Instructions to use stevems1/distilroberta-base-SmithsModel with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use stevems1/distilroberta-base-SmithsModel with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="stevems1/distilroberta-base-SmithsModel")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("stevems1/distilroberta-base-SmithsModel") model = AutoModelForCausalLM.from_pretrained("stevems1/distilroberta-base-SmithsModel", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use stevems1/distilroberta-base-SmithsModel with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "stevems1/distilroberta-base-SmithsModel" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "stevems1/distilroberta-base-SmithsModel", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/stevems1/distilroberta-base-SmithsModel
- SGLang
How to use stevems1/distilroberta-base-SmithsModel 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 "stevems1/distilroberta-base-SmithsModel" \ --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": "stevems1/distilroberta-base-SmithsModel", "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 "stevems1/distilroberta-base-SmithsModel" \ --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": "stevems1/distilroberta-base-SmithsModel", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use stevems1/distilroberta-base-SmithsModel with Docker Model Runner:
docker model run hf.co/stevems1/distilroberta-base-SmithsModel
|
Download README.md from stevems1/distilroberta-base-SmithsModel: direct link, hf CLI and curl.
- Browser
- Download file 1.38 kB
-
https://huggingface.co/stevems1/distilroberta-base-SmithsModel/resolve/main/README.md
- Command line
-
hf download hf://stevems1/distilroberta-base-SmithsModel/README.md
-
curl -L -o README.md https://huggingface.co/stevems1/distilroberta-base-SmithsModel/resolve/main/README.md
1.38 kB
| license: apache-2.0 | |
| tags: | |
| - generated_from_trainer | |
| model-index: | |
| - name: distilroberta-base-SmithsModel | |
| results: [] | |
| <!-- This model card has been generated automatically according to the information the Trainer had access to. You | |
| should probably proofread and complete it, then remove this comment. --> | |
| # distilroberta-base-SmithsModel | |
| This model is a fine-tuned version of [distilroberta-base](https://huggingface.co/distilroberta-base) on the None dataset. | |
| It achieves the following results on the evaluation set: | |
| - Loss: 2.3070 | |
| ## 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: 2e-05 | |
| - train_batch_size: 8 | |
| - eval_batch_size: 8 | |
| - seed: 42 | |
| - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 | |
| - lr_scheduler_type: linear | |
| - num_epochs: 3.0 | |
| ### Training results | |
| | Training Loss | Epoch | Step | Validation Loss | | |
| |:-------------:|:-----:|:----:|:---------------:| | |
| | 4.6589 | 1.0 | 830 | 2.8652 | | |
| | 2.8362 | 2.0 | 1660 | 2.4309 | | |
| | 2.6291 | 3.0 | 2490 | 2.2826 | | |
| ### Framework versions | |
| - Transformers 4.18.0 | |
| - Pytorch 1.10.0+cu111 | |
| - Datasets 2.1.0 | |
| - Tokenizers 0.12.1 | |