Text Generation
Transformers
Safetensors
English
modernbert
fill-mask
sentiment-control
continuous-control
controllable-text-generation
encoder-generation
non-autoregressive
masked-language-model
text-style-transfer
data-augmentation
emnlp2026
Instructions to use shawhed/SenseShift-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use shawhed/SenseShift-base with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="shawhed/SenseShift-base")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("shawhed/SenseShift-base") model = AutoModelForMaskedLM.from_pretrained("shawhed/SenseShift-base", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use shawhed/SenseShift-base with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "shawhed/SenseShift-base" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "shawhed/SenseShift-base", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/shawhed/SenseShift-base
- SGLang
How to use shawhed/SenseShift-base 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 "shawhed/SenseShift-base" \ --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": "shawhed/SenseShift-base", "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 "shawhed/SenseShift-base" \ --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": "shawhed/SenseShift-base", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use shawhed/SenseShift-base with Docker Model Runner:
docker model run hf.co/shawhed/SenseShift-base
| { | |
| "senseshift_version": "0.1.0", | |
| "base_model": "answerdotai/ModernBERT-base", | |
| "control_type": "text_token", | |
| "sentiment_scorer": "vader", | |
| "sentiment_grid": [ | |
| -1.0, | |
| -0.9, | |
| -0.8, | |
| -0.7, | |
| -0.6, | |
| -0.5, | |
| -0.4, | |
| -0.3, | |
| -0.2, | |
| -0.1, | |
| 0.0, | |
| 0.1, | |
| 0.2, | |
| 0.3, | |
| 0.4, | |
| 0.5, | |
| 0.6, | |
| 0.7, | |
| 0.8, | |
| 0.9, | |
| 1.0 | |
| ], | |
| "generation": { | |
| "top_k": 40, | |
| "beam_size": 2, | |
| "max_iters": 30, | |
| "alpha": 0.7, | |
| "gamma": 0.05, | |
| "temperature": 0.8, | |
| "min_words": 3, | |
| "add_num_masks": 12 | |
| } | |
| } |