Instructions to use monsoon-nlp/ar-seq2seq-gender-decoder with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use monsoon-nlp/ar-seq2seq-gender-decoder with Transformers:

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

pipe = pipeline("text-generation", model="monsoon-nlp/ar-seq2seq-gender-decoder")

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

tokenizer = AutoTokenizer.from_pretrained("monsoon-nlp/ar-seq2seq-gender-decoder")
model = AutoModelForCausalLM.from_pretrained("monsoon-nlp/ar-seq2seq-gender-decoder", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use monsoon-nlp/ar-seq2seq-gender-decoder with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "monsoon-nlp/ar-seq2seq-gender-decoder"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "monsoon-nlp/ar-seq2seq-gender-decoder",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/monsoon-nlp/ar-seq2seq-gender-decoder

SGLang

How to use monsoon-nlp/ar-seq2seq-gender-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 "monsoon-nlp/ar-seq2seq-gender-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": "monsoon-nlp/ar-seq2seq-gender-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 "monsoon-nlp/ar-seq2seq-gender-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": "monsoon-nlp/ar-seq2seq-gender-decoder",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use monsoon-nlp/ar-seq2seq-gender-decoder with Docker Model Runner:
```
docker model run hf.co/monsoon-nlp/ar-seq2seq-gender-decoder
```

ar-seq2seq-gender-decoder / config.json

monsoon-nlp

decoder

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	{
	"add_cross_attention": true,
	"architectures": [
	"BertLMHeadModel"
	],
	"attention_probs_dropout_prob": 0.1,
	"directionality": "bidi",
	"gradient_checkpointing": false,
	"hidden_act": "gelu",
	"hidden_dropout_prob": 0.1,
	"hidden_size": 768,
	"initializer_range": 0.02,
	"intermediate_size": 3072,
	"is_decoder": true,
	"layer_norm_eps": 1e-12,
	"max_position_embeddings": 512,
	"model_type": "bert",
	"num_attention_heads": 12,
	"num_hidden_layers": 12,
	"pad_token_id": 0,
	"pooler_fc_size": 768,
	"pooler_num_attention_heads": 12,
	"pooler_num_fc_layers": 3,
	"pooler_size_per_head": 128,
	"pooler_type": "first_token_transform",
	"position_embedding_type": "absolute",
	"transformers_version": "4.2.1",
	"type_vocab_size": 2,
	"use_cache": true,
	"vocab_size": 100000
	}