Instructions to use mrm8488/bloom-560m-finetuned-sd-prompts with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mrm8488/bloom-560m-finetuned-sd-prompts with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="mrm8488/bloom-560m-finetuned-sd-prompts")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("mrm8488/bloom-560m-finetuned-sd-prompts") model = AutoModelForCausalLM.from_pretrained("mrm8488/bloom-560m-finetuned-sd-prompts", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use mrm8488/bloom-560m-finetuned-sd-prompts with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "mrm8488/bloom-560m-finetuned-sd-prompts" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "mrm8488/bloom-560m-finetuned-sd-prompts", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/mrm8488/bloom-560m-finetuned-sd-prompts
- SGLang
How to use mrm8488/bloom-560m-finetuned-sd-prompts 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 "mrm8488/bloom-560m-finetuned-sd-prompts" \ --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": "mrm8488/bloom-560m-finetuned-sd-prompts", "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 "mrm8488/bloom-560m-finetuned-sd-prompts" \ --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": "mrm8488/bloom-560m-finetuned-sd-prompts", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use mrm8488/bloom-560m-finetuned-sd-prompts with Docker Model Runner:
docker model run hf.co/mrm8488/bloom-560m-finetuned-sd-prompts
Completion stuck in a loop
Hi, @ParkerBurchett . In the inference widget it doesn't seem to work too fine. I didn't tested it there because it is slowly. Can you test in a Colab? I will add the recommend generation params
Sure I got it up in Colab. It's still in a loop
https://colab.research.google.com/drive/1YcH40MhJfYiFd39Q361SC6cauq2YFx9v?usp=sharing
prompt = f"McDonald's hamburger promotion on a red billboard, white lettering, "
input_ids = tokenizer(prompt, return_tensors="pt").to('cuda')
sample = model.generate(**input_ids, max_new_tokens=50)
tokenizer.decode(sample[0])
McDonald's hamburger promotion on a red billboard, white lettering, digital billboard, digital billboard, digital billboard, digital billboard, digital billboard, digital billboard, digital billboard, digital billboard, digital billboard, digital billboard, digital billboard, digital billboard, digital bill
@mrm8488 which are the generation params you are using?
I had the same issue using the default parameters but I put a high repetition_penalty to fix it.
In Colab it still gets stuck in a loop
import torch
from transformers import BloomTokenizerFast, BloomForCausalLM
device = 'cuda' if torch.cuda.is_available() else 'cpu'
ckpt = 'mrm8488/bloom-560m-finetuned-sd-prompts'
tokenizer = BloomTokenizerFast.from_pretrained(ckpt)
model = BloomForCausalLM.from_pretrained(ckpt).to(device)
def generate_prompt(text):
torch.cuda.empty_cache()
inputs = tokenizer(text, return_tensors='pt')
input_ids = inputs.input_ids.to(device)
attention_mask = inputs.attention_mask.to(device)
output = model.generate(input_ids, attention_mask=attention_mask, max_length=512, eos_token_id=tokenizer.eos_token_id)
return tokenizer.decode(output[0], skip_special_tokens=False)
text = "<s>Prompt: pikachu dinning in the eiffel tower"
text2 = f"<s>Prompt: McDonald's hamburger promotion on a red billboard, white lettering, "
generate_prompt(text2)
Returns
<s>Prompt: McDonald's hamburger promotion on a red billboard, white lettering, digital billboard, digital billboard, digital billboard, digital billboard, digital billboard, digital billboard, digital billboard, digital billboard, digital billboard, digital billboard, digital billboard, digital billboard, digital billboard, digital billboard, digital billboard, digital billboard, digital billboard, digital billboard, digital billboard, digital billboard, digital billboard, digital billboard, digital billboard, digital billboard, digital billboard, digital billboard, digital billboard, digital billboard, digital billboard, digital billboard, digital billboard, digital billboard, digital billboard, digital billboard, digital billboard, digital billboard, digital billboard, digital billboard, digital billboard, digital billboard, digital billboard, digital billboard, digital billboard, digital billboard, digital billboard, digital billboard, digital billboard, digital billboard, digital billboard, digital billboard, digital billboard, digital billboard, digital billboard, digital billboard, digital billboard, digital billboard, digital billboard, digital billboard, digital billboard, digital billboard, digital billboard, digital billboard, digital billboard, digital billboard, digital billboard, digital billboard, digital billboard, digital billboard, digital billboard, digital billboard, digital billboard, digital billboard, digital billboard, digital billboard, digital billboard, digital billboard, digital billboard, digital billboard, digital billboard, digital billboard, digital billboard, digital billboard, digital billboard, digital billboard, digital billboard, digital billboard, digital billboard, digital billboard, digital billboard, digital billboard, digital billboard, digital billboard, digital billboard, digital billboard, digital billboard, digital billboard, digital billboard, digital billboard, digital billboard, digital billboard, digital billboard, digital billboard, digital billboard, digital billboard, digital billboard, digital billboard, digital billboard, digital billboard, digital billboard, digital billboard, digital billboard, digital billboard, digital billboard, digital billboard, digital billboard, digital billboard, digital billboard, digital billboard, digital billboard, digital billboard, digital billboard, digital billboard, digital billboard, digital bil
@undefined2 changing the repetition_penalty to 1.05 worked for me too.
def generate_prompt(text):
inputs = tokenizer(text, return_tensors='pt')
input_ids = inputs.input_ids.to(device)
attention_mask = inputs.attention_mask.to(device)
output = model.generate(input_ids, attention_mask=attention_mask, repetition_penalty=1.05, max_length=512, eos_token_id=tokenizer.eos_token_id)
return tokenizer.decode(output[0], skip_special_tokens=False)
text2 = f"<s>Prompt: McDonald's hamburger promotion on a red billboard, white lettering,"
generate_prompt(text2)
<s>Prompt: McDonald's hamburger promotion on a red billboard, white lettering, advertisement with posters and flyrets in the style of artgerm.</s>
@mrm8488 Maybe change the default repetition_penalty to slightly over 1 in the example code?
I got the problem when running the code, it generates the exactly same sentence every time. What should I do?
