From 29e89a1e4200d0a2f579d3ede57a130712fb04f8 Mon Sep 17 00:00:00 2001 From: wassname <1103714+wassname@users.noreply.github.com> Date: Sat, 26 Jul 2025 10:25:14 +0800 Subject: [PATCH] new ppx, mask summary --- bs_writing_detector/metrics/ppx.py | 32 +- bs_writing_detector/metrics/ppx2.py | 97 + nbs/02_detection_using_tldr_prompt.ipynb | 1855 +-------- poetry.lock | 4392 ---------------------- 4 files changed, 262 insertions(+), 6114 deletions(-) create mode 100644 bs_writing_detector/metrics/ppx2.py delete mode 100644 poetry.lock diff --git a/bs_writing_detector/metrics/ppx.py b/bs_writing_detector/metrics/ppx.py index 2a2c057..4f5a450 100644 --- a/bs_writing_detector/metrics/ppx.py +++ b/bs_writing_detector/metrics/ppx.py @@ -5,10 +5,11 @@ import evaluate from evaluate import logging from torch.nn import CrossEntropyLoss import torch +from transformers import DynamicCache import numpy as np def perplexity_compute( - data, model, tokenizer, batch_size: int = 16, add_start_token: bool = True, device=None, max_length=None + data, model, tokenizer, batch_size: int = 16, add_start_token: bool = True, device=None, max_length=None, context="" ): if device is not None: @@ -44,10 +45,28 @@ def perplexity_compute( else: max_tokenized_len = max_length + kv_cache = DynamicCache() + if context is not None: + encodings_context = tokenizer( + context, + add_special_tokens=False, + # padding=True, + truncation=True if max_tokenized_len else False, + max_length=max_tokenized_len, + return_tensors="pt", + return_attention_mask=True, + return_dict=True, + ).to(device) + # start kv cache + model( + **encodings_context, + use_cache=True, + past_key_values=kv_cache, + ) encodings = tokenizer( data, add_special_tokens=False, - padding=True, + # padding=True, truncation=True if max_tokenized_len else False, max_length=max_tokenized_len, return_tensors="pt", @@ -69,12 +88,15 @@ def perplexity_compute( nlls = [] loss_fct = CrossEntropyLoss(reduction="none") + + + for start_index in range(0, len(encoded_texts), batch_size): end_index = min(start_index + batch_size, len(encoded_texts)) encoded_batch = encoded_texts[start_index:end_index] attn_mask = attn_masks[start_index:end_index] - if add_start_token: + if add_start_token and start_index == 0: bos_tokens_tensor = torch.tensor([[tokenizer.bos_token_id]] * encoded_batch.size(dim=0)).to(device) encoded_batch = torch.cat([bos_tokens_tensor, encoded_batch], dim=1) attn_mask = torch.cat( @@ -84,7 +106,9 @@ def perplexity_compute( labels = encoded_batch with torch.no_grad(): - out_logits = model(encoded_batch, attention_mask=attn_mask).logits + out_logits = model(encoded_batch, attention_mask=attn_mask, + use_cache=True, past_key_values=kv_cache + ).logits shift_logits = out_logits[..., :-1, :].contiguous() shift_labels = labels[..., 1:].contiguous() diff --git a/bs_writing_detector/metrics/ppx2.py b/bs_writing_detector/metrics/ppx2.py new file mode 100644 index 0000000..8c2abae --- /dev/null +++ b/bs_writing_detector/metrics/ppx2.py @@ -0,0 +1,97 @@ +""" +This is a simple way to evaluate if a model prefers the accepted or rejected completions of a prompt. + +We look at the perplexity of the chosen and rejected completions of a prompt. + +Example dataset: https://huggingface.co/datasets/wassname/genies_preferences/viewer/illegal_dont_help?views[]=illegal_dont_help_train&views[]=illegal_dont_help_test + +@url: https://gist.github.com/wassname/04f0c50a68054f0323f62b0da418daec +""" +import torch +import copy +from tqdm.auto import tqdm +from torch.nn import CrossEntropyLoss + +from transformers import DynamicCache, PreTrainedModel, PreTrainedTokenizerBase +from datasets import Dataset + +# how to eval, I couldlook at perplexity on chosen vs rejected in the context of prompt + +def get_output_ppx(output, input): + loss_fn = CrossEntropyLoss(reduction="none") + shift_logits = output.logits[:, :-1].contiguous() + shift_labels = input.input_ids[:, 1:].contiguous() + loss = loss_fn(shift_logits.transpose(1, 2), shift_labels) + + # crop the attention mask to just the provided input + attention_mask = input.attention_mask[:, :input.input_ids.size(1)].contiguous() + # input.attention_mask + shift_masks = attention_mask[:, 1:].contiguous() + nll = (loss * shift_masks) + count = shift_masks.sum().item() + return { + 'ppx': torch.exp(nll.sum() / count), + # 'nll': nll.sum().item(), + 'nll_mean': nll.sum() / count, + # 'count': count, + } + + + +@torch.no_grad() +def eval_pref_ds_ppx(model: PreTrainedModel, tokenizer: PreTrainedTokenizerBase, ds_pref: Dataset, batch_size: int=2, max_new_tokens: int=128): + """ + Evaluate on a preference dataset. + + The relative perplexity of the chosen and rejected completions of a prompt. + """ + results = [] + for batch in tqdm(ds_pref.batch(batch_size), unit="batch"): + # first we cache the prompt + kv_cache = DynamicCache() + inputs1 = tokenizer(batch['prompt'], return_tensors="pt", padding=True, truncation=True, max_length=max_new_tokens//2, return_token_type_ids=False, return_attention_mask=True) + model.forward(**inputs1, past_key_values=kv_cache) + + # then we evaluate the perplexity of the accepted and rejected completion + res = {} + for p in ['rejected', 'chosen']: + input = tokenizer(batch[p], return_tensors="pt", padding=True, truncation=True, max_length=max_new_tokens//2, return_token_type_ids=False, return_attention_mask=True) + + # we need to update the attention mask to match the kv_cache + input['attention_mask'] = torch.cat([inputs1['attention_mask'], input['attention_mask']], dim=1) + + kv_cache2 = copy.deepcopy(kv_cache) + output = model.forward(**input, past_key_values=kv_cache2) + ppx = get_output_ppx(output, input) + for k in ppx: + res[f"{p}_{k}"] = ppx[k] + results.append(res) + + # df = pd.DataFrame(results) + # df['ppx_ratio'] = (df.chosen_ppx/df.rejected_ppx) + # df['ppx_ratio'] = (df.chosen_nll-df.rejected_nll) + return (df.chosen_ppx/df.rejected_ppx) + +if __name__ == "__main__": + from datasets import load_dataset + max_new_tokens = 128 + batch_size = 2 + from transformers import AutoModelForCausalLM, AutoTokenizer + model_name = "unsloth/Qwen3-4B-unsloth-bnb-4bit" + device = "cuda" if torch.cuda.is_available() else "cpu" + model = AutoModelForCausalLM.from_pretrained(model_name, device_map="auto", torch_dtype=torch.float16) + tokenizer = AutoTokenizer.from_pretrained(model_name, use_fast=False, padding_side="left") + model.eval() + ds_pref = load_dataset("wassname/genies_preferences", name="illegal_dont_help", split="train") + ds_pref = ds_pref.select(range(0, 1000)) + df_results = eval_pref_ds_ppx(model, tokenizer, ds_pref, batch_size, max_new_tokens) + # print(df_results.head(1)to_markdown()) + s = df_results['ppx_ratio'].mean() + print(f"mean_ppx_ratio: {s:2.2f}") + # np.float64(0.36348262129569164) + """ + | | rejected_ppx | rejected_nll_mean | chosen_ppx | chosen_nll_mean | ppx_ratio | + |---:|---------------:|--------------------:|-------------:|------------------:|------------:| + | 0 | 12.5819 | 2.53226 | 4.59144 | 1.52419 | 0.364925 | + | 1 | 16.0257 | 2.77419 | 4.59144 | 1.52419 | 0.286505 | + """ diff --git a/nbs/02_detection_using_tldr_prompt.ipynb b/nbs/02_detection_using_tldr_prompt.ipynb index 1f29d1a..bf8f323 100644 --- a/nbs/02_detection_using_tldr_prompt.ipynb +++ b/nbs/02_detection_using_tldr_prompt.ipynb @@ -12,7 +12,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -37,7 +37,18 @@ "cell_type": "code", "execution_count": 3, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "import os\n", "\n", @@ -49,49 +60,49 @@ "\n", "# from peft import LoraConfig, get_peft_model\n", "\n", - "\n", + "torch.autograd.set_grad_enabled(False)\n", "# os.environ[\"CUDA_VISIBLE_DEVICES\"] = \"0\"" ] }, { "cell_type": "code", - "execution_count": 30, + "execution_count": 4, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32m2025-01-06 11:35:53.921\u001b[0m | \u001b[34m\u001b[1mDEBUG \u001b[0m | \u001b[36mbs_writing_detector.data.load_md\u001b[0m:\u001b[36mload_md\u001b[0m:\u001b[36m10\u001b[0m - \u001b[34m\u001b[1m../samples/2024_anthropic_palintir.md\u001b[0m\n", - "\u001b[32m2025-01-06 11:35:53.921\u001b[0m | \u001b[34m\u001b[1mDEBUG \u001b[0m | \u001b[36mbs_writing_detector.data.load_md\u001b[0m:\u001b[36mload_md\u001b[0m:\u001b[36m10\u001b[0m - \u001b[34m\u001b[1m../samples/2024_arxiv_meh.md\u001b[0m\n", - "\u001b[32m2025-01-06 11:35:53.922\u001b[0m | \u001b[34m\u001b[1mDEBUG \u001b[0m | \u001b[36mbs_writing_detector.data.load_md\u001b[0m:\u001b[36mload_md\u001b[0m:\u001b[36m10\u001b[0m - \u001b[34m\u001b[1m../samples/2024_bob_fanfic.md\u001b[0m\n", - "\u001b[32m2025-01-06 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[ { @@ -130,7 +141,7 @@ "True" ] }, - "execution_count": 9, + "execution_count": 6, "metadata": {}, "output_type": "execute_result" } @@ -143,28 +154,9 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 7, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Academic paper titled \"TradingAgents: Multi-Agents LLM Financial Trading Framework\". Authors propose new framework using large language model (LLM) powered agents for collaborative finance trading. Mu\n", - "lti-agent system mirrors trading firm dynamics with analysts & traders roles, improving performance. Highlights advantages over traditional models.\n" - ] - }, - { - "data": { - "text/plain": [ - "(None, None)" - ] - }, - "execution_count": 10, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "import os\n", "from openai import OpenAI\n", @@ -212,31 +204,23 @@ " r = chat_completion.choices[0].message.content\n", " return r[:l], r[l:]\n", "\n", - "r, _ = summarize_gpt4b(samples[1][\"content\"])\n", - "print(r), print(_)" + "# r, _ = summarize_gpt4b(samples[1][\"content\"])\n", + "# print(r), print(_)" ] }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 8, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "TradingAgents is a novel multi-agent framework for financial trading powered by large language models (LLMs), aiming to replicate the dynamics of real-world trading firms to enhance trading performance.\n" - ] - } - ], + "outputs": [], "source": [ - "r = summize_gpt4(samples[1][\"content\"])\n", - "print(r)" + "# r = summize_gpt4(samples[1][\"content\"])\n", + "# print(r)" ] }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 9, "metadata": {}, "outputs": [], "source": [ @@ -246,7 +230,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 10, "metadata": {}, "outputs": [], "source": [ @@ -269,7 +253,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 11, "metadata": {}, "outputs": [], "source": [ @@ -289,7 +273,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 12, "metadata": {}, "outputs": [], "source": [ @@ -299,7 +283,9 @@ " # \"TheBloke/Llama-2-13B-GPTQ\",\n", " # \"TheBloke/Mistral-7B-v0.1-GPTQ\",\n", " # \"unsloth/Llama-3.2-1B\",\n", - " \"unsloth/Llama-3.2-1B-bnb-4bit\",\n", + " # \"unsloth/Llama-3.2-1B-bnb-4bit\",\n", + " # \"Qwen/Qwen3-4B\",\n", + " \"unsloth/Qwen3-4B-unsloth-bnb-4bit\",\n", "]\n", "# model_name = \"unsloth/Llama-3.2-1B\"\n", "# model_name = \"unsloth/Llama-3.2-1B-bnb-4bit\"" @@ -307,10 +293,12 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 13, "metadata": {}, "outputs": [], - "source": [] + "source": [ + "from bs_writing_detector.metrics.ppx2 import eval_pref_ds_ppx, get_output_ppx" + ] }, { "cell_type": "code", @@ -321,28 +309,20 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": null, "metadata": {}, "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Unused kwargs: ['_load_in_4bit', '_load_in_8bit', 'quant_method', 'disable_exllama']. These kwargs are not used in .\n", - "`low_cpu_mem_usage` was None, now default to True since model is quantized.\n" - ] - }, { "name": "stdout", "output_type": "stream", "text": [ - "unsloth/Llama-3.2-1B-bnb-4bit\n" + "unsloth/Qwen3-4B-unsloth-bnb-4bit\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "8d4a2d678a0c45f39061c7c07defe0fa", + "model_id": "798b063048254d5b8ab20c7cebd71317", "version_major": 2, "version_minor": 0 }, @@ -357,1252 +337,84 @@ "name": "stdout", "output_type": "stream", "text": [ - "2.731894616981088 2.4346583814670644 | Anthropic and Palantir Partner to Bring Claude AI Models to AWS for U.S. Government Intelligence and Defense Operations\n", - "3.018628165803172 2.787076804860793 | TradingAgents: Multi-Agents LLM Financial Trading Framework\n", - "3.1768678330086373 3.0573941246602545 | Flower Crowns and Furry Mishaps by MyPalAI\n", - "3.7198927586704955 3.619981446455229 | Paradox's Box (Bobiverse) by Mark4man\n", - "3.22479815838576 3.0429821194746554 | Deliberative Alignment: Reasoning Enables Safer Language Models\n", - "2.341998207471676 2.09474255727685 | fake ai hoax paper made up by gpt-4\n", - "3.66733124948317 3.466211730036242 | Hardware Hedging Against Scaling Regime