Text Generation
Transformers
PyTorch
TensorBoard
Safetensors
gpt_bigcode
Generated from Trainer
text-generation-inference
Instructions to use HuggingFaceH4/starchat-beta with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use HuggingFaceH4/starchat-beta with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="HuggingFaceH4/starchat-beta")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("HuggingFaceH4/starchat-beta") model = AutoModelForCausalLM.from_pretrained("HuggingFaceH4/starchat-beta") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use HuggingFaceH4/starchat-beta with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "HuggingFaceH4/starchat-beta" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "HuggingFaceH4/starchat-beta", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/HuggingFaceH4/starchat-beta
- SGLang
How to use HuggingFaceH4/starchat-beta 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 "HuggingFaceH4/starchat-beta" \ --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": "HuggingFaceH4/starchat-beta", "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 "HuggingFaceH4/starchat-beta" \ --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": "HuggingFaceH4/starchat-beta", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use HuggingFaceH4/starchat-beta with Docker Model Runner:
docker model run hf.co/HuggingFaceH4/starchat-beta
Every Inference gives the prompt as part of output also, any way to remove that?
#26
by vermanic - opened
Hey, i have this general problem of any model on HF outputting with the input prompt always, any way to exclude that?
as I just need the output.
Code:
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch
checkpoint = "HuggingFaceH4/starchat-beta"
device = "cuda" if torch.cuda.is_available() else "cpu" # "cuda:X" for GPU usage or "cpu" for CPU usage
class Model:
def __init__(self):
print("Running in " + device)
self.tokenizer = AutoTokenizer.from_pretrained(checkpoint)
self.model = AutoModelForCausalLM.from_pretrained(checkpoint, device_map='auto')
def infer(self, input_text, token_count):
inputs = self.tokenizer.encode(input_text, return_tensors="pt").to(device)
outputs = self.model.generate(inputs, max_new_tokens=token_count)
return self.tokenizer.decode(outputs[0])
Also, max_new_tokens means the amount of tokens with which I want the model to respond with, right?
vermanic changed discussion title from Every Inference gives the prompt as part of output also, any way to fix this? to Every Inference gives the prompt as part of output also, any way to remove that?
Resolved by:
return self.tokenizer.decode(outputs[0])[len(input_text):]
vermanic changed discussion status to closed