Instructions to use ByteWave/prompt-generator with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use ByteWave/prompt-generator with Transformers:

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

pipe = pipeline("text-generation", model="ByteWave/prompt-generator")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("ByteWave/prompt-generator")
model = AutoModelForCausalLM.from_pretrained("ByteWave/prompt-generator")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use ByteWave/prompt-generator with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/ByteWave/prompt-generator

SGLang

How to use ByteWave/prompt-generator 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 "ByteWave/prompt-generator" \
    --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": "ByteWave/prompt-generator",
		"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 "ByteWave/prompt-generator" \
        --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": "ByteWave/prompt-generator",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use ByteWave/prompt-generator with Docker Model Runner:
```
docker model run hf.co/ByteWave/prompt-generator
```

Q-bert commited on Nov 10, 2023

Commit

9dacb68

1 Parent(s): 5328583

Update README.md

Browse files

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README.md +4 -4

README.md CHANGED Viewed

@@ -12,12 +12,12 @@ tags:
 - Agents
 - LLMs
 ---
-<a href="https://www.buymeacoffee.com/PulsarAI" target="_blank"><img src="https://cdn.buymeacoffee.com/buttons/v2/default-yellow.png" alt="Buy Me A Coffee" style="height: 60px !important;width: 217px !important;" ></a>
-# Prompt Generator by PulsarAI:
-Welcome to the official repository of Prompt Generator, a powerful tool for effortlessly generating prompts for Large Language Models (LLMs) by PulsarAI.
 ## About Prompt Generator:
@@ -35,7 +35,7 @@ Prompt Generator is designed to streamline the process of generating text prompt
 ```python
 from transformers import pipeline
-generator = pipeline("text-generation",model="PulsarAI/prompt-generator")
 act = f"""
       Action: Doctor
       Prompt:

 - Agents
 - LLMs
 ---
+# Prompt Generator by ByteWave:
+Welcome to the official repository of Prompt Generator, a powerful tool for effortlessly generating prompts for Large Language Models (LLMs) by ByteWave.
 ## About Prompt Generator:
 ```python
 from transformers import pipeline
+generator = pipeline("text-generation",model="ByteWave/prompt-generator")
 act = f"""
       Action: Doctor
       Prompt: