Instructions to use PracticeLLM/SOLAR-tail-10.7B-Merge-v1.0 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use PracticeLLM/SOLAR-tail-10.7B-Merge-v1.0 with Transformers:

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

pipe = pipeline("text-generation", model="PracticeLLM/SOLAR-tail-10.7B-Merge-v1.0")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("PracticeLLM/SOLAR-tail-10.7B-Merge-v1.0")
model = AutoModelForCausalLM.from_pretrained("PracticeLLM/SOLAR-tail-10.7B-Merge-v1.0", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use PracticeLLM/SOLAR-tail-10.7B-Merge-v1.0 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "PracticeLLM/SOLAR-tail-10.7B-Merge-v1.0"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "PracticeLLM/SOLAR-tail-10.7B-Merge-v1.0",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/PracticeLLM/SOLAR-tail-10.7B-Merge-v1.0

SGLang

How to use PracticeLLM/SOLAR-tail-10.7B-Merge-v1.0 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/SOLAR-tail-10.7B-Merge-v1.0" \
    --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/SOLAR-tail-10.7B-Merge-v1.0",
		"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/SOLAR-tail-10.7B-Merge-v1.0" \
        --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/SOLAR-tail-10.7B-Merge-v1.0",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use PracticeLLM/SOLAR-tail-10.7B-Merge-v1.0 with Docker Model Runner:
```
docker model run hf.co/PracticeLLM/SOLAR-tail-10.7B-Merge-v1.0
```

kyujinpy commited on Dec 28, 2023

Commit

957474e

1 Parent(s): 9234966

Upload README.md

Browse files

Files changed (1) hide show

README.md +1 -3

README.md CHANGED Viewed

@@ -48,10 +48,8 @@ dtype: float16
 | Model | Average | ARC | HellaSwag | MMLU | TruthfulQA | Ko-CommonGenV2 |
 | --- | --- | --- | --- | --- | --- | --- |
-| PracticeLLM/SOLAR-tail-10.7B-Merge-v1.0 | NaN | NaN | NaN | NaN | NaN | NaN |
 | jjourney1125/M-SOLAR-10.7B-v1.0 | 55.15 | 49.57 | 60.12 | 54.60 | 49.23 | 62.22 |
-| beomi/Yi-Ko-6B | 48.79 | 41.04 | 53.39 | 46.28 | 41.64 | 61.63 |
-| mistralai/Mistral-7B-v0.1 | 46.89 | 38.14 | 48.19 | 45.20 | 46.13 | 56.79 |
 - Follow up as [En-link](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
 (coming soon...)

 | Model | Average | ARC | HellaSwag | MMLU | TruthfulQA | Ko-CommonGenV2 |
 | --- | --- | --- | --- | --- | --- | --- |
+| PracticeLLM/SOLAR-tail-10.7B-Merge-v1.0 | 48.32 | 45.73 | 56.97 | 38.77 | 38.75 | 61.16 |
 | jjourney1125/M-SOLAR-10.7B-v1.0 | 55.15 | 49.57 | 60.12 | 54.60 | 49.23 | 62.22 |
 - Follow up as [En-link](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
 (coming soon...)