Instructions to use giprime/OOM-13B_01 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use giprime/OOM-13B_01 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="giprime/OOM-13B_01")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("giprime/OOM-13B_01") model = AutoModelForCausalLM.from_pretrained("giprime/OOM-13B_01") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use giprime/OOM-13B_01 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "giprime/OOM-13B_01" # 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-13B_01", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/giprime/OOM-13B_01
- SGLang
How to use giprime/OOM-13B_01 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-13B_01" \ --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-13B_01", "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-13B_01" \ --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-13B_01", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use giprime/OOM-13B_01 with Docker Model Runner:
docker model run hf.co/giprime/OOM-13B_01
Model Architecture
OOM-13B_01 is an language model that uses an optimized transformer architecture based on Llama-2.
Model description
Based on "beomi/llama-2-koen-13b"
Intended uses & limitations
T.B.D.
Training and evaluation data
T.B.D.
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-04
- train_batch_size: 2
- eval_batch_size: 8
- seed: 24
- gradient_accumulation_steps: 1
- total_train_batch_size:
- num_epochs: 2.0
Training results
Framework versions
- Transformers 4.37.2
- Pytorch 2.2.0+cu118
- Datasets 2.16.1
- Tokenizers 0.15.1
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