Instructions to use tonmoytalukder/Bangla-Key2Text with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use tonmoytalukder/Bangla-Key2Text with Transformers:

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

pipe = pipeline("text-generation", model="tonmoytalukder/Bangla-Key2Text")

# Load model directly
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM

tokenizer = AutoTokenizer.from_pretrained("tonmoytalukder/Bangla-Key2Text")
model = AutoModelForSeq2SeqLM.from_pretrained("tonmoytalukder/Bangla-Key2Text", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use tonmoytalukder/Bangla-Key2Text with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "tonmoytalukder/Bangla-Key2Text"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "tonmoytalukder/Bangla-Key2Text",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/tonmoytalukder/Bangla-Key2Text

SGLang

How to use tonmoytalukder/Bangla-Key2Text 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 "tonmoytalukder/Bangla-Key2Text" \
    --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": "tonmoytalukder/Bangla-Key2Text",
		"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 "tonmoytalukder/Bangla-Key2Text" \
        --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": "tonmoytalukder/Bangla-Key2Text",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use tonmoytalukder/Bangla-Key2Text with Docker Model Runner:
```
docker model run hf.co/tonmoytalukder/Bangla-Key2Text
```

Bangla-Key2Text

File size: 924 Bytes

d1ee1d4

{
  "_name_or_path": "/content/drive/MyDrive/BengaliKey2Text/ModelV10V3",
  "architectures": [
    "MT5ForConditionalGeneration"
  ],
  "d_ff": 2048,
  "d_kv": 64,
  "d_model": 768,
  "decoder_start_token_id": 0,
  "dense_act_fn": "gelu_new",
  "dropout_rate": 0.1,
  "eos_token_id": 1,
  "feed_forward_proj": "gated-gelu",
  "initializer_factor": 1.0,
  "is_encoder_decoder": true,
  "is_gated_act": true,
  "layer_norm_epsilon": 1e-06,
  "length_penalty": 0.6,
  "max_length": 84,
  "model_type": "mt5",
  "no_repeat_ngram_size": 2,
  "num_beams": 4,
  "num_decoder_layers": 12,
  "num_heads": 12,
  "num_layers": 12,
  "output_past": true,
  "pad_token_id": 0,
  "relative_attention_max_distance": 128,
  "relative_attention_num_buckets": 32,
  "tie_word_embeddings": false,
  "tokenizer_class": "T5Tokenizer",
  "torch_dtype": "float32",
  "transformers_version": "4.30.2",
  "use_cache": true,
  "vocab_size": 250112
}