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
File size: 556 Bytes
df10fc7 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 | {
"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
}
} |