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 30, 2023

Commit

b27f33f

1 Parent(s): 957474e

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@@ -52,7 +52,11 @@ dtype: float16
 | 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...)
 ## lm-evaluation-harness
 ```

 | 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).
+| Model | Average | ARC | HellaSwag | MMLU | TruthfulQA | Winogrande | GSM8K |
+| --- | --- | --- | --- | --- | --- | --- | --- |
+| PracticeLLM/SOLAR-tail-10.7B-Merge-v1.0 | 71.68 | 66.13 | 86.54 | **66.52** | 60.57 | **84.77** | **65.58** |
+| kyujinpy/Sakura-SOLAR-Instruct | **74.40** | **70.99** | **88.42** | 66.33 | **71.79** | 83.66 | 65.20 |
 ## lm-evaluation-harness
 ```