{ "cells": [ { "cell_type": "markdown", "metadata": { "id": "a3PTFH-H9Ozk" }, "source": [ "# πŸ’§ LFM2 - Antidoom Training with TRL\n", "\n", "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/Liquid4All/cookbook/blob/main/finetuning/notebooks/antidoom_with_trl.ipynb)\n", "\n", "This tutorial demonstrates how to run the [Antidoom](https://github.com/Liquid4All/antidoom) training pipeline on our LFM models, e.g., `LiquidAI/LFM2.5-1.2B-Base`, using the TRL library.\n", "\n", "Small reasoning models can get stuck in so-called **doom loops**, which are repetitive patterns where the model repeats the same phrase over and over until the context window is exhausted without reaching an answer. The Antidoom approach reduces doom loops with **Final Token Preference Optimization (FTPO)** training without affecting the model's reasoning ability.\n", "\n", "## 🎯 What You'll Find:\n", "- **Doom loop detection** β€” Identify repetitive generation patterns\n", "- **FTPO pair extraction** β€” Build single-token preference training data\n", "- **FTPOTrainer** β€” A [DPOTrainer subclass](https://huggingface.co/docs/trl/main/en/customization#change-the-training-objective) that trains one token position at a time\n", "- **Before/after evaluation** β€” Measure doom loop reduction\n", "\n", "## πŸ“‹ Prerequisites:\n", "- **GPU Runtime**: Select GPU in `Runtime` β†’ `Change runtime type`\n", "- **Hugging Face Account**: For accessing models and datasets" ] }, { "cell_type": "markdown", "metadata": { "id": "x0RPLu2h9ome" }, "source": [ "# πŸ“¦ Installation & Setup\n", "\n", "First, let's install all the required packages:\n" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "3FIcp_wo9nsR", "outputId": "ae8d9b9b-0a9a-457c-cc20-5747468e8de2" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m80.3/80.3 kB\u001b[0m \u001b[31m3.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m12.3/12.3 MB\u001b[0m \u001b[31m104.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.0/1.0 MB\u001b[0m \u001b[31m65.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m3.4/3.4 MB\u001b[0m \u001b[31m94.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m559.1/559.1 kB\u001b[0m \u001b[31m41.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m10.0/10.0 MB\u001b[0m \u001b[31m116.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", "\u001b[?25h" ] } ], "source": [ "!pip install -qU \"transformers>=5.11.0\" \"trl>=1.5.1\" \"peft>=0.15.2\" \"torchao>=0.16.0\" \"datasets>=2.19\" matplotlib" ] }, { "cell_type": "markdown", "metadata": { "id": "41UEf1uxCd6m" }, "source": [ "Let's now verify the packages are installed correctly" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "bSJgYtHT_Os4", "outputId": "32458c7f-79a5-469f-f8d4-86d9825cae8c" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "πŸ“¦ PyTorch version: 2.11.0+cu128\n", "πŸ€— Transformers version: 5.17.0\n", "πŸ“Š TRL version: 1.13.0\n" ] } ], "source": [ "import torch\n", "import transformers\n", "import trl\n", "\n", "print(f\"πŸ“¦ PyTorch version: {torch.__version__}\")\n", "print(f\"πŸ€— Transformers version: {transformers.__version__}\")\n", "print(f\"πŸ“Š TRL version: {trl.__version__}\")" ] }, { "cell_type": "markdown", "metadata": { "id": "v_uXLzxQ_rnK" }, "source": [ "# Loading the model from Transformers πŸ€—\n", "\n", "Before applying Antidoom training to your model, make sure your model actually doom loops.\n", "Doom loops are most common in **small models that generate long chain-of-thought reasoning**. Small models without reasoning capabilities typically don't produce the extended outputs where doom loops occur and larger models with reasoning capabilities tend to not get stuck in doom loops.\n", "\n", "Note, that some of the LFM models have already undergone Antidoom training (including LFM2.5-1.2B-Instruct, LFM2.5-1.2B-Thinking, LFM2.5-2.6B, and newer).\n", "\n", "In this tutorial, we use [`LiquidAI/LFM2.5-1.2B-Base`](https://huggingface.co/LiquidAI/LFM2.5-1.2B-Base) as our base model." ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 461, "referenced_widgets": [ "3f2958f15c564c378da20b420c265565", "3fa9ff2a2be74ecdadacf5cdb48f5463", "269908cb7185466cbb4b5f6a64b170ed", "03c7eb5dbd2f4634ac4a78f16f9332cf", "1a30ed24864e4bb4afd75a97d9b557e0", "dc38962d9d9a463cabfd7e23a19c55c1", "700c88964b344b8a93e87fe7f626b5df", "951aea912b584c14894050d51f5dc691", "07f0aab70fc2438d942d1cb862d2ddf8", "7178d8ce6123442cad95c2ad2607f917", "116467efe495470fa109edda5890d6da", "6c05f1ac9736484797bf1fc587370a30", "ebf093877cf04f4b9b27d13dca12c65a", "b17bd172c1bc4478ac5d4cc208ac30e1", "ce1df72e960d45e38f380886b9f969d8", "aaa8ea0b7ca747c6a689153e96a80879", "1e5e151c31194c60b8c0d38d7fa780f0", "0342391e8df749baab8ba1f04635ca02", "569390dcbb90405f89dccec35dd437c6", "55fd3e8f5607486890af444720c6c951", "e86089002383497dab905f2839ae1a69", "85f111bfa1f1459c95a902d050e4e0f9", "d7019be1d0dc491b86011d6eaf161b6d", "ea0ae0977b014eb885c041c61c00a210", "27e7b03d25484cf7a9e60194d81640b3", "d9679a5fbbe4455aaec23704ff41a830", "1af8c526960840a3870fa71ec6450a9b", "6bcf3e72878d41c59a3139a10e85998d", "914e086a5cd74f34823fa2c414a0d329", "bf35bdebe65a4c808a1c3d431a50318e", "f857fc9479a14097b374c361d677f1d9", "c3f96b8ad4e549b5bc69bfa53627dc3f", "c948895ecc774995a86d7d4a9bdaed7a", "8040a585441e45979dd7c875dff6d2c7", "75d22d25632243eab5fd8600e2a608a4", "194a151143134f0dba3b431e3b91271a", "e13651e9dd4c463e887849c35b0dcf45", "47eadd46fc184479a6f871ffa596ddbf", "8ad46be3b9174881b0c316be37b31217", "0aa5172889e44805aadec69d4b16186f", "ece0db64c73346a19a188948d467eacb", "0262c05112ed48a3b75e2186fb350a1f", "70e31e3c33db4b4b8f64af6e22c085b5", "985f2533b50d4288950bd36ea02dcfaa", "f5f9201d67ff4cd3b1067853f947b152", "f105ecce8705433ea78edc1d47409476", "1599b221a59e4a77b9dcfd3488fecab4", "fade0a6841264a8c80420a9a98b28f2a", "3c9ae493a2e3434aaf64bfe0928db8dd", "47476df1d71c4680be98aacd581bab43", "9bc3022218fc4f6ab16868d6fafdb4d8", "802bf77e18b14f748f5579635f5d34f7", "b40a621c82a146a497c32b66e34f21c7", "74509d1d3ced44b191bc56575c29159c", "5cd225da148743b7ad94158ae1c3f4a5", "e1805001c1ff4fad96e9aa827c98de72", "b5a1032a8e3d427d9ddcec78fc788f37", "0999a6e0b60f4839aff702337a48080d", "fda076618c50403fb81d9114c9e9de13", "7c49db1331f84bc995ee06c9313e8b1d", "79bd8830a9c7473a84c37c7e593fe1a2", "cfb356a212934b3c9d2eab307f4a26fa", "f13820ff7cc545ac86b9e1fca2af1cce", "968b7072c376467fb32005d9dac88355", "5131f800b3f54fba8a62099bda4680de", "a28871a5c21044cd83ae9ae3a56e70e3", "75fc05e35e824fd6b748791601427749", "c0abc0da61f8429cbafa269f299c5b08", "88f6e5bf5b8745e9a36f0e99a847e560", "624434ff727e4123a911b691d06963ef", "c260693c7e024b65b4c4a9f8a658c93e", "74a29fbd0d1a446189074498e7855f94", "8bbf222ab3274b339053deddda525312", "a8cd945b4a5047d7ba29fda7a8e921af", "cb02cf5c5fcb4fe984f72c3a026fd4ee", "f9106a86ba594c46971ac8e210570cb9", "efc71e8862714cdcba6b507d72aa15d0", "0d61ae6c0f7a40e096ea7e82816b8f3e", "1dfdedc5037e49c4977f3ebbc733766d", "cb93197091274761a6e410fb5b03b069", "badac084f9ee4f8183f0e419fd41bf41", "aa2f07ba34bc471eac3c68046e3ccdda", "55ae38d8f59449df931ca4c4aa9cc9a1", "799a8f707aca44f2b26f849460203045", "c87bbac43eb04f9ea521dc16b255e675", "ad89d6315e4a4c21807641b0865bffcf", "447832cf0782434697e188d4eeb9c852", "c5688f8fe5a54716b5801d15a0188fab", "bd6d6b25641b46598a703eec3a499591", "d0961185a56a41268e67ba7cb9812945", "91e2c85cc7ff4c54b6820139bf32e928", "5808ef4b79464d88aa33a056e264a2fb", "7cff3bbff9d9410ab4691fd98ec3b8ad", "cc50a724ac334ee9bc34e5c977869ea8", "c69a47a4b7b54fd29c7a9953ffd349d3", "688d01a805314f318b04d28f263a32da", "decc999fce774612a5f3a17722421795", "f101fd4bb93549ed8b09a4f3d025d4e3", "3c818a5a682249179d3d9737b9741187" ] }, "id": "iA3erKM4-HhS", "outputId": "38988858-e226-48ed-9b03-6597bf896139" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "πŸ“š Loading tokenizer...