Instructions to use big-kek/NeuroSkeptic with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use big-kek/NeuroSkeptic with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="big-kek/NeuroSkeptic")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("big-kek/NeuroSkeptic") model = AutoModelForCausalLM.from_pretrained("big-kek/NeuroSkeptic") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use big-kek/NeuroSkeptic with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "big-kek/NeuroSkeptic" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "big-kek/NeuroSkeptic", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/big-kek/NeuroSkeptic
- SGLang
How to use big-kek/NeuroSkeptic 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 "big-kek/NeuroSkeptic" \ --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": "big-kek/NeuroSkeptic", "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 "big-kek/NeuroSkeptic" \ --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": "big-kek/NeuroSkeptic", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use big-kek/NeuroSkeptic with Docker Model Runner:
docker model run hf.co/big-kek/NeuroSkeptic
opt-model
This model is a fine-tuned version of facebook/opt-13b on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 2.3965
- Accuracy: 0.5020
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: 1e-05
- train_batch_size: 24
- eval_batch_size: 24
- seed: 42
- distributed_type: multi-GPU
- num_devices: 3
- total_train_batch_size: 72
- total_eval_batch_size: 72
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5.0
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 3.6363 | 1.0 | 3 | 3.2090 | 0.4082 |
| 2.8168 | 2.0 | 6 | 2.4805 | 0.4874 |
| 2.3529 | 3.0 | 9 | 2.4219 | 0.4915 |
| 2.1842 | 4.0 | 12 | 2.4023 | 0.4991 |
| 2.0765 | 5.0 | 15 | 2.3965 | 0.5020 |
Framework versions
- Transformers 4.20.1
- Pytorch 1.12.0+cu102
- Datasets 2.3.2
- Tokenizers 0.12.1
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