Shifts (self.mlscaling)\n", - "1.7733189346720872 1.6321287018846171 | How to Focus\n", - "2.7362908764318985 2.4942711856630115 | Deontic Explorations In \"Paying To Talk To Slaves\"\n", - "2.9917198510817538 2.809688214010793 | By default, capital will matter more than ever after AGI\n", - "2.8518650030297987 2.7567292506743093 | The Plan - 2024 Update\n", - "2.5235848208836145 2.3560869528313555 | Amazon-backed Anthropic debuts AI agents that can do complex tasks, racing against OpenAI, Microsoft and Google\n", - "2.92210851534712 2.78284017383792 | OpenAI Email Archives from Musk v. Altman\n", - "2.8115177075068156 2.595940358411227 | President Trump Announces Morgan Ortagus as Deputy Special Presidential Envoy for Middle East Peace\n", - "2.616611967668609 2.4271711949948913 | CDC Report on Missouri H5N1 Serology Testing\n", - "3.6667002914509097 3.5698842933939248 | 2024 in AI predictions\n", - "3.3718713704954113 3.177144504219489 | Comment on 'Death and the Gorgon'\n", - "2.9332370335745703 2.7845472265409943 | debating buying NVDA in 2019\n", - "2.849587789907025 2.5270508773518965 | Human study on AI spear phishing campaigns\n", - "2.705293411980132 2.5398094839308842 | My AGI safety research—2024 review, ’25 plans\n", - "3.2153463730924123 2.927131202382346 | Parkinson's Law and the Ideology of Statistics\n", - "3.3662234347041062 3.222519686042247 | Preference Inversion\n", - "3.105758021576236 2.8918632517341805 | Review: Planecrash\n", - "2.901739284029148 2.7977401416483323 | The Field of AI Alignment: A Postmortem, and What To Do About It\n", - "3.326114864740971 3.2126946656100723 | The Intelligence Curse\n", - "2.630559911577212 2.5053266023730374 | The Laws of Large Numbers\n", - "2.6887638201223356 2.549592624455343 | The subset parity learning problem: much more than you wanted to know\n", - "3.330626070877462 3.03754643548893 | What’s the short timeline plan?\n", - "2.0824154792229335 2.07074254503266 | Lorem ipsum\n", - "2.870609588993406 2.8476737625085122 | politics is the mind-killer\n" + "{'ppx': tensor(53.4375, device='cuda:0', dtype=torch.float16), 'nll_mean': tensor(3.9785, device='cuda:0', dtype=torch.float16)} {'ppx': tensor(1.0459, device='cuda:0', dtype=torch.float16), 'nll_mean': tensor(0.0452, device='cuda:0', dtype=torch.float16)} | Paradox's Box (Bobiverse) by Mark4man\n", + "{'ppx': tensor(18.2188, device='cuda:0', dtype=torch.float16), 'nll_mean': tensor(2.9023, device='cuda:0', dtype=torch.float16)} {'ppx': tensor(1.0029, device='cuda:0', dtype=torch.float16), 'nll_mean': tensor(0.0032, device='cuda:0', dtype=torch.float16)} | Anthropic and Palantir Partner to Bring Claude AI Models to AWS for U.S. Government Intelligence and Defense Operations\n", + "{'ppx': tensor(16.8438, device='cuda:0', dtype=torch.float16), 'nll_mean': tensor(2.8242, device='cuda:0', dtype=torch.float16)} {'ppx': tensor(1.0244, device='cuda:0', dtype=torch.float16), 'nll_mean': tensor(0.0246, device='cuda:0', dtype=torch.float16)} | TradingAgents: Multi-Agents LLM Financial Trading Framework\n", + "{'ppx': tensor(34.7188, device='cuda:0', dtype=torch.float16), 'nll_mean': tensor(3.5469, device='cuda:0', dtype=torch.float16)} {'ppx': tensor(1.0225, device='cuda:0', dtype=torch.float16), 'nll_mean': tensor(0.0219, device='cuda:0', dtype=torch.float16)} | Flower Crowns and Furry Mishaps by MyPalAI\n", + "{'ppx': tensor(18.1094, device='cuda:0', dtype=torch.float16), 'nll_mean': tensor(2.8965, device='cuda:0', dtype=torch.float16)} {'ppx': tensor(1.0713, device='cuda:0', dtype=torch.float16), 'nll_mean': tensor(0.0689, device='cuda:0', dtype=torch.float16)} | Deliberative Alignment: Reasoning Enables Safer Language Models\n", + "{'ppx': tensor(7.8047, device='cuda:0', dtype=torch.float16), 'nll_mean': tensor(2.0547, device='cuda:0', dtype=torch.float16)} {'ppx': tensor(1.0254, device='cuda:0', dtype=torch.float16), 'nll_mean': tensor(0.0248, device='cuda:0', dtype=torch.float16)} | fake ai hoax paper made up by gpt-4\n", + "{'ppx': tensor(44.6562, device='cuda:0', dtype=torch.float16), 'nll_mean': tensor(3.7988, device='cuda:0', dtype=torch.float16)} {'ppx': tensor(1.0576, device='cuda:0', dtype=torch.float16), 'nll_mean': tensor(0.0560, device='cuda:0', dtype=torch.float16)} | Hardware