Instructions to use BatsResearch/bonito-v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use BatsResearch/bonito-v1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="BatsResearch/bonito-v1")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("BatsResearch/bonito-v1") model = AutoModelForCausalLM.from_pretrained("BatsResearch/bonito-v1") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use BatsResearch/bonito-v1 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "BatsResearch/bonito-v1" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "BatsResearch/bonito-v1", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/BatsResearch/bonito-v1
- SGLang
How to use BatsResearch/bonito-v1 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 "BatsResearch/bonito-v1" \ --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": "BatsResearch/bonito-v1", "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 "BatsResearch/bonito-v1" \ --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": "BatsResearch/bonito-v1", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use BatsResearch/bonito-v1 with Docker Model Runner:
docker model run hf.co/BatsResearch/bonito-v1
regarding working on my own dataset
Hello,
I am trying to use bonito and i have created my own csv data to generate some instructions pair but it is not allowing me to do so. The error i am getting is i have to get access for my own dataset to work on it. Could you please help me, i am rookie to this?
Hi @prascoder .
Based on authors' tutorial file, I guess you should modify the unannotated_text part.
This documentation might helpful to your situation.
For me, I worked with json file with [{"input": "something", "output": ""}] format.
@seungwoos is right! You will need to modify the unannotated_text object. You should load the dataset as follows:
from datasets import load_dataset
unannotated_dataset = load_dataset("csv", data_files="my_file.csv")
Once you load the dataset, pass the object along the column containing the unannotated text (context_col):
synthetic_dataset = bonito.generate_tasks(
unannotated_dataset,
context_col="input",
task_type="nli",
sampling_params=sampling_params
)
Let me know if you run into any more issues.