Instructions to use bigscience/bloom-7b1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use bigscience/bloom-7b1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="bigscience/bloom-7b1")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("bigscience/bloom-7b1") model = AutoModelForCausalLM.from_pretrained("bigscience/bloom-7b1") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use bigscience/bloom-7b1 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "bigscience/bloom-7b1" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "bigscience/bloom-7b1", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/bigscience/bloom-7b1
- SGLang
How to use bigscience/bloom-7b1 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 "bigscience/bloom-7b1" \ --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": "bigscience/bloom-7b1", "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 "bigscience/bloom-7b1" \ --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": "bigscience/bloom-7b1", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use bigscience/bloom-7b1 with Docker Model Runner:
docker model run hf.co/bigscience/bloom-7b1
Is it possible to fine-tuning the 7b1 model on 4 A100 (80G) gpus?
I have tried many configurations the FT 7b1 on four A100. But unfortunately, I got OOM all the time. So I am curious about minimal demands of GPUS to FT this model. Could you share your experiences?
If you freeze some layers, even in 1 A100 it is possible.
Check this: https://gitlab.inria.fr/synalp/plm4all/-/tree/main/finetune_accelerate
It is still a draft but it's running.
@hatimbr hi, how to transform the quat fp32 model to fp16, and then can i ft it with 24g RTX3090ti for fp16?
sure, this model-fit is fp32 of quantitated?
hi @redauzhang you can pass the parameter torch_dtype=torch.float16 (or even better torch_dtype=torch.bfloat16) in the from_pretrained method.