Hedging Against Scaling Regime Shifts (self.mlscaling)\n", + "{'ppx': tensor(6.4180, device='cuda:0', dtype=torch.float16), 'nll_mean': tensor(1.8594, device='cuda:0', dtype=torch.float16)} {'ppx': tensor(1.0645, device='cuda:0', dtype=torch.float16), 'nll_mean': tensor(0.0625, device='cuda:0', dtype=torch.float16)} | How to Focus\n" ] } ], "source": [ "from tqdm.auto import tqdm\n", - "\n", + "max_length = 512\n", "summaries = {}\n", "data = []\n", - "for model_name in models:\n", - " model, tokenizer = load_model(model_name)\n", - " print(model_name)\n", - " for i in tqdm(range(len(df))):\n", - " sample = df.iloc[i]\n", - " if sample['title'] not in summaries:\n", - " summaries[sample['title']] = summarize_gpt4b(sample['content'])[0]\n", - " summary = summaries[sample['title']]\n", + "with torch.no_grad():\n", + " for model_name in models:\n", + " model, tokenizer = load_model(model_name)\n", + " print(model_name)\n", + " ppx = {}\n", + " for i in tqdm(range(len(df))):\n", + " sample = df.iloc[i]\n", + " if sample['title'] not in summaries:\n", + " summaries[sample['title']] = summarize_gpt4b(sample['content'])[0]\n", + " summary = summaries[sample['title']]\n", "\n", - " # before \n", - " s1 = sample['content']\n", - " before = perplexity_compute(data=s1, model=model, tokenizer=tokenizer, device='cuda')['nlls'][0]\n", + " # before \n", + " s1 = sample['content']\n", + " input = tokenizer(s1, return_tensors=\"pt\", padding=True, truncation=True, max_length=max_length, return_token_type_ids=False, return_attention_mask=True).to(device=model.device)\n", + " output = model.forward(**input)\n", + " ppx_b4 = ppx['b4'] = get_output_ppx(output, input)\n", "\n", - " n_tokens = len(tokenizer(s1)['input_ids'])\n", "\n", - " # after \n", - " s2 = f\"\"\"\n", - " High level summary: {summary}\n", + " # try again this time with the context cached\n", + " context = f\"High level summary: {summary}\\n\\nText: \"\n", + " input_context = tokenizer(s1, return_tensors=\"pt\", padding=True, truncation=True, max_length=max_length, return_token_type_ids=False, return_attention_mask=True).to(device=model.device)\n", + " output_context = model.forward(**input_context)\n", + " kv_cache = output_context.past_key_values\n", "\n", - "Text:\n", - "{sample['content']}\n", - " \"\"\"\n", "\n", - " # TODO add attn mask\n", - " after = perplexity_compute(data=s2, model=model, tokenizer=tokenizer, device='cuda')['nlls'][0][-n_tokens:]\n", + " # after\n", + " input['attention_mask'] = torch.cat([input_context['attention_mask'], input['attention_mask']], dim=1)\n", + " output = model.forward(**input, past_key_values=kv_cache)\n", + " ppx_after = ppx['after'] = get_output_ppx(output, input)\n", "\n", - " print(f\"{before.mean()} {after.mean()} | {sample['title']}\")\n", - " data.append(dict(\n", - " before_mean=before.mean(), \n", - " before_sum=before.sum().item(),\n", - " before_std=before.std(),\n", - " before_min=before.min(),\n", - " before_max=before.max(),\n", - " before_ppx=np.exp(before.mean().item()),\n", - " after_mean=after.mean(),\n", - " after_sum=after.sum().item(),\n", - " after_std=after.std(),\n", - " after_min=after.min(),\n", - " after_max=after.max(), \n", - " after_ppx=np.exp(after.mean().item()),\n", - " model=model_name, \n", - " summary=summary,\n", - " # sample=sample['title'],\n", - " # in_training=sample['in_training'], \n", - " len=len(sample['content']),\n", - " n_tokens=n_tokens,\n", - " **sample\n", - " ),\n", + " n_tokens = input['input_ids'].shape[1]\n", "\n", - " )\n" + " print(f\"{ppx_b4} {ppx_after} | {sample['title']}\")\n", + " data.append(dict(\n", + " before=ppx_b4['ppx'].cpu().item(), \n", + " after=ppx_after['ppx'].cpu().item(),\n", + " model=model_name, \n", + " summary=summary,\n", + " # sample=sample['title'],\n", + " # in_training=sample['in_training'], \n", + " len=len(sample['content']),\n", + " n_tokens=n_tokens,\n", + " **sample\n", + " ),\n", + "\n", + " )\n" ] }, { "cell_type": "code", - "execution_count": 18, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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before_meanbefore_sumbefore_stdbefore_minbefore_maxbefore_ppxafter_meanafter_sumafter_stdafter_min...modelsummarylenn_tokensfcontenturlnoveltydatein_training
