Instructions to use shawhed/SenseShift-large with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use shawhed/SenseShift-large with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="shawhed/SenseShift-large")

# Load model directly
from transformers import AutoTokenizer, AutoModelForMaskedLM

tokenizer = AutoTokenizer.from_pretrained("shawhed/SenseShift-large")
model = AutoModelForMaskedLM.from_pretrained("shawhed/SenseShift-large", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use shawhed/SenseShift-large with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "shawhed/SenseShift-large"
# 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-large",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/shawhed/SenseShift-large

SGLang

How to use shawhed/SenseShift-large 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-large" \
    --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-large",
		"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-large" \
        --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-large",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use shawhed/SenseShift-large with Docker Model Runner:
```
docker model run hf.co/shawhed/SenseShift-large
```

SenseShift-large

File size: 861 Bytes

9d135a2

{
  "backend": "tokenizers",
  "clean_up_tokenization_spaces": true,
  "cls_token": "[CLS]",
  "extra_special_tokens": [
    "[-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]"
  ],
  "is_local": true,
  "mask_token": "[MASK]",
  "max_length": 512,
  "model_input_names": [
    "input_ids",
    "attention_mask"
  ],
  "model_max_length": 8192,
  "pad_to_multiple_of": null,
  "pad_token": "[PAD]",
  "pad_token_type_id": 0,
  "padding_side": "right",
  "sep_token": "[SEP]",
  "stride": 0,
  "tokenizer_class": "PreTrainedTokenizerFast",
  "truncation_side": "right",
  "truncation_strategy": "longest_first",
  "unk_token": "[UNK]"
}