Spaces:
Runtime error
Runtime error
| import torch | |
| import torch.nn.functional as F | |
| import transformers | |
| import gradio as gr | |
| from src.client import DistributedBloomForCausalLM | |
| INITIAL_PEERS = ['/ip4/193.106.95.184/tcp/443/p2p/QmSXDXLeSMXjS4YerDrdn1zpGQaNzkZ9ogN2SoAEyAdDhs'] | |
| import hivemind # test that DHT instances work on localhost | |
| dht1 = hivemind.DHT(start=True) | |
| dht2 = hivemind.DHT(start=True, initial_peers=dht1.get_visible_maddrs()) | |
| tokenizer = transformers.BloomTokenizerFast.from_pretrained("bigscience/test-bloomd-6b3") | |
| model = DistributedBloomForCausalLM.from_pretrained("bigscience/test-bloomd-6b3", initial_peers=INITIAL_PEERS, low_cpu_mem_usage=True, torch_dtype=torch.float32) | |
| def inference(text, seq_length=1): | |
| input_ids = tokenizer(text, return_tensors='pt')['input_ids'] | |
| with torch.inference_mode(), model.transformer.h.inference_session() as remote_transformer: | |
| for i in range(seq_length): | |
| h = model.transformer.word_embeddings(input_ids) | |
| h = model.transformer.word_embeddings_layernorm(h) | |
| h = remote_transformer.step(h) | |
| return repr(h) | |
| iface = gr.Interface(fn=inference, inputs="text", outputs="text") | |
| iface.launch() |