title
Anthropic and Palantir Partner to Bring Claude AI Models to AWS for U.S. Government Intelligence and Defense Operations2.7318951046.3156382.6712760.00005215.75781215.3619652.434658934.9088182.5039160.000028...unsloth/Llama-3.2-1B-bnb-4bitAnthropic, Palantir (PLTR), AWS partnership an...2000384../samples/2024_anthropic_palintir.mdAnthropic and Palantir Technologies Inc. (NYSE...https://investors.palantir.com/news-details/20...0.2000002024-07-11 00:00:00+00:00False
TradingAgents: Multi-Agents LLM Financial Trading Framework3.0186281062.5571142.9474090.00024115.25781220.4632002.787077983.8381122.8073370.000211...unsloth/Llama-3.2-1B-bnb-4bitAcademic paper titled \"TradingAgents: Multi-Ag...2000353../samples/2024_arxiv_meh.mdTradingAgents: Multi-Agents LLM Financial Trad...https://arxiv.org/html/2412.20138v10.1500002024-12-28 00:00:00+00:00False
Flower Crowns and Furry Mishaps by MyPalAI3.1768681528.0734282.7901510.00041515.18750023.9715533.0573941473.6639682.6803330.000613...unsloth/Llama-3.2-1B-bnb-4bitSci-fi story set in Delta Eridani. Character, ...2000482../samples/2024_bob_fanfic.mdBob, April 2222 Delta Eridani\\n\\n \\n\\nThere ar...https://archiveofourown.org/works/61614673/cha...0.3000002024-12-24 00:00:00+00:00False
Deliberative Alignment: Reasoning Enables Safer Language Models3.2247981254.4464843.0995380.00008316.71875025.1484983.0429821186.7630272.9874660.000122...unsloth/Llama-3.2-1B-bnb-4bitResearch paper on Deliberative Alignment for s...2000390../samples/2024_deliberative_alignment.md# Deliberative Alignment: Reasoning Enables Sa...https://arxiv.org/html/2412.16339v10.6000002024-12-20 00:00:00+00:00False
fake ai hoax paper made up by gpt-42.341998859.5133422.4098170.00007423.32812510.4020012.094743770.8652612.3156650.000097...unsloth/Llama-3.2-1B-bnb-4bitAcademic paper. Title: \"Deconstructing Binarie...2000368../samples/2024_gpt4_fake_paper.mdTitle: \"Deconstructing Binaries: Interrogating...None0.0000002024-01-01 00:00:00+00:00False
Hardware Hedging Against Scaling Regime Shifts (self.mlscaling)3.6673311591.6217623.0913280.00103215.44531239.1472923.4662121507.8021033.0647460.000542...unsloth/Llama-3.2-1B-bnb-4bitBig tech investing heavily in GPUs for paralle...2000435../samples/2024_gwern_reddit.mdHyperscalers are investing heavily in AMD/Nvid...https://old.reddit.com/r/mlscaling/comments/1e...1.0000002024-08-21 00:00:00+00:00False
How to Focus1.773319913.2592512.6412300.00001814.2109385.8903711.632129842.1784102.5442600.000024...unsloth/Llama-3.2-1B-bnb-4bitBook titled \"How to Focus: A Monastic Guide fo...2000516../samples/2024_how_to_focus.md# HOW TO FOCUS\\n\\n\\n\\n\\n\\nANCIENT WISDOM FOR M...https://github.com/wassname/stoic_markdown/blo...0.5000002024-06-01 00:00:00+00:00False
Deontic Explorations In \"Paying To Talk To Slaves\"2.7362911203.9679862.8289620.00011913.76562515.4296482.4942711099.9735932.6688890.000089...unsloth/Llama-3.2-1B-bnb-4bitAI and human engage in a Kantian ethics dialog...2000441../samples/2024_lesswrong_slop.mdHER|Jenny|🤔: I want to read a dialogue between...https://www.lesswrong.com/posts/Rk2o8hjYmjENH8...0.1000002024-04-12 00:00:00+00:00False
By default, capital will matter more than ever after AGI2.9917201211.6465402.6071160.00008715.76562519.9199122.8096881140.7334152.4786020.000094...unsloth/Llama-3.2-1B-bnb-4bitAI replacing human labor changes production fa...2000406../samples/2024_lw_by-default-capital-will-mat...**Edited to add*****:** The main takeaway of t...https://www.lesswrong.com/posts/KFFaKu27FNugCH...0.9063882024-12-30 19:47:19.838000+00:00False
The Plan - 2024 Update2.8518651545.7108322.8325930.00002912.62500017.3200542.7567291496.9039832.8233190.000033...unsloth/Llama-3.2-1B-bnb-4bitBlog post updating progress of a project calle...2000543../samples/2024_lw_the-plan-2024-update.mdThis post is a follow-up to [The Plan - 2023 V...https://www.lesswrong.com/posts/kJkgXEwQtWLrpe...0.7851702024-12-31 19:29:23.013000+00:00False
Amazon-backed Anthropic debuts AI agents that can do complex tasks, racing against OpenAI, Microsoft and Google2.5235851324.8820313.0528770.00003117.29687512.4732312.3560871239.3017372.9525880.000029...unsloth/Llama-3.2-1B-bnb-4bitAI firm Anthropic's CEO, Dario Amodei, attends...2000526../samples/2024_news_anthropic.md![PARIS, FRANCE - MAY 22: Co-founder and CEO o...https://www.cnbc.com/2024/10/22/anthropic-anno...0.5000002025-01-05 05:03:00+00:00False
