Instructions to use zjunlp/OceanGPT-basic-7B-v0.1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use zjunlp/OceanGPT-basic-7B-v0.1 with Transformers:

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

pipe = pipeline("text-generation", model="zjunlp/OceanGPT-basic-7B-v0.1")

# Load model directly
from transformers import AutoTokenizer, AutoModelForMultimodalLM

tokenizer = AutoTokenizer.from_pretrained("zjunlp/OceanGPT-basic-7B-v0.1")
model = AutoModelForMultimodalLM.from_pretrained("zjunlp/OceanGPT-basic-7B-v0.1")

Inference
Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use zjunlp/OceanGPT-basic-7B-v0.1 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "zjunlp/OceanGPT-basic-7B-v0.1"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "zjunlp/OceanGPT-basic-7B-v0.1",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/zjunlp/OceanGPT-basic-7B-v0.1

SGLang

How to use zjunlp/OceanGPT-basic-7B-v0.1 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 "zjunlp/OceanGPT-basic-7B-v0.1" \
    --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": "zjunlp/OceanGPT-basic-7B-v0.1",
		"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 "zjunlp/OceanGPT-basic-7B-v0.1" \
        --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": "zjunlp/OceanGPT-basic-7B-v0.1",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use zjunlp/OceanGPT-basic-7B-v0.1 with Docker Model Runner:
```
docker model run hf.co/zjunlp/OceanGPT-basic-7B-v0.1
```

Ningyu commited on Jul 5, 2024

Commit

977e1b9

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@@ -93,6 +93,14 @@ response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
 OceanGPT is trained based on the open-sourced large language models including [Qwen](https://huggingface.co/Qwen), [MiniCPM](https://huggingface.co/collections/openbmb/minicpm-2b-65d48bf958302b9fd25b698f), [LLaMA](https://huggingface.co/meta-llama). Thanks for their great contributions!
 ### 🚩Citation

 OceanGPT is trained based on the open-sourced large language models including [Qwen](https://huggingface.co/Qwen), [MiniCPM](https://huggingface.co/collections/openbmb/minicpm-2b-65d48bf958302b9fd25b698f), [LLaMA](https://huggingface.co/meta-llama). Thanks for their great contributions!
+## Limitations
+- The model may have hallucination issues.
+- We did not optimize the identity and the model may generate identity information similar to that of Qwen/MiniCPM/LLaMA/GPT series models.
+- The model's output is influenced by prompt tokens, which may result in inconsistent results across multiple attempts.
 ### 🚩Citation