Instructions to use vngrs-ai/VBART-Small-Base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use vngrs-ai/VBART-Small-Base with Transformers:

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

pipe = pipeline("text-generation", model="vngrs-ai/VBART-Small-Base")

# Load model directly
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM

tokenizer = AutoTokenizer.from_pretrained("vngrs-ai/VBART-Small-Base")
model = AutoModelForSeq2SeqLM.from_pretrained("vngrs-ai/VBART-Small-Base")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use vngrs-ai/VBART-Small-Base with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/vngrs-ai/VBART-Small-Base

SGLang

How to use vngrs-ai/VBART-Small-Base 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 "vngrs-ai/VBART-Small-Base" \
    --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": "vngrs-ai/VBART-Small-Base",
		"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 "vngrs-ai/VBART-Small-Base" \
        --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": "vngrs-ai/VBART-Small-Base",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use vngrs-ai/VBART-Small-Base with Docker Model Runner:
```
docker model run hf.co/vngrs-ai/VBART-Small-Base
```

meliksahturker commited on Mar 22, 2024

Commit

078cb3d

verified ·

1 Parent(s): 0993451

Upload TFMBartForConditionalGeneration

Browse files

Files changed (3) hide show

config.json +1 -3
generation_config.json +1 -2
tf_model.h5 +2 -2

config.json CHANGED Viewed

@@ -1,5 +1,4 @@
 {
-  "_name_or_path": "tfhf_model_checkpointsmall_epoch_0010_opt.hdf5",
   "activation_dropout": 0.0,
   "activation_function": "gelu",
   "architectures": [
@@ -28,8 +27,7 @@
   "num_hidden_layers": 4,
   "pad_token_id": 0,
   "scale_embedding": false,
-  "torch_dtype": "float32",
   "transformers_version": "4.39.0",
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   "pad_token_id": 0,
   "scale_embedding": false,
   "transformers_version": "4.39.0",
   "use_cache": true,
+  "vocab_size": 32001
 }

generation_config.json CHANGED Viewed

@@ -5,6 +5,5 @@
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   "pad_token_id": 0,
-  "transformers_version": "4.39.0",
-  "max_new_tokens": 1024
 }

   "eos_token_id": 3,
   "forced_eos_token_id": 3,
   "pad_token_id": 0,
+  "transformers_version": "4.39.0"
 }

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