Instructions to use OpenBuddy/openbuddy-llama2-13b-v8.1-fp16 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use OpenBuddy/openbuddy-llama2-13b-v8.1-fp16 with Transformers:

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

pipe = pipeline("text-generation", model="OpenBuddy/openbuddy-llama2-13b-v8.1-fp16")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("OpenBuddy/openbuddy-llama2-13b-v8.1-fp16")
model = AutoModelForCausalLM.from_pretrained("OpenBuddy/openbuddy-llama2-13b-v8.1-fp16", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use OpenBuddy/openbuddy-llama2-13b-v8.1-fp16 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "OpenBuddy/openbuddy-llama2-13b-v8.1-fp16"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "OpenBuddy/openbuddy-llama2-13b-v8.1-fp16",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/OpenBuddy/openbuddy-llama2-13b-v8.1-fp16

SGLang

How to use OpenBuddy/openbuddy-llama2-13b-v8.1-fp16 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 "OpenBuddy/openbuddy-llama2-13b-v8.1-fp16" \
    --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": "OpenBuddy/openbuddy-llama2-13b-v8.1-fp16",
		"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 "OpenBuddy/openbuddy-llama2-13b-v8.1-fp16" \
        --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": "OpenBuddy/openbuddy-llama2-13b-v8.1-fp16",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use OpenBuddy/openbuddy-llama2-13b-v8.1-fp16 with Docker Model Runner:
```
docker model run hf.co/OpenBuddy/openbuddy-llama2-13b-v8.1-fp16
```

最好能给出Instruction模板和示例，另外请问底层是llama2-base还是llama2-chat？

by bash99 - opened Aug 2, 2023

Discussion

bash99

Aug 2, 2023

方便没加群的上手测试。

我试过
User:
Assistant:
这样可以，比较奇特的是直接调用
You are .... assistant. Think it over and answer user question correctly.
User:
Assistant:
可以，但是用ooba ui + extllama加载后，api调用时，却需要
User: You are .... assistant. Think it over and answer user question correctly. \n
Assistant:
这样才行。

同时做领域finetune测试时，发现上述对话格式做模板 loss下降很慢，如果换成llama2的instruction模板，就是带
的那个，loss下降快很多。

ff670

OpenBuddy org Aug 2, 2023

你好，Instruction模板和示例请参考我们的Github上的example：https://github.com/OpenBuddy/OpenBuddy/blob/main/examples/hello.py

sft的时候，System Prompt只需You are a helpful assistant.

ff670

OpenBuddy org Aug 2, 2023

另外，底层是llama2-base

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