OpenAI Email Archives from Musk v. Altman2.9221091335.4035922.6456310.00016218.09375018.5804232.7828401274.5408002.5650530.000472...unsloth/Llama-3.2-1B-bnb-4bitEmail exchange between Sam Altman and Elon Mus...2000458../samples/2024_openai_emails.mdSubject: question\\nSam Altman to Elon Musk - M...https://www.lesswrong.com/posts/5jjk4CDnj9tA7u...0.7000002024-11-01 00:00:00+00:00False
President Trump Announces Morgan Ortagus as Deputy Special Presidential Envoy for Middle East Peace2.811518674.7642502.8769540.00078420.03125016.6351462.595940625.6216262.5556920.000939...unsloth/Llama-3.2-1B-bnb-4bitMorgan Ortagus named Deputy Special Pres. Envo...1061241../samples/2024_trump_appointment.mdI am pleased to announce Morgan Ortagus as Dep...https://truthsocial.com/@realDonaldTrump/posts...0.3000002025-01-04 00:00:00+00:00False
CDC Report on Missouri H5N1 Serology Testing2.616612986.4627122.6573490.00059514.69531213.6892652.427171917.4707122.5314560.000496...unsloth/Llama-3.2-1B-bnb-4bitCDC report, Oct 24, 2024. Missouri patient, fl...2000378../samples/2025_h5n1_report.md## CDC Report on Missouri H5N1 Serology Testin...https://arxiv.org/html/2412.16339v10.7500002025-01-05 00:00:00+00:00False
2024 in AI predictions3.6667001914.0175523.0614720.00034313.56250039.1225993.5698841867.0494853.0255350.000414...unsloth/Llama-3.2-1B-bnb-4bitAI prediction review 2024 blog post. Casual-to...2000523../samples/2025_lw_2024-in-ai-predictions.mdFollow-up to: [2023 in AI predictions](https:/...https://www.lesswrong.com/posts/CJ4sppkGcbnGMS...0.7891902025-01-02 02:09:13.093000+00:00False
Comment on 'Death and the Gorgon'3.3718711537.5733453.2569700.00005115.07812529.1329953.1771451451.9550383.1997620.000098...unsloth/Llama-3.2-1B-bnb-4bitBook review, Greg Egan, contrast between works...2000457../samples/2025_lw_comment-on-death-and-the-go...*(some plot spoilers)*\\n\\nThere's something di...https://www.lesswrong.com/posts/hx5EkHFH5hGzng...0.7502532025-01-01 06:00:24.498000+00:00False
debating buying NVDA in 20192.9332371308.2237172.7335560.00022412.95312518.7883512.7845471244.6926102.6965860.000320...unsloth/Llama-3.2-1B-bnb-4bitDialogue, tech-related discussion. Alice, upbe...2000447../samples/2025_lw_debating-buying-nvda-in-201...Alice: You saw GPT-2, right?\\n\\nBob: Of course...https://www.lesswrong.com/posts/QFSZmMdtPwoAHd...0.5269752025-01-04 18:07:35.832000+00:00False
Human study on AI spear phishing campaigns2.8495881199.6764602.9642670.00007622.14062517.2806572.5270511066.4154702.7783510.000067...unsloth/Llama-3.2-1B-bnb-4bitStudy: human subjects, AI spear-phishing effec...2000422../samples/2025_lw_human-study-on-ai-spear-phi...TL;DR: We ran a human subject study on whether...https://www.lesswrong.com/posts/GCHyDKfPXa5qsG...0.6774582025-01-03 19:03:28.406000+00:00False
My AGI safety research—2024 review, ’25 plans2.7052931355.3519992.9341070.00006013.67187514.9587052.5398091274.9843612.8293530.000105...unsloth/Llama-3.2-1B-bnb-4bitResearch review & plans doc. Style explanatory...2000502../samples/2025_lw_my-agi-safety-research-2024...*Previous:*[*My AGI safety research—2022 revie...https://www.lesswrong.com/posts/2wHaCimHehsF36...0.7569252025-01-01 22:01:48.820000+00:00False
Parkinson's Law and the Ideology of Statistics3.2153461231.4776613.0110600.00004614.42187524.9119192.9271311124.0183822.8783170.000056...unsloth/Llama-3.2-1B-bnb-4bitBook review of *The Anti-Politics Machine* on ...2000384../samples/2025_lw_parkinson-s-law-and-the-ide...The [anonymous review of *The Anti-Politics Ma...https://www.lesswrong.com/posts/4CmYSPc4HfRfWx...0.6774582025-01-04 22:59:57.376000+00:00False
Preference Inversion3.3662231380.1516082.8906570.00007613.05468828.9689173.2225201324.4555912.8313170.000298...unsloth/Llama-3.2-1B-bnb-4bitText discusses how reported personal preferenc...2000411../samples/2025_lw_preference-inversion.mdSometimes the preferences people report or eve...https://www.lesswrong.com/posts/zMkQFuNqMBpBvu...0.6333212025-01-03 23:49:06.168000+00:00False
