Generate text using distributed BLOOM and fine-tune it for your own tasks:
Generate text using distributed [BLOOM-176B](https://huggingface.co/bigscience/bloom) and fine-tune it for your own tasks:
```python
```python
from petals import DistributedBloomForCausalLM
from petals import DistributedBloomForCausalLM
@ -58,7 +58,7 @@ Check out more examples and tutorials:
## How does it work?
## How does it work?
- Petals runs large language models like BLOOM-176B **collaboratively** — you load a small part of the model, then team up with people serving the other parts to run inference or fine-tuning.
- Petals runs large language models like [BLOOM-176B](https://huggingface.co/bigscience/bloom)**collaboratively** — you load a small part of the model, then team up with people serving the other parts to run inference or fine-tuning.
- Inference runs at ≈ 1 sec per step (token) — 10x faster than possible with offloading, enough for chatbots and other interactive apps. Parallel inference reaches hundreds of tokens/sec.
- Inference runs at ≈ 1 sec per step (token) — 10x faster than possible with offloading, enough for chatbots and other interactive apps. Parallel inference reaches hundreds of tokens/sec.
- Beyond classic language model APIs — you can employ any fine-tuning and sampling methods by executing custom paths through the model or accessing its hidden states. You get the comforts of an API with the flexibility of PyTorch.
- Beyond classic language model APIs — you can employ any fine-tuning and sampling methods by executing custom paths through the model or accessing its hidden states. You get the comforts of an API with the flexibility of PyTorch.