Instructions to use PracticeLLM/Custom-KoLLM-13B-v8 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use PracticeLLM/Custom-KoLLM-13B-v8 with Transformers:

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

pipe = pipeline("text-generation", model="PracticeLLM/Custom-KoLLM-13B-v8")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("PracticeLLM/Custom-KoLLM-13B-v8")
model = AutoModelForCausalLM.from_pretrained("PracticeLLM/Custom-KoLLM-13B-v8")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use PracticeLLM/Custom-KoLLM-13B-v8 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-v8"
# 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-v8",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/PracticeLLM/Custom-KoLLM-13B-v8

SGLang

How to use PracticeLLM/Custom-KoLLM-13B-v8 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-v8" \
    --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-v8",
		"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-v8" \
        --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-v8",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use PracticeLLM/Custom-KoLLM-13B-v8 with Docker Model Runner:
```
docker model run hf.co/PracticeLLM/Custom-KoLLM-13B-v8
```

Update README.md

by cherry0328 - opened Jan 6

base: refs/heads/main

←

from: refs/pr/2

Discussion Files changed

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Files changed (1) hide show

README.md +4 -2

README.md CHANGED Viewed

@@ -1,11 +1,13 @@
 ---
 language:
 - ko
-datasets:
 - kyujinpy/OpenOrca-ko-v3
 library_name: transformers
 pipeline_tag: text-generation
 license: cc-by-nc-sa-4.0
 ---
 # **⭐My custom LLM 13B⭐**
@@ -53,4 +55,4 @@ OpenOrca_tokenizer = AutoTokenizer.from_pretrained(repo)
 # Hyperparameters
 - QLoRA
 - lora_target_modules '[gate_proj, down_proj, up_proj]'
-- lora_r 64

 ---
 language:
 - ko
+datasets:
 - kyujinpy/OpenOrca-ko-v3
 library_name: transformers
 pipeline_tag: text-generation
 license: cc-by-nc-sa-4.0
+base_model:
+- beomi/llama-2-koen-13b
 ---
 # **⭐My custom LLM 13B⭐**
 # Hyperparameters
 - QLoRA
 - lora_target_modules '[gate_proj, down_proj, up_proj]'
+- lora_r 64