Instructions to use oneonlee/LDCC-SOLAR-gugutypus-10.7B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use oneonlee/LDCC-SOLAR-gugutypus-10.7B with Transformers:

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

pipe = pipeline("text-generation", model="oneonlee/LDCC-SOLAR-gugutypus-10.7B")

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("oneonlee/LDCC-SOLAR-gugutypus-10.7B")
model = AutoModelForCausalLM.from_pretrained("oneonlee/LDCC-SOLAR-gugutypus-10.7B", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use oneonlee/LDCC-SOLAR-gugutypus-10.7B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "oneonlee/LDCC-SOLAR-gugutypus-10.7B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "oneonlee/LDCC-SOLAR-gugutypus-10.7B",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/oneonlee/LDCC-SOLAR-gugutypus-10.7B

SGLang

How to use oneonlee/LDCC-SOLAR-gugutypus-10.7B 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 "oneonlee/LDCC-SOLAR-gugutypus-10.7B" \
    --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": "oneonlee/LDCC-SOLAR-gugutypus-10.7B",
		"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 "oneonlee/LDCC-SOLAR-gugutypus-10.7B" \
        --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": "oneonlee/LDCC-SOLAR-gugutypus-10.7B",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use oneonlee/LDCC-SOLAR-gugutypus-10.7B with Docker Model Runner:
```
docker model run hf.co/oneonlee/LDCC-SOLAR-gugutypus-10.7B
```

oneonlee commited on Mar 2, 2024

Commit

99d9151

verified ·

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@@ -38,11 +38,13 @@ pipeline_tag: text-generation
 ## Model comparisons
-- **Ko-LLM leaderboard (YYYY/MM/DD)** [[link]](https://huggingface.co/spaces/upstage/open-ko-llm-leaderboard)
-| Model                 | Average | Ko-ARC | Ko-HellaSwag | Ko-MMLU | Ko-TruthfulQA | Ko-CommonGen V2 |
-| --------------------- | ------- | ------ | ------------ | ------- | ------------- | --------------- |
-| **LDCC-SOLAR-gugutypus-10.7B** | NaN     | NaN    | NaN          | NaN     | NaN           | NaN             |
 <br>

 ## Model comparisons
+- **Ko-LLM leaderboard (2024/03/01)** [[link]](https://huggingface.co/spaces/upstage/open-ko-llm-leaderboard)
+| Model                                     | Average     | Ko-ARC | Ko-HellaSwag | Ko-MMLU | Ko-TruthfulQA | Ko-CommonGen V2 |
+| ----------------------------------------- | ----------- | ------ | ------------ | ------- | ------------- | --------------- |
+| **[oneonlee/KoSOLAR-v0.2-gugutypus-10.7B](https://huggingface.co/oneonlee/KoSOLAR-v0.2-gugutypus-10.7B)** | **51.17**   | 47.78  | 58.29        | 47.27   | 48.31         | 54.19           |
+| oneonlee/LDCC-SOLAR-gugutypus-10.7B  | 49.45   | 45.9   | 55.46        | 47.96   | 48.93         | 49              |
 <br>