Instructions to use rrw-x2/KoSOLAR-10.9B-v0.5 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use rrw-x2/KoSOLAR-10.9B-v0.5 with Transformers:

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

pipe = pipeline("text-generation", model="rrw-x2/KoSOLAR-10.9B-v0.5")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("rrw-x2/KoSOLAR-10.9B-v0.5")
model = AutoModelForCausalLM.from_pretrained("rrw-x2/KoSOLAR-10.9B-v0.5", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use rrw-x2/KoSOLAR-10.9B-v0.5 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "rrw-x2/KoSOLAR-10.9B-v0.5"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "rrw-x2/KoSOLAR-10.9B-v0.5",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/rrw-x2/KoSOLAR-10.9B-v0.5

SGLang

How to use rrw-x2/KoSOLAR-10.9B-v0.5 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 "rrw-x2/KoSOLAR-10.9B-v0.5" \
    --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": "rrw-x2/KoSOLAR-10.9B-v0.5",
		"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 "rrw-x2/KoSOLAR-10.9B-v0.5" \
        --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": "rrw-x2/KoSOLAR-10.9B-v0.5",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use rrw-x2/KoSOLAR-10.9B-v0.5 with Docker Model Runner:
```
docker model run hf.co/rrw-x2/KoSOLAR-10.9B-v0.5
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

merge

This is a merge of pre-trained language models created using mergekit.

Merge Details

Merge Method

This model was merged using the SLERP merge method.

Models Merged

The following models were included in the merge:

Configuration

The following YAML configuration was used to produce this model:



slices:

  - sources:

      - model: LDCC/LDCC-SOLAR-10.7B

        layer_range: [0, 48]

      - model: hyeogi/SOLAR-10.7B-dpo-v1

        layer_range: [0, 48]

merge_method: slerp
tokenizer_source: base
base_model: LDCC/LDCC-SOLAR-10.7B

embed_slerp: true

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: bfloat16

Datasets

Finetuned using LoRA with kyujinpy/OpenOrca-KO

Downloads last month: 5

Safetensors

Model size

11B params

Tensor type

F16

Model tree for rrw-x2/KoSOLAR-10.9B-v0.5

LDCC/LDCC-SOLAR-10.7B

hyeogi/SOLAR-10.7B-dpo-v1

Merge model

this model

Quantizations

3 models