Instructions to use PracticeLLM/Custom-KoLLM-13B-v4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use PracticeLLM/Custom-KoLLM-13B-v4 with Transformers:

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

pipe = pipeline("text-generation", model="PracticeLLM/Custom-KoLLM-13B-v4")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("PracticeLLM/Custom-KoLLM-13B-v4")
model = AutoModelForCausalLM.from_pretrained("PracticeLLM/Custom-KoLLM-13B-v4")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use PracticeLLM/Custom-KoLLM-13B-v4 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-v4"
# 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-v4",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/PracticeLLM/Custom-KoLLM-13B-v4

SGLang

How to use PracticeLLM/Custom-KoLLM-13B-v4 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-v4" \
    --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-v4",
		"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-v4" \
        --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-v4",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use PracticeLLM/Custom-KoLLM-13B-v4 with Docker Model Runner:
```
docker model run hf.co/PracticeLLM/Custom-KoLLM-13B-v4
```

kyujinpy commited on Nov 28, 2023

Commit

84e8fe3

1 Parent(s): 6485918

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@@ -29,9 +29,9 @@ 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-v4⭐** | NaN | NaN | NaN | NaN | NaN | NaN |
 ---
 # Model comparisons2

 | 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-v4⭐** | 49.89 | 45.05 | **57.06** | **41.83** | **42.93** | 62.57 |
 ---
 # Model comparisons2