Instructions to use BernardOng/Banking-FT-Bong-v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use BernardOng/Banking-FT-Bong-v1 with Transformers:

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

pipe = pipeline("text-generation", model="BernardOng/Banking-FT-Bong-v1")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("BernardOng/Banking-FT-Bong-v1")
model = AutoModelForCausalLM.from_pretrained("BernardOng/Banking-FT-Bong-v1")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use BernardOng/Banking-FT-Bong-v1 with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/BernardOng/Banking-FT-Bong-v1

SGLang

How to use BernardOng/Banking-FT-Bong-v1 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 "BernardOng/Banking-FT-Bong-v1" \
    --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": "BernardOng/Banking-FT-Bong-v1",
		"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 "BernardOng/Banking-FT-Bong-v1" \
        --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": "BernardOng/Banking-FT-Bong-v1",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use BernardOng/Banking-FT-Bong-v1 with Docker Model Runner:
```
docker model run hf.co/BernardOng/Banking-FT-Bong-v1
```

BernardOng commited on Jul 10, 2023

Commit

a5ab40d

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@@ -14,9 +14,8 @@ thumbnail: https://h2o.ai/etc.clientlibs/h2o/clientlibs/clientlib-site/resources
 ## Summary
 This model was trained using [H2O LLM Studio](https://github.com/h2oai/h2o-llmstudio).
-Caution: This is only an experimental model used mainly for research and testing purposes. It is not meant for production use.
 - Base model: [h2oai/h2ogpt-oig-oasst1-512-6.9b](https://huggingface.co/h2oai/h2ogpt-oig-oasst1-512-6.9b)
 ## Usage

 ## Summary
 This model was trained using [H2O LLM Studio](https://github.com/h2oai/h2o-llmstudio).
 - Base model: [h2oai/h2ogpt-oig-oasst1-512-6.9b](https://huggingface.co/h2oai/h2ogpt-oig-oasst1-512-6.9b)
+- Caution: This is only an experimental model used mainly for research and testing purposes. It is not meant for production use.
 ## Usage