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 / tokenizer_config.json

shawhed

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	{
	"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]",
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	"[-0.2]",
	"[-0.1]",
	"[0.0]",
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	],
	"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]"
	}