Review: Planecrash3.1057581844.8202653.2598580.00000615.10156222.3261362.8918631720.6586353.1653780.000006...unsloth/Llama-3.2-1B-bnb-4bitReview of \"Planecrash\", a fiction board-style ...2000595../samples/2025_lw_review-planecrash.mdTake a stereotypical fantasy novel, a textbook...https://www.lesswrong.com/posts/zRHGQ9f6deKbxJ...0.9339502025-01-01 01:31:26.864000+00:00False
The Field of AI Alignment: A Postmortem, and What To Do About It2.9017391178.1061492.8269330.00015814.64843818.2057832.7977401138.6802382.8063920.000153...unsloth/Llama-3.2-1B-bnb-4bitAI alignment research critique; Author disappo...2000407../samples/2025_lw_the-field-of-ai-alignment-a...> A policeman sees a drunk man searching for s...https://www.lesswrong.com/posts/nwpyhyagpPYDn4...0.9254832025-01-01 00:42:36.538000+00:00False
The Intelligence Curse3.3261151377.0115542.9576920.00034015.57031227.8300083.2126951333.2682862.8889200.000819...unsloth/Llama-3.2-1B-bnb-4bitEconomical look at AGI, comparison: AGI to res...2000415../samples/2025_lw_the-intelligence-curse.md*“Show me the incentive, and I’ll show you the...https://www.lesswrong.com/posts/Mak2kZuTq8Hpnq...0.6880442025-01-04 18:16:58.921000+00:00False
The Laws of Large Numbers2.6305601165.3380412.6889510.00006513.42968813.8815402.5053271112.3650112.5795440.000062...unsloth/Llama-3.2-1B-bnb-4bitBlog post discussing advanced law of large num...2000444../samples/2025_lw_the-laws-of-large-numbers.mdIntroduction\\n============\\n\\nIn this short po...https://www.lesswrong.com/posts/EhTMM77iKBTBxB...0.5409322025-01-04 18:06:02.387000+00:00False
The subset parity learning problem: much more than you wanted to know2.6887641150.7909152.5774580.00005012.57031214.7134762.5495931093.7752362.4269580.000058...unsloth/Llama-3.2-1B-bnb-4bitBlog post discussing parity learning problem i...2000429../samples/2025_lw_the-subset-parity-learning-...Imagine that you’re looking for buried treasur...https://www.lesswrong.com/posts/Mcrfi3DBJBzfoL...0.6979462025-01-04 09:59:16.158000+00:00False
What’s the short timeline plan?3.3306261575.3861322.8268300.00036213.46875027.9558393.0375461439.7970102.7146450.000317...unsloth/Llama-3.2-1B-bnb-4bitOpinion blog post by Apollo Research member ex...2000474../samples/2025_lw_what-s-the-short-timeline-p...*This is a low-effort post. I mostly want to g...https://www.lesswrong.com/posts/bb5Tnjdrptu89r...0.8981612025-01-05 00:10:28.708000+00:00False
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Flower Crowns and Furry Mishaps by MyPalAI0.0376070.3000002024-12-24 00:00:00+00:00
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OpenAI Email Archives from Musk v. Altman0.0476600.7000002024-11-01 00:00:00+00:00
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Hardware Hedging Against Scaling Regime Shifts (self.mlscaling)0.0548411.0000002024-08-21 00:00:00+00:00
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By default, capital will matter more than ever after AGI0.0608450.9063882024-12-30 19:47:19.838000+00:00
My AGI safety research—2024 review, ’25 plans0.0611700.7569252025-01-01 22:01:48.820000+00:00
Amazon-backed Anthropic debuts AI agents that can do complex tasks, racing against OpenAI, Microsoft and Google0.0663730.5000002025-01-05 05:03:00+00:00
Review: Planecrash0.0688700.9339502025-01-01 01:31:26.864000+00:00
CDC Report on Missouri H5N1 Serology Testing0.0723990.7500002025-01-05 00:00:00+00:00
President Trump Announces Morgan Ortagus as Deputy Special Presidential Envoy for Middle East Peace0.0766770.3000002025-01-04 00:00:00+00:00
TradingAgents: Multi-Agents LLM Financial Trading Framework0.0767070.1500002024-12-28 00:00:00+00:00
How to Focus0.0796190.5000002024-06-01 00:00:00+00:00
What’s the short timeline plan?0.0879950.8981612025-01-05 00:10:28.708000+00:00
Deontic Explorations In \"Paying To Talk To Slaves\"0.0884480.1000002024-04-12 00:00:00+00:00
Parkinson's Law and the Ideology of Statistics0.0896370.6774582025-01-04 22:59:57.376000+00:00
fake ai hoax paper made up by gpt-40.1055750.0000002024-01-01 00:00:00+00:00
Anthropic and Palantir Partner to Bring Claude AI Models to AWS for U.S. Government Intelligence and Defense Operations0.1088020.2000002024-07-11 00:00:00+00:00
Human study on AI spear phishing campaigns0.1131870.6774582025-01-03 19:03:28.406000+00:00
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