Instructions to use Congi-libya/BayanSimplify-v0.2-Fast with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Congi-libya/BayanSimplify-v0.2-Fast with Transformers:

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

pipe = pipeline("text-generation", model="Congi-libya/BayanSimplify-v0.2-Fast")

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM

tokenizer = AutoTokenizer.from_pretrained("Congi-libya/BayanSimplify-v0.2-Fast")
model = AutoModelForSeq2SeqLM.from_pretrained("Congi-libya/BayanSimplify-v0.2-Fast", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Congi-libya/BayanSimplify-v0.2-Fast with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Congi-libya/BayanSimplify-v0.2-Fast"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Congi-libya/BayanSimplify-v0.2-Fast",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/Congi-libya/BayanSimplify-v0.2-Fast

SGLang

How to use Congi-libya/BayanSimplify-v0.2-Fast 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 "Congi-libya/BayanSimplify-v0.2-Fast" \
    --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": "Congi-libya/BayanSimplify-v0.2-Fast",
		"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 "Congi-libya/BayanSimplify-v0.2-Fast" \
        --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": "Congi-libya/BayanSimplify-v0.2-Fast",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use Congi-libya/BayanSimplify-v0.2-Fast with Docker Model Runner:
```
docker model run hf.co/Congi-libya/BayanSimplify-v0.2-Fast
```

BayanSimplify-v0.2-Fast / config.json

marwanelamami

BayanSimplify-v0.2-Fast: weights, tokenizer and model card

dbf3e7a verified 9 days ago

Raw History Blame Contribute Delete

1.44 kB

	{
	"activation_dropout": 0.1,
	"activation_function": "gelu",
	"add_bias_logits": false,
	"add_final_layer_norm": true,
	"architectures": [
	"MBartForConditionalGeneration"
	],
	"attention_dropout": 0.1,
	"bos_token_id": 0,
	"classif_dropout": 0.1,
	"classifier_dropout": 0.0,
	"d_model": 768,
	"decoder_attention_heads": 12,
	"decoder_ffn_dim": 3072,
	"decoder_layerdrop": 0.0,
	"decoder_layers": 6,
	"decoder_start_token_id": 2,
	"do_blenderbot_90_layernorm": false,
	"dropout": 0.1,
	"early_stopping": null,
	"encoder_attention_heads": 12,
	"encoder_ffn_dim": 3072,
	"encoder_layerdrop": 0.0,
	"encoder_layers": 6,
	"eos_token_id": 2,
	"extra_pos_embeddings": 2,
	"force_bos_token_to_be_generated": false,
	"forced_eos_token_id": 2,
	"gradient_checkpointing": false,
	"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_position_embeddings": 1024,
	"model_type": "mbart",
	"no_repeat_ngram_size": null,
	"normalize_before": true,
	"normalize_embedding": true,
	"num_beams": null,
	"num_hidden_layers": 6,
	"pad_token_id": 1,
	"scale_embedding": false,
	"static_position_embeddings": false,
	"tokenizer_class": "BarthezTokenizer",
	"torch_dtype": "float32",
	"transformers_version": "4.46.3",
	"use_cache": true,
	"vocab_size": 50002
	}