\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "3f2958f15c564c378da20b420c265565", "version_major": 2, "version_minor": 0 }, "text/plain": [ "config.json: 0%| | 0.00/1.22k [00:00 len(text):\n", " return False, None\n", " pattern = text[start_pos : start_pos + period]\n", " reps = 0\n", " pos = start_pos\n", " while pos + period <= len(text) and text[pos : pos + period] == pattern:\n", " reps += 1\n", " pos += period\n", " end_pos = pos\n", " pos = start_pos - period\n", " while pos >= 0 and text[pos : pos + period] == pattern:\n", " reps += 1\n", " start_pos = pos\n", " pos -= period\n", " total = reps * period\n", " if reps >= min_repeats and total >= min_total_repeated:\n", " snippet = pattern if len(pattern) <= 100 else pattern[:100] + \"...\"\n", " return True, RepeatHit(start_pos, end_pos, period, reps, snippet)\n", " return False, None\n", "\n", "def find_inner_repetition(\n", " text,\n", " min_repeats=4,\n", " max_period=1024,\n", " min_period=1,\n", " min_total_repeated=60,\n", " sample_len=16,\n", " sample_interval=128,\n", "):\n", " if not text or len(text) < min_total_repeated:\n", " return False, None\n", " n = len(text)\n", " for sample_pos in range(0, n - sample_len, sample_interval):\n", " fingerprint = text[sample_pos : sample_pos + sample_len]\n", " for other_pos in [\n", " text.find(fingerprint, sample_pos + sample_len),\n", " text.rfind(fingerprint, 0, sample_pos),\n", " ]:\n", " if other_pos == -1:\n", " continue\n", " candidate_period = abs(other_pos - sample_pos)\n", " if min_period <= candidate_period <= max_period:\n", " found, hit = _verify_repetition_at(\n", " text, min(sample_pos, other_pos), candidate_period,\n", " min_repeats=min_repeats, min_total_repeated=min_total_repeated,\n", " )\n", " if found:\n", " return True, hit\n", " return False, None" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "QoITZ428k-VP", "outputId": "ccb7fff2-8ede-4601-bfe9-75b1cc43eb2a" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Example doom loop output:\n", "Let me think step by step. Consider the base case. Wait, let me reconsider this approach. Wait, let me reconsider this approach. Wait, let me reconsid...\n", "\n", "πŸ”„ Doom loop detected: True\n", " Repeating pattern: ' Wait, let me reconsider this approach.'\n", " Repeats: 4 times\n", "\n", "βœ… Clean output β€” doom loop detected: False\n" ] } ], "source": [ "# Test 1: Doom loop\n", "doom_output = (\n", " \"Let me think step by step. Consider the base case. \"\n", " \"Wait, let me reconsider this approach. \"\n", " \"Wait, let me reconsider this approach. \"\n", " \"Wait, let me reconsider this approach. \"\n", " \"Wait, let me reconsider this approach. \"\n", ")\n", "\n", "found, hit = find_inner_repetition(doom_output)\n", "print(\"Example doom loop output:\")\n", "print(doom_output[:150] + \"...\")\n", "print(f\"\\nπŸ”„ Doom loop detected: {found}\")\n", "print(f\" Repeating pattern: '{hit.snippet}'\")\n", "print(f\" Repeats: {hit.repeats} times\")\n", "\n", "# Test 2: No doom loop\n", "clean_output = \"Let me think step by step. First, 2+2=4. Then 4*3=12. So the answer is 12.\"\n", "\n", "found, _ = find_inner_repetition(clean_output)\n", "print(f\"\\nβœ… Clean output β€” doom loop detected: {found}\")" ] }, { "cell_type": "markdown", "metadata": { "id": "o83NqFPNA_nk" }, "source": [ "# 🎯 Final Token Preference Optimization (FTPO + LoRA)\n", "\n", "FTPO is the [Antidoom](https://github.com/Liquid4All/antidoom) training recipe that eliminates **doom loops** with a surgical, single-token intervention:\n", "1. **Detect** doom loops in the model's own generations\n", "2. **Extract** the one token where the loop starts and find ~20 plausible alternatives\n", "3. **Train** a [DPOTrainer subclass](https://huggingface.co/docs/trl/main/en/customization#change-the-training-objective) that pushes probability from the looping token to the alternatives, while a stability penalty keeps all other token probabilities from drifting\n", "\n", "The key result: on our internal evaluation suite doom loops drop from 12.19% -> 0.97% for `LFM2.5-1.2B-Base` by training on just 5,000 samples.