Instructions to use hf-internal-testing/tiny-random-BartForCausalLM with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use hf-internal-testing/tiny-random-BartForCausalLM with Transformers:

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

pipe = pipeline("text-generation", model="hf-internal-testing/tiny-random-BartForCausalLM")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-BartForCausalLM")
model = AutoModelForCausalLM.from_pretrained("hf-internal-testing/tiny-random-BartForCausalLM")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use hf-internal-testing/tiny-random-BartForCausalLM with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/hf-internal-testing/tiny-random-BartForCausalLM

SGLang

How to use hf-internal-testing/tiny-random-BartForCausalLM 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 "hf-internal-testing/tiny-random-BartForCausalLM" \
    --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": "hf-internal-testing/tiny-random-BartForCausalLM",
		"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 "hf-internal-testing/tiny-random-BartForCausalLM" \
        --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": "hf-internal-testing/tiny-random-BartForCausalLM",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use hf-internal-testing/tiny-random-BartForCausalLM with Docker Model Runner:
```
docker model run hf.co/hf-internal-testing/tiny-random-BartForCausalLM
```

ydshieh HF Staff commited on Apr 6, 2023

Commit

c25526a

1 Parent(s): 6ca393c

Upload tiny models for BartForCausalLM

Browse files

Files changed (4) hide show

config.json +1 -1
generation_config.json +8 -0
pytorch_model.bin +1 -1
tokenizer_config.json +1 -1

config.json CHANGED Viewed

@@ -39,7 +39,7 @@
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   "scale_embedding": false,
   "torch_dtype": "float32",
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   "use_cache": true,
   "vocab_size": 1024
 }

   "pad_token_id": 1,
   "scale_embedding": false,
   "torch_dtype": "float32",
+  "transformers_version": "4.28.0.dev0",
   "use_cache": true,
   "vocab_size": 1024
 }

generation_config.json ADDED Viewed

	@@ -0,0 +1,8 @@

+{
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+  "bos_token_id": 0,
+  "decoder_start_token_id": 2,
+  "eos_token_id": 2,
+  "pad_token_id": 1,
+  "transformers_version": "4.28.0.dev0"
+}

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tokenizer_config.json CHANGED Viewed

@@ -8,6 +8,7 @@
     "rstrip": false,
     "single_word": false
   },
   "cls_token": {
     "__type": "AddedToken",
     "content": "<s>",
@@ -34,7 +35,6 @@
     "single_word": false
   },
   "model_max_length": 100,
-  "name_or_path": "temp/dummy/bart/processors",
   "pad_token": {
     "__type": "AddedToken",
     "content": "<pad>",

     "rstrip": false,
     "single_word": false
   },
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     "content": "<s>",
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