pretrained("PracticeLLM/Custom-KoLLM-13B-v1")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use PracticeLLM/Custom-KoLLM-13B-v1 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-v1"
# 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-v1",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/PracticeLLM/Custom-KoLLM-13B-v1

SGLang

How to use PracticeLLM/Custom-KoLLM-13B-v1 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-v1" \
    --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-v1",
		"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-v1" \
        --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-v1",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use PracticeLLM/Custom-KoLLM-13B-v1 with Docker Model Runner:
```
docker model run hf.co/PracticeLLM/Custom-KoLLM-13B-v1
```

kyujinpy commited on Nov 25, 2023

Commit

ece285b

1 Parent(s): 4b87796

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@@ -38,7 +38,7 @@ license: cc-by-nc-sa-4.0
 | 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⭐** | NaN | 0.8269 | NaN | 0.5660 | NaN | 0.5060 | NaN | 0.9723 |
 | [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 |
 ---

 | 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⭐** | 0.7987 | 0.8269 | 0.4994 | 0.5660 | 0.3343 | 0.5060 | 0.6984 | 0.9723 |
 | [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 |
 ---