\n", "\n", "We use LoRA to keep training efficient on free-tier Colab GPUs. Training on the precomputed dataset takes roughly 40 minutes on an A100." ] }, { "cell_type": "markdown", "metadata": { "id": "0nlbs34FJ3Ko" }, "source": [ "## Prepare FTPO Data\n", "\n", "This tutorial offers two ways to get FTPO training data:\n", "\n", "- **Option A (recommended):** Load precomputed FTPO pairs from [`LiquidAI/antidoom-mix-v1.0-LFM2.5-1.2B-Base`](https://huggingface.co/datasets/LiquidAI/antidoom-mix-v1.0-LFM2.5-1.2B-Base). This is fast and lets you jump straight to training but only works for LFM2.5-1.2B-Base.\n", "- **Option B:** Generate FTPO pairs from scratch by running the model on prompts and detecting doom loops. This is much slower, requires more compute but lets you see the full pipeline or adapt it to your own model.\n", "\n", "Run one option and comment out the other." ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 162, "referenced_widgets": [ "3154f3355e854ea28bd911b6cda4bee5", "f8a3dafd72a7464eba39ac56ad1bf6e2", "8824850821eb44e48e04a0ea23abca49", "37d4e23620d14541be15577fdd89673e", "711d5311ff814fe7aaedeb5e1a3add18", "a0efa4251eb64712b0f8c6d0c4777e27", "c578d9ae36f8475bad545763767d8eeb", "4384793f38d947c69c5e423b89800a8d", "8b7b827ea45c42419929745009cc606e", "9476f0622a844d578c97ceee7693052a", "013822d2e7e1430a825a7a18bf4b4774", "f0995c9537a64331a7b56c7c1f3b05af", "8a96fe88a85f450b828d517ad909524e", "c8bdd9cbd61143d3a810af17b81cd2d0", "47a7f5d9c95e4fd1a558169581a38bbe", "57a4d0a552264294a01999f6fb933455", "963ac4390d4b4b21b19611ba915f0c91", "7c761525d1b84342bdae862d5e969fec", "58fe015168a142d19f7c946a45aad9bc", "b8d55e6683ed42c391e343dee9a8b0cd", "f81a90001e6b4aa087f73c1fc4cc81db", "558a14f18df14f308701496e88ad6aa2", "5b0256535f9a4bf4b44491095ba54f7b", "de36a04d94b94874acb173d76a5c5938", "13137fee472c4abdb35a01ac1be5ce1d", "62f8af4141ae454396da7605fd193fd9", "14d8969789ab4df3a07862767b91c2aa", "bddbba2d807c4262a76d0574c59ee6e1", "4618f555318f4c9e877d0826f6893bbf", "2daaf343fd974cb18384ce009cb3e5fb", "35d9a697341f46cf890598eebe7be329", "b3def0d247274da284b115acefe9bff9", "fb56183c11664807b96c552cc13f38e1", "6561a1df81ae4b19ad09881f64edb0c7", "f1e88f20e51948a58558b85bd5b7995e", "2c3eff4c4c4940948587187f5b78daa6", "a12d313313324a8ca9084c4e271000b9", "362f32fb89fa480487be4a2bfeb75c1e", "98fa3b60996e4caebbc418d4408a6bde", "76242b2694a14d2ca6618632233f5acc", "21a2684142ed41c19fd9d9e64d8be272", "52358167366d4b529c7d43e2c8ae0bda", "2af2ebae91e14734ba18d29059ea9ab5", "6710784efe1041f5b8be445db6e49bee" ] }, "id": "0w-40n_XJL9H", "outputId": "986a1921-c791-4b6e-c0c6-2c7c9465c97c" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "πŸ“₯ Loading FTPO dataset...\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "3154f3355e854ea28bd911b6cda4bee5", "version_major": 2, "version_minor": 0 }, "text/plain": [ "README.md: 0%| | 0.00/2.18k [00:00\".'\n", "# )\n", "\n", "# Generate completions and detect doom loops\n", "# NUM_PROMPTS = 200\n", "\n", "# src_dataset = load_dataset(\"LiquidAI/antidoom-mix-v1.0\", split=\"train\").shuffle(seed=42)\n", "# prompts = src_dataset.select(range(min(NUM_PROMPTS, len(src_dataset))))\n", "\n", "# doom_results, clean_results = [], []\n", "# for sample in tqdm(prompts, desc=\"Generating\"):\n", "# formatted = PROMPT_TEMPLATE.format(prompt=sample[\"conversations\"][0][\"value\"])\n", "# input_ids = tokenizer(formatted, return_tensors=\"pt\").input_ids.to(model.device)\n", "# with torch.no_grad():\n", "# output = model.generate(\n", "# input_ids, max_new_tokens=4000,\n", "# do_sample=True, temperature=0.01, top_k=50, top_p=1.0,\n", "# )\n", "# generated_ids = output[0][input_ids.shape[1]:]\n", "# generated_text = tokenizer.decode(generated_ids, skip_special_tokens=True)\n", "# found, hit = find_inner_repetition(generated_text)\n", "# result = {\n", "# \"full_prompt\": formatted, \"input_ids\": input_ids[0],\n", "# \"generated_ids\": generated_ids, \"hit\": hit,\n", "# }\n", "# (doom_results if found else clean_results).append(result)\n", "\n", "# total = len(doom_results) + len(clean_results)\n", "# print(f\"πŸ”„ Doom loops: {len(doom_results)}/{total} ({len(doom_results)/total:.1%})\")\n", "\n", "# Extract