Instructions to use DEplain/trimmed_longmbart_docs_apa with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use DEplain/trimmed_longmbart_docs_apa with Transformers:

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

pipe = pipeline("text-generation", model="DEplain/trimmed_longmbart_docs_apa")

# Load model directly
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM

tokenizer = AutoTokenizer.from_pretrained("DEplain/trimmed_longmbart_docs_apa")
model = AutoModelForSeq2SeqLM.from_pretrained("DEplain/trimmed_longmbart_docs_apa", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use DEplain/trimmed_longmbart_docs_apa with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/DEplain/trimmed_longmbart_docs_apa

SGLang

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

Docker Model Runner
How to use DEplain/trimmed_longmbart_docs_apa with Docker Model Runner:
```
docker model run hf.co/DEplain/trimmed_longmbart_docs_apa
```

trimmed_longmbart_docs_apa / config.json

omarmomen

init commit

184237e over 3 years ago

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	{
	"_name_or_path": "pretrained_models/trimmed_longmbart",
	"_num_labels": 3,
	"activation_dropout": 0.0,
	"activation_function": "gelu",
	"add_bias_logits": false,
	"add_final_layer_norm": true,
	"architectures": [
	"MLongformerEncoderDecoderForConditionalGeneration"
	],
	"attention_dilation": [
	1,
	1,
	1,
	1,
	1,
	1,
	1,
	1,
	1,
	1,
	1,
	1
	],
	"attention_dropout": 0.1,
	"attention_mode": "sliding_chunks",
	"attention_probs_dropout_prob": 0.0,
	"attention_window": [
	512,
	512,
	512,
	512,
	512,
	512,
	512,
	512,
	512,
	512,
	512,
	512
	],
	"autoregressive": false,
	"bos_token_id": 0,
	"classif_dropout": 0.0,
	"classifier_dropout": 0.0,
	"d_model": 1024,
	"decoder_attention_heads": 16,
	"decoder_ffn_dim": 4096,
	"decoder_layerdrop": 0.0,
	"decoder_layers": 12,
	"dropout": 0.3,
	"encoder_attention_heads": 16,
	"encoder_ffn_dim": 4096,
	"encoder_layerdrop": 0.0,
	"encoder_layers": 12,
	"eos_token_id": 2,
	"forced_eos_token_id": 2,
	"global_attention_indices": [
	-1
	],
	"id2label": {
	"0": "LABEL_0",
	"1": "LABEL_1",
	"2": "LABEL_2"
	},
	"init_std": 0.02,
	"is_encoder_decoder": true,
	"label2id": {
	"LABEL_0": 0,
	"LABEL_1": 1,
	"LABEL_2": 2
	},
	"max_decoder_position_embeddings": 1024,
	"max_encoder_position_embeddings": 2048,
	"max_length": 1024,
	"max_position_embeddings": 1024,
	"model_type": "mbart",
	"normalize_before": true,
	"normalize_embedding": true,
	"num_beams": 5,
	"num_hidden_layers": 12,
	"output_past": true,
	"pad_token_id": 1,
	"scale_embedding": true,
	"static_position_embeddings": false,
	"task_specific_params": {
	"translation_en_to_ro": {
	"decoder_start_token_id": 250020
	}
	},
	"torch_dtype": "float32",
	"transformers_version": "4.26.1",
	"use_cache": true,
	"vocab_size": 34851
	}