Instructions to use JY623/KoSOLAR-10.7B-merge-v3.0 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use JY623/KoSOLAR-10.7B-merge-v3.0 with Transformers:

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

pipe = pipeline("text-generation", model="JY623/KoSOLAR-10.7B-merge-v3.0")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("JY623/KoSOLAR-10.7B-merge-v3.0")
model = AutoModelForCausalLM.from_pretrained("JY623/KoSOLAR-10.7B-merge-v3.0")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use JY623/KoSOLAR-10.7B-merge-v3.0 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "JY623/KoSOLAR-10.7B-merge-v3.0"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "JY623/KoSOLAR-10.7B-merge-v3.0",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/JY623/KoSOLAR-10.7B-merge-v3.0

SGLang

How to use JY623/KoSOLAR-10.7B-merge-v3.0 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 "JY623/KoSOLAR-10.7B-merge-v3.0" \
    --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": "JY623/KoSOLAR-10.7B-merge-v3.0",
		"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 "JY623/KoSOLAR-10.7B-merge-v3.0" \
        --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": "JY623/KoSOLAR-10.7B-merge-v3.0",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use JY623/KoSOLAR-10.7B-merge-v3.0 with Docker Model Runner:
```
docker model run hf.co/JY623/KoSOLAR-10.7B-merge-v3.0
```

KoSOLAR-10.7B-merge-v3.0

File size: 1,057 Bytes

---
base_model:
- hyeogi/SOLAR-10.7B-v1.5
- yanolja/KoSOLAR-10.7B-v0.2
library_name: transformers
tags:
- mergekit
- merge
license: apache-2.0
---
# slerp_test1

This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit).

## Merge Details
### Merge Method

This model was merged using the SLERP merge method.

### Models Merged

The following models were included in the merge:
* [hyeogi/SOLAR-10.7B-v1.5](https://huggingface.co/hyeogi/SOLAR-10.7B-v1.5)
* [yanolja/KoSOLAR-10.7B-v0.2](https://huggingface.co/yanolja/KoSOLAR-10.7B-v0.2)

### Configuration

The following YAML configuration was used to produce this model:

```yaml
slices:
  - sources:
    - model: yanolja/KoSOLAR-10.7B-v0.2
      layer_range: [0, 48]
    - model: hyeogi/SOLAR-10.7B-v1.5
      layer_range: [0, 48] 

merge_method: slerp
base_model: yanolja/KoSOLAR-10.7B-v0.2
parameters:
  t:
    - filter: self_attn
      value: [0, 0.5, 0.3, 0.7, 1]
    - filter: mlp
      value: [1, 0.5, 0.7, 0.3, 0]
    - value: 0.5
dtype: float16

```