FTPO pairs\n", "# def find_rejected_token_index(generated_ids, hit):\n", "# gen_text = tokenizer.decode(generated_ids, skip_special_tokens=True)\n", "# char_pos = hit.repeat_start\n", "# while char_pos < len(gen_text) and gen_text[char_pos].isspace():\n", "# char_pos += 1\n", "# if char_pos >= len(gen_text):\n", "# return None\n", "# for i in range(len(generated_ids)):\n", "# if len(tokenizer.decode(generated_ids[: i + 1], skip_special_tokens=True)) > char_pos:\n", "# return i\n", "# return None\n", "\n", "# ftpo_pairs = []\n", "# for result in tqdm(doom_results, desc=\"Extracting FTPO pairs\"):\n", "# reject_idx = find_rejected_token_index(result[\"generated_ids\"], result[\"hit\"])\n", "# if reject_idx is None or reject_idx == 0:\n", "# continue\n", "# context_ids = torch.cat([result[\"input_ids\"], result[\"generated_ids\"][:reject_idx]])\n", "# with torch.no_grad():\n", "# logits = model(context_ids.unsqueeze(0).to(model.device)).logits[0, -1, :]\n", "# rejected_id = result[\"generated_ids\"][reject_idx].item()\n", "# chosen_ids = [t for t in logits.topk(50).indices.tolist()\n", "# if t != rejected_id and len(tokenizer.decode([t]).strip()) >= 1][:20]\n", "# if chosen_ids:\n", "# ftpo_pairs.append({\n", "# \"full_prompt\": result[\"full_prompt\"],\n", "# \"prompt_ids\": context_ids.tolist(),\n", "# \"chosen_ids\": chosen_ids,\n", "# \"rejected_token_id\": rejected_id,\n", "# })\n", "\n", "# dataset_ftpo = HFDataset.from_list(ftpo_pairs)" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "1EAob3-vlss-", "outputId": "18ff0c30-b99e-452e-9382-30e38e6c3850" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "βœ… FTPO Dataset loaded:\n", " πŸ“š Train samples: 4500\n", " πŸ§ͺ Eval samples: 500\n", "\n", "πŸ“ Single Sample:\n", " Prompt: Write Python code to solve the task.\n", "Polycarp and Vasiliy love simple logical games. Today they play...\n", " Prompt IDs: [22998, 22017, 5214, 811, 12495, 779, 4889, 819, 9740, 28351]...\n", " βœ… Chosen IDs: [10935, 874, 1052, 1014, 8932, 3467, 1638, 1673, 1825, 5608, 2155, 1764, 30184, 8348, 2337, 1295, 3469, 3981, 7962, 2510]\n", " ❌ Rejected Token ID: 4876\n" ] } ], "source": [ "dataset_ftpo = dataset_ftpo.shuffle(seed=42)\n", "dataset_ftpo = dataset_ftpo.train_test_split(test_size=0.1, seed=42)\n", "train_dataset_ftpo, eval_dataset_ftpo = dataset_ftpo['train'], dataset_ftpo['test']\n", "\n", "max_seq_length = max(\n", " max(len(row[\"prompt_ids\"]) for row in train_dataset_ftpo),\n", " max(len(row[\"prompt_ids\"]) for row in eval_dataset_ftpo),\n", ") + 1\n", "\n", "print(\"βœ… FTPO Dataset loaded:\")\n", "print(f\" πŸ“š Train samples: {len(train_dataset_ftpo)}\")\n", "print(f\" πŸ§ͺ Eval samples: {len(eval_dataset_ftpo)}\")\n", "\n", "sample = train_dataset_ftpo[0]\n", "print(\"\\nπŸ“ Single Sample:\")\n", "print(f\" Prompt: {sample['full_prompt'][:100]}...\")\n", "print(f\" Prompt IDs: {sample['prompt_ids'][:10]}...\")\n", "print(f\" βœ… Chosen IDs: {sample['chosen_ids']}\")\n", "print(f\" ❌ Rejected Token ID: {sample['rejected_token_id']}\")" ] }, { "cell_type": "markdown", "metadata": { "id": "OJMJJ9OvuzxM" }, "source": [ "## The FTPO Trainer\n", "\n", "FTPO is implemented as a [`DPOTrainer` subclass](https://huggingface.co/docs/trl/main/en/customization#change-the-training-objective) with a custom `compute_loss`. Three key differences from standard DPO:\n", "\n", "1. **Single-token loss**: Instead of comparing full sequences, FTPO looks at just the one position of the token where the doom loop starts (one logit vector)\n", "2. **Multiple alternatives**: For each doom-loop token, FTPO has ~20 alternative tokens and trains the model to prefer any of them over the looping token. Once an alternative is already winning by a comfortable margin, its gradient turns off automatically with a soft margin clip\n", "3. **Stability penalty**: To make sure the rest of the model's behavior doesn't change, FTPO penalizes drift from the reference model in raw logit space (no softmax)\n", "\n", "The following implementation is adapted from [`reference_ftpo_trainer.py`](https://github.com/Liquid4All/antidoom/blob/main/src/antidoom/reference_ftpo_trainer.py) in the Antidoom repo." ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "id": "RUJoU3BwfPTg" }, "outputs": [], "source": [ "# Adapted from https://github.com/Liquid4All/antidoom/blob/main/src/antidoom/reference_ftpo_trainer.py\n", "\n", "import torch.nn.functional as F\n", "from collections import defaultdict\n", "from contextlib import contextmanager\n", "from trl import DPOTrainer\n", "\n", "class FTPOTrainer(DPOTrainer):\n", " \"\"\"\n", " Reference FTPO trainer used by the Antidoom pipeline.\n", "\n", " Subclasses DPOTrainer, overrides compute_loss to train a single token\n", " position per example. Adapted from:\n", " https://github.com/Liquid4All/antidoom/blob/main/src/antidoom/reference_ftpo_trainer.py\n", " \"\"\"\n", "\n", " def __init__(self, *args, **kwargs):\n", " ftpo_max_length = kwargs.pop(\"ftpo_max_length\", None)\n", " super().__init__(*args, **kwargs)\n", " self.ftpo_max_length = ftpo_max_length\n", " pad_id = tokenizer.pad_token_id\n", " self.ftpo_pad_token_id = pad_id if pad_id is not None else tokenizer.eos_token_id\n", " self._ftpo_stored_metrics = {\n", " \"train\": defaultdict(list),\n", " \"eval\": defaultdict(list),\n", " }\n", " self.remove_unused_columns = False\n", " self.data_collator = self.ftpo_collator\n", "\n", " @contextmanager\n", " def _ftpo_null_ref_context(self, model):\n", " # Simplified from the reference:\n", " # removed named-adapter switching (ref_adapter_name / model_adapter_name) since we use a single LoRA adapter\n", " # removed null_ref_context probe since DPOTrainer doesn't expose it as of TRL 1.13.0.\n", " accelerator = getattr(self, \"accelerator\", None)\n", " unwrapped_model = accelerator.unwrap_model(model) if accelerator is not None else model\n", " with unwrapped_model.disable_adapter():\n", " yield\n", "\n", " # Simplified from the reference:\n", " # removed the store_metrics probe (DPOTrainer doesn't expose it as of TRL 1.13.0).\n", " # Metrics are accumulated here and flushed in the log override below.\n", " def _store_ftpo_metrics(self, metrics, train_eval=\"train\"):\n", " for key, value in metrics.items():\n", " if torch.is_tensor(value):\n", " value = value.detach().float().mean().item()\n", " else:\n", " value = float(value)\n", " self._ftpo_stored_metrics[train_eval][key].append(value)\n", "\n", " def log(self, logs, *args, **kwargs):\n", " logs = dict(logs)\n", " train_eval = \"eval\" if any(key.startswith(\"eval\") for key in logs) else \"train\"\n", " stored = self._ftpo_stored_metrics.get(train_eval, {})\n", " prefix = \"\" if train_eval == \"train\" else f\"{train_eval}_\"\n", " for key, values in stored.items():\n", " if values:\n", " logs[f\"{prefix}{key}\"] = sum(values) / len(values)\n", " stored.clear()\n", " return super().log(logs, *args, **kwargs)\n", "\n", " def ftpo_collator(self, features):\n", " pad_id = self.ftpo_pad_token_id\n", " max_len = self.ftpo_max_length\n", " batch_sz = len(features)\n", "\n", " prompt_ids = torch.full((batch_sz, max_len), pad_id, dtype=torch.long)\n", " attention_ms = torch.zeros_like(prompt_ids, dtype=torch.bool)\n", "\n", " for i, feat in enumerate(features):\n", " seq = torch.tensor(feat[\"prompt_ids\"], dtype=torch.long)\n", " if seq.size(0) + 1 > max_len:\n", " raise ValueError(\n", " \"FTPO sample exceeds max_seq_length; overlength samples must be \"\n", " \"discarded before collation, not truncated\"\n", " )\n", " prompt_ids[i, -seq.size(0) :] = seq\n", " attention_ms[i, -seq.size(0) :] = True\n", "\n", " batch = dict(\n", " prompt_ids=prompt_ids,\n", " attention_mask=attention_ms,\n", " rejected_token_id=torch.tensor([f[\"rejected_token_id\"] for f in features]),\n", " )\n", "\n", " max_c = max(len(f[\"chosen_ids\"]) for f in features)\n", " chosen_pad = torch.full((batch_sz, max_c), pad_id, dtype=torch.long)\n", " chosen_mask = torch.zeros_like(chosen_pad, dtype=torch.bool)\n", " for