Instructions to use GItaf/bert2bert-with-cross-attn-decoder with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use GItaf/bert2bert-with-cross-attn-decoder with Transformers:

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

pipe = pipeline("text-generation", model="GItaf/bert2bert-with-cross-attn-decoder")

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("GItaf/bert2bert-with-cross-attn-decoder")
model = AutoModelForCausalLM.from_pretrained("GItaf/bert2bert-with-cross-attn-decoder", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use GItaf/bert2bert-with-cross-attn-decoder with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "GItaf/bert2bert-with-cross-attn-decoder"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "GItaf/bert2bert-with-cross-attn-decoder",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/GItaf/bert2bert-with-cross-attn-decoder

SGLang

How to use GItaf/bert2bert-with-cross-attn-decoder 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 "GItaf/bert2bert-with-cross-attn-decoder" \
    --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": "GItaf/bert2bert-with-cross-attn-decoder",
		"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 "GItaf/bert2bert-with-cross-attn-decoder" \
        --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": "GItaf/bert2bert-with-cross-attn-decoder",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use GItaf/bert2bert-with-cross-attn-decoder with Docker Model Runner:
```
docker model run hf.co/GItaf/bert2bert-with-cross-attn-decoder
```

bert2bert-with-cross-attn-decoder / tokenizer_config.json

GItaf

Training in progress, epoch 1

cacfbfc about 4 years ago

Raw History Blame

348 Bytes

{
  "cls_token": "[CLS]",
  "do_lower_case": true,
  "mask_token": "[MASK]",
  "model_max_length": 512,
  "name_or_path": "bert-base-uncased",
  "pad_token": "[PAD]",
  "sep_token": "[SEP]",
  "special_tokens_map_file": null,
  "strip_accents": null,
  "tokenize_chinese_chars": true,
  "tokenizer_class": "BertTokenizer",
  "unk_token": "[UNK]"
}

	{
	"cls_token": "[CLS]",
	"do_lower_case": true,
	"mask_token": "[MASK]",
	"model_max_length": 512,
	"name_or_path": "bert-base-uncased",
	"pad_token": "[PAD]",
	"sep_token": "[SEP]",
	"special_tokens_map_file": null,
	"strip_accents": null,
	"tokenize_chinese_chars": true,
	"tokenizer_class": "BertTokenizer",
	"unk_token": "[UNK]"
	}