Instructions to use gorni123/orkihate with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use gorni123/orkihate with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="gorni123/orkihate")# Load model directly from transformers import AutoTokenizer, AutoModelForMultimodalLM tokenizer = AutoTokenizer.from_pretrained("gorni123/orkihate") model = AutoModelForMultimodalLM.from_pretrained("gorni123/orkihate") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use gorni123/orkihate with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "gorni123/orkihate" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "gorni123/orkihate", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/gorni123/orkihate
- SGLang
How to use gorni123/orkihate 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 "gorni123/orkihate" \ --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": "gorni123/orkihate", "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 "gorni123/orkihate" \ --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": "gorni123/orkihate", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use gorni123/orkihate with Docker Model Runner:
docker model run hf.co/gorni123/orkihate
orkihate
This model is a fine-tuned version of EleutherAI/gpt-neo-125M on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.4899
Model description
More information needed
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: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 10
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| 0.871 | 1.0 | 859 | 0.8495 |
| 0.5779 | 2.0 | 1718 | 0.6016 |
| 0.3873 | 3.0 | 2577 | 0.5525 |
| 0.3259 | 4.0 | 3436 | 0.5166 |
| 0.2787 | 5.0 | 4295 | 0.4948 |
| 0.2748 | 6.0 | 5154 | 0.4909 |
| 0.2493 | 7.0 | 6013 | 0.4793 |
| 0.2314 | 8.0 | 6872 | 0.4846 |
| 0.1996 | 9.0 | 7731 | 0.4849 |
| 0.1926 | 10.0 | 8590 | 0.4899 |
Framework versions
- Transformers 4.54.1
- Pytorch 2.6.0+cu124
- Datasets 4.0.0
- Tokenizers 0.21.4
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Model tree for gorni123/orkihate
Base model
EleutherAI/gpt-neo-125m