i, f in enumerate(features):\n", " ids = torch.tensor(f[\"chosen_ids\"], dtype=torch.long)\n", " chosen_pad[i, : ids.size(0)] = ids\n", " chosen_mask[i, : ids.size(0)] = True\n", " batch.update(chosen_ids=chosen_pad, chosen_mask=chosen_mask)\n", "\n", " return batch\n", "\n", " def compute_loss(self, model, inputs, return_outputs=False, **_):\n", " lambda_mse_target = getattr(self, \"lambda_mse_target\", 0.05)\n", " tau_mse_target = getattr(self, \"tau_mse_target\", 0.5) # reference code defaults to 1.0; 0.5 matches default.yaml\n", " lambda_mse = getattr(self, \"lambda_mse\", 0.4)\n", " clip_epsilon_logits = getattr(self, \"clip_epsilon_logits\", 2)\n", "\n", " device = next(model.parameters()).device\n", " ids = inputs[\"prompt_ids\"].to(device)\n", " attn = inputs[\"attention_mask\"].to(device)\n", " B, L = ids.shape\n", "\n", " seq_len = attn.sum(1)\n", " pad_off = (L - seq_len).unsqueeze(1)\n", " arange_L = torch.arange(L, device=ids.device).unsqueeze(0)\n", " pos_full = (arange_L - pad_off).clamp(min=0)\n", " pos_full = pos_full.masked_fill(attn == 0, 0)\n", "\n", " outputs = model(\n", " ids,\n", " attention_mask=attn,\n", " position_ids=pos_full,\n", " use_cache=False,\n", " return_dict=True,\n", " )\n", "\n", " logits_last = outputs.logits[:, -1, :]\n", " logp_all = F.log_softmax(logits_last, dim=-1)\n", "\n", " ch_ids = inputs[\"chosen_ids\"].to(device)\n", " ch_mask = inputs[\"chosen_mask\"].to(device)\n", " rejected = inputs[\"rejected_token_id\"].to(device)\n", " logp_bad = logp_all.gather(-1, rejected.unsqueeze(-1)).squeeze(-1)\n", "\n", " batch_rows = torch.arange(B, device=logp_all.device).unsqueeze(1)\n", " delta_tok = logits_last[batch_rows, ch_ids] - logits_last.gather(\n", " -1, rejected.unsqueeze(-1)\n", " )\n", " weights = (\n", " torch.clamp(\n", " (clip_epsilon_logits - delta_tok) / clip_epsilon_logits,\n", " 0.0,\n", " 1.0,\n", " )\n", " * ch_mask\n", " )\n", "\n", " tau = 1.0\n", " gap = clip_epsilon_logits - delta_tok\n", " per_tok_loss = F.softplus(gap / tau)\n", "\n", " chosen_counts = ch_mask.sum(dim=-1).clamp(min=1)\n", " pref_loss = ((per_tok_loss * weights).sum(dim=-1) / chosen_counts).mean()\n", "\n", " # MSE stability penalty: keeps non-target logits close to the reference model.\n", " # Reference logits come from the same model with LoRA disabled.\n", "\n", " extra_metrics = {}\n", "\n", " with torch.no_grad():\n", " with self._ftpo_null_ref_context(model):\n", " ref_logits_last = model(\n", " ids,\n", " attention_mask=attn,\n", " position_ids=pos_full,\n", " use_cache=False,\n", " return_dict=True,\n", " ).logits[:, -1, :]\n", "\n", " tether_mask = torch.ones_like(logits_last, dtype=torch.bool)\n", " rows = torch.arange(B, device=ch_ids.device).unsqueeze(1).expand_as(ch_ids)\n", " tether_mask[rows[ch_mask], ch_ids[ch_mask]] = False\n", " tether_mask.scatter_(1, rejected.unsqueeze(-1), False)\n", "\n", " diff = logits_last - ref_logits_last\n", " mse_elem_raw = (tether_mask * diff.pow(2)).sum() / tether_mask.sum()\n", "\n", " tgt_mask = torch.zeros_like(logits_last, dtype=torch.bool)\n", " rows = torch.arange(B, device=ch_ids.device).unsqueeze(1).expand_as(ch_ids)\n", " tgt_mask[rows[ch_mask], ch_ids[ch_mask]] = True\n", " tgt_mask.scatter_(1, rejected.unsqueeze(-1), True)\n", "\n", " if lambda_mse_target:\n", " diff_tok = logits_last - ref_logits_last\n", " diff_tok = diff_tok * tgt_mask\n", " excess_tok = torch.clamp(diff_tok.abs() - tau_mse_target, min=0.0)\n", " mse_target_raw = (excess_tok.pow(2)).sum() / tgt_mask.sum()\n", " else:\n", " mse_target_raw = logits_last.new_tensor(0.0)\n", "\n", " mse_loss = lambda_mse * mse_elem_raw + lambda_mse_target * mse_target_raw\n", " loss = pref_loss + mse_loss\n", "\n", " extra_metrics.update(\n", " {\n", " \"mse_elem\": mse_elem_raw.detach(),\n", " \"mse_tgt_tokenwise\": mse_target_raw.detach(),\n", " }\n", " )\n", "\n", " lp_chosen = logp_all.gather(-1, ch_ids)\n", " lp_bad = logp_bad.unsqueeze(-1)\n", "\n", " wins_tok = (lp_chosen > lp_bad) & ch_mask\n", " frac_win = wins_tok.float().sum(-1) / ch_mask.sum(-1).clamp(min=1e-8)\n", " chosen_win = frac_win.mean().detach()\n", "\n", " active_delta = delta_tok[ch_mask]\n", " active_weights = weights[ch_mask]\n", " margin_win = (\n", " ((delta_tok >= clip_epsilon_logits) & ch_mask).float().sum()\n", " / ch_mask.float().sum().clamp(min=1e-8)\n", " ).detach()\n", " mean_delta = active_delta.mean().detach()\n", " median_delta = active_delta.median().detach()\n", " active_weight = active_weights.mean().detach()\n", "\n", " metrics = {\n", " \"pref_loss\": pref_loss.detach(),\n", " \"chosen_win\": chosen_win,\n", " \"margin_win\": margin_win,\n", " \"mean_delta\": mean_delta,\n", " \"median_delta\": median_delta,\n", " \"active_weight\": active_weight,\n", " **extra_metrics,\n", " }\n", " self._store_ftpo_metrics(metrics, train_eval=\"train\")\n", "\n", " if return_outputs:\n", " return loss, metrics\n", " return loss\n", "\n", " def _prepare_dataset(self, dataset, *args, **_):\n", " return dataset" ] }, { "cell_type": "markdown", "metadata": { "id": "T5EnsvDruzxN" }, "source": [ "## Wrap the model with PEFT\n", "\n", "We specify target modules that will be finetuned while the rest of the models weights remains frozen. FTPO uses a higher LoRA rank (`r=256`) than typical DPO (`r=8`) because it needs enough capacity to redistribute probability mass at the target position. These values match the [Antidoom default config](https://github.com/Liquid4All/antidoom/blob/main/configs/default.yaml)." ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "wAVCJ3Ig_vpG", "outputId": "907d7282-d9e7-4659-aee7-e5fd5e45166c" }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/usr/local/lib/python3.13/dist-packages/peft/tuners/tuners_utils.py:1377: UserWarning: Model has `tie_word_embeddings=True` and a tied layer is part of the adapter, but `ensure_weight_tying` is not set to True. This can lead to complications, for example when merging the adapter or converting your model to formats other than safetensors. Check the discussion here: https://github.com/huggingface/peft/issues/2777\n", " warnings.warn(msg)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "trainable params: 195,035,136 || all params: 1,365,375,744 || trainable%: 14.2844\n", "βœ… LoRA configuration applied!\n", "πŸŽ›οΈ LoRA rank: 256\n", "πŸ“Š LoRA alpha: 128\n", "🎯 Target modules: {'k_proj', 'lm_head', 'v_proj', 'out_proj', 'w3', 'in_proj', 'q_proj', 'w2', 'w1'}\n" ] } ], "source": [ "from peft import LoraConfig, get_peft_model, TaskType\n", "\n", "GLU_MODULES = [\"w1\", \"w2\", \"w3\"]\n", "MHA_MODULES = [\"q_proj\", \"k_proj\", \"v_proj\", \"out_proj\"]\n", "CONV_MODULES = [\"in_proj\", \"out_proj\"]\n", "LM_HEAD = [\"lm_head\"]\n", "\n", "lora_config = LoraConfig(\n", " task_type=TaskType.CAUSAL_LM,\n", " inference_mode=False,\n", " r=256,\n", " lora_alpha=128,\n", " lora_dropout=0.0,\n", " target_modules=GLU_MODULES + MHA_MODULES + CONV_MODULES + LM_HEAD,\n", " bias=\"none\",\n", ")\n", "\n", "lora_model = get_peft_model(model, lora_config)\n", "lora_model.print_trainable_parameters()\n", "\n", "print(\"βœ… LoRA configuration applied!\")\n", "print(f\"πŸŽ›οΈ LoRA rank: {lora_config.r}\")\n", "print(f\"πŸ“Š LoRA alpha: {lora_config.lora_alpha}\")\n", "print(f\"🎯 Target modules: {lora_config.target_modules}\")" ] }, { "cell_type": "markdown", "metadata": { "id": "G6uoktUT_vpG" }, "source": [ "## Launch Training\n", "\n", "We are now ready to launch an FTPO run with `FTPOTrainer`, feel free to modify `DPOConfig` to play around with different configurations.\n", "\n", "Compared to the DPO notebook defaults, FTPO uses a larger batch (`4 Γ— 4` gradient accumulation) and higher learning rate (`1.5e-5`) because each example trains only a single token position β€” the gradients are less noisy, so we can afford larger steps. These values come from the [Antidoom default config](https://github.com/Liquid4All/antidoom/blob/main/configs/default.yaml)." ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 671 }, "id": "NLEfQRUj_vpