Instructions to use I042/bert-base-arabert-finetuned-wikitext2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use I042/bert-base-arabert-finetuned-wikitext2 with Transformers:

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

pipe = pipeline("text-generation", model="I042/bert-base-arabert-finetuned-wikitext2")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("I042/bert-base-arabert-finetuned-wikitext2")
model = AutoModelForCausalLM.from_pretrained("I042/bert-base-arabert-finetuned-wikitext2", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use I042/bert-base-arabert-finetuned-wikitext2 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "I042/bert-base-arabert-finetuned-wikitext2"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "I042/bert-base-arabert-finetuned-wikitext2",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/I042/bert-base-arabert-finetuned-wikitext2

SGLang

How to use I042/bert-base-arabert-finetuned-wikitext2 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 "I042/bert-base-arabert-finetuned-wikitext2" \
    --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": "I042/bert-base-arabert-finetuned-wikitext2",
		"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 "I042/bert-base-arabert-finetuned-wikitext2" \
        --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": "I042/bert-base-arabert-finetuned-wikitext2",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use I042/bert-base-arabert-finetuned-wikitext2 with Docker Model Runner:
```
docker model run hf.co/I042/bert-base-arabert-finetuned-wikitext2
```

I042 commited on Sep 12, 2024

Commit

4d24262

verified ·

1 Parent(s): a8e23d0

Training in progress, step 500

Browse files

Files changed (4) hide show

config.json +2 -1
model.safetensors +3 -0
runs/Sep12_07-16-57_014d06935856/events.out.tfevents.1726125424.014d06935856.413.2 +3 -0
training_args.bin +3 -0

config.json CHANGED Viewed

@@ -1,7 +1,7 @@
 {
   "_name_or_path": "aubmindlab/bert-base-arabert",
   "architectures": [
-    "BertForMaskedLM"
   ],
   "attention_probs_dropout_prob": 0.1,
   "classifier_dropout": null,
@@ -23,6 +23,7 @@
   "pooler_size_per_head": 128,
   "pooler_type": "first_token_transform",
   "position_embedding_type": "absolute",
   "transformers_version": "4.44.2",
   "type_vocab_size": 2,
   "use_cache": true,

 {
   "_name_or_path": "aubmindlab/bert-base-arabert",
   "architectures": [
+    "BertLMHeadModel"
   ],
   "attention_probs_dropout_prob": 0.1,
   "classifier_dropout": null,
   "pooler_size_per_head": 128,
   "pooler_type": "first_token_transform",
   "position_embedding_type": "absolute",
+  "torch_dtype": "float32",
   "transformers_version": "4.44.2",
   "type_vocab_size": 2,
   "use_cache": true,

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