Spaces:
Sleeping
Sleeping
| from __future__ import annotations | |
| import torch | |
| import gradio as gr | |
| import spaces | |
| from huggingface_hub import hf_hub_download | |
| from safetensors.torch import load_file | |
| from transformers import AutoTokenizer, AutoModelForCausalLM | |
| from configuration_riemann import RiemannConfig | |
| from modeling_riemann import RiemannForCausalLM | |
| from analysis import ( | |
| token_surprisal, | |
| surprisal_html, | |
| greedy_continue, | |
| contrast_rows, | |
| md_table, | |
| WIKI_FEWSHOT, | |
| CONTRAST_SET, | |
| ) | |
| OPENPEER_REPO = "OpenPeerAI/OpenPeerLLM" | |
| RIEMANN_REPO = "OpenPeerAI/My-Name-is-Riemann-Prerelease" | |
| tokenizer = AutoTokenizer.from_pretrained("gpt2") | |
| tokenizer.pad_token = tokenizer.eos_token | |
| config = RiemannConfig( | |
| vocab_size=50257, | |
| hidden_size=768, | |
| num_hidden_layers=12, | |
| num_attention_heads=12, | |
| intermediate_size=3072, | |
| max_position_embeddings=1024, | |
| ) | |
| openpeer = RiemannForCausalLM(config) | |
| _state = load_file(hf_hub_download(OPENPEER_REPO, "model.safetensors")) | |
| _missing, _unexpected = openpeer.load_state_dict(_state, strict=False) | |
| openpeer.eval() | |
| LOAD_REPORT = f"{len(_state)} tensors loaded from {OPENPEER_REPO}, {len(_missing)} missing, {len(_unexpected)} unexpected." | |
| N_OPENPEER = sum(p.numel() for p in openpeer.parameters()) | |
| _riemann = {"model": None} | |
| def riemann(): | |
| if _riemann["model"] is None: | |
| m = AutoModelForCausalLM.from_pretrained( | |
| RIEMANN_REPO, | |
| trust_remote_code=True, | |
| dtype=torch.bfloat16, | |
| low_cpu_mem_usage=True, | |
| ) | |
| m.eval() | |
| _riemann["model"] = m | |
| return _riemann["model"] | |
| def _gpu(m): | |
| if m.device.type != "cuda": | |
| m.to("cuda") | |
| def complete(subject, opening, max_new, compare): | |
| prompt_tail = f"{subject.strip()} {opening.strip()}".strip() | |
| _gpu(openpeer) | |
| a = greedy_continue( | |
| openpeer, | |
| tokenizer, | |
| WIKI_FEWSHOT + prompt_tail, | |
| int(max_new), | |
| repetition_penalty=1.3, | |
| ) | |
| out = ( | |
| f"### OpenPeerLLM ({N_OPENPEER/1e6:.0f}M, the starting rung)\n{prompt_tail}{a}" | |
| ) | |
| if compare: | |
| r = riemann() | |
| _gpu(r) | |
| b = greedy_continue( | |
| r, | |
| tokenizer, | |
| WIKI_FEWSHOT + prompt_tail, | |
| int(max_new), | |
| repetition_penalty=1.25, | |
| max_context=2048, | |
| ) | |
| out += f"\n\n### My Name is Riemann Prerelease (13.1B, six rungs later)\n{prompt_tail}{b}" | |
| return out | |
| def analyse(text, compare): | |
| _gpu(openpeer) | |
| a = token_surprisal(openpeer, tokenizer, text) | |
| out = surprisal_html(a, f"OpenPeerLLM ({N_OPENPEER/1e6:.0f}M):") | |
| if compare: | |
| r = riemann() | |
| _gpu(r) | |
| b = token_surprisal(r, tokenizer, text, max_length=2048) | |
| out += "<hr/>" + surprisal_html(b, "My Name is Riemann Prerelease (13.1B):") | |
| out += f"<p><b>Perplexity {a['ppl']:.1f} to {b['ppl']:.1f}</b> across the growth ladder on this text.</p>" | |
| return out | |
| def contrast(compare): | |
| _gpu(openpeer) | |
| rows_a = contrast_rows(openpeer, tokenizer, CONTRAST_SET) | |
| if not compare: | |
| return md_table([[r[0], r[1], r[2]] for r in rows_a]) | |
| r = riemann() | |
| _gpu(r) | |
| rows_b = contrast_rows(r, tokenizer, CONTRAST_SET) | |
| header = ("Text", "OpenPeerLLM 163M", "Riemann Prerelease 13.1B") | |
| rows = [[ra[0], ra[1], rb[1]] for ra, rb in zip(rows_a, rows_b)] | |
| return ( | |
| md_table(rows, header) | |
| + "\n\nPerplexity on each string. Lower is better on fluent text; the gap between fluent text and noise " | |
| "should widen as the model improves. OpenPeerLLM is the 163M checkpoint that every later rung inherits from." | |
| ) | |
| DESCRIPTION = f""" | |
| # OpenPeerLLM Demo | |
| [OpenPeerAI/OpenPeerLLM](https://huggingface.co/{OPENPEER_REPO}) is a decentralised-training language model | |
| by Andrew Magdy Kamal Nassief (DOI [10.57967/hf/6469](https://doi.org/10.57967/hf/6469)). This space loads its | |
| published `model.safetensors` ({N_OPENPEER/1e6:.0f}M parameters) into a faithful reimplementation of its | |
| architecture, with the causal attention mask the reference forward pass omits. {LOAD_REPORT} | |
| OpenPeerLLM is the **starting rung** of the | |
| [My Name is Riemann](https://huggingface.co/{RIEMANN_REPO}) growth ladder. Every tab lets you put the | |
| 13.1B Prerelease next to it on the same input, so the effect of six rungs of growth and training is visible | |
| directly. Both are base models: they complete and score text, they do not chat. | |
| """ | |
| with gr.Blocks(title="OpenPeerLLM Demo") as demo: | |
| gr.Markdown(DESCRIPTION) | |
| with gr.Tab("Complete an article"): | |
| gr.Markdown( | |
| "Two short example articles are prepended so the model knows the register." | |
| ) | |
| with gr.Row(): | |
| subject = gr.Textbox(label="Subject", value="The Nile") | |
| opening = gr.Textbox( | |
| label="Opening clause", | |
| value="is a major north-flowing river in northeastern Africa. It", | |
| ) | |
| with gr.Row(): | |
| max_new = gr.Slider(16, 120, value=64, step=8, label="Max new tokens") | |
| cmp1 = gr.Checkbox( | |
| value=True, label="Also run My Name is Riemann Prerelease (13.1B)" | |
| ) | |
| out1 = gr.Markdown() | |
| gr.Button("Complete", variant="primary").click( | |
| complete, [subject, opening, max_new, cmp1], out1 | |
| ) | |
| gr.Examples( | |
| [ | |
| ["Mount Kilimanjaro", "is a dormant volcano in Tanzania. It"], | |
| [ | |
| "The Rosetta Stone", | |
| "is a stele inscribed with three versions of a decree issued in", | |
| ], | |
| ], | |
| inputs=[subject, opening], | |
| ) | |
| with gr.Tab("Analyse text"): | |
| gr.Markdown( | |
| "Each token is coloured by how surprised the model was to see it; green is expected, red is surprising." | |
| ) | |
| a_text = gr.Textbox( | |
| label="Text", | |
| lines=4, | |
| value="The Great Pyramid of Giza is the oldest of the Seven Wonders of the Ancient World.", | |
| ) | |
| cmp2 = gr.Checkbox( | |
| value=True, label="Also analyse with My Name is Riemann Prerelease (13.1B)" | |
| ) | |
| out2 = gr.HTML() | |
| gr.Button("Analyse", variant="primary").click(analyse, [a_text, cmp2], out2) | |
| with gr.Tab("Fluency test"): | |
| gr.Markdown( | |
| "Six fixed strings from fluent English down to random words, scored by perplexity." | |
| ) | |
| cmp3 = gr.Checkbox( | |
| value=True, label="Compare with My Name is Riemann Prerelease (13.1B)" | |
| ) | |
| out3 = gr.Markdown() | |
| gr.Button("Run fluency test", variant="primary").click(contrast, cmp3, out3) | |
| gr.Markdown( | |
| "OpenPeerLLM is released under OPNL, MIT and CC BY 4.0. Maintained by OpenPeer AI." | |
| ) | |
| demo.queue(max_size=16).launch() | |