Instructions to use giprime/OOM-SOLAR-10.7B_02 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use giprime/OOM-SOLAR-10.7B_02 with Transformers:

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

pipe = pipeline("text-generation", model="giprime/OOM-SOLAR-10.7B_02")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("giprime/OOM-SOLAR-10.7B_02")
model = AutoModelForCausalLM.from_pretrained("giprime/OOM-SOLAR-10.7B_02", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use giprime/OOM-SOLAR-10.7B_02 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "giprime/OOM-SOLAR-10.7B_02"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "giprime/OOM-SOLAR-10.7B_02",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/giprime/OOM-SOLAR-10.7B_02

SGLang

How to use giprime/OOM-SOLAR-10.7B_02 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 "giprime/OOM-SOLAR-10.7B_02" \
    --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": "giprime/OOM-SOLAR-10.7B_02",
		"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 "giprime/OOM-SOLAR-10.7B_02" \
        --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": "giprime/OOM-SOLAR-10.7B_02",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use giprime/OOM-SOLAR-10.7B_02 with Docker Model Runner:
```
docker model run hf.co/giprime/OOM-SOLAR-10.7B_02
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Model Architecture

OOM-SOLAR-10.7B_02 is an language model that uses an optimized transformer architecture based on upstage/SOLAR-10.7B-v1.0.

Model description

Based on "beomi/OPEN-SOLAR-KO-10.7B"

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 5e-05
train_batch_size: 2
eval_batch_size: 8
seed: 24
gradient_accumulation_steps: 1
total_train_batch_size:
num_epochs: 1.0

Training results

Framework versions

Transformers 4.37.2
Pytorch 2.2.1+cu121
Datasets 2.18.0
Tokenizers 0.15.2

Downloads last month: 20

Safetensors

Model size

11B params

Tensor type

F16

Model tree for giprime/OOM-SOLAR-10.7B_02

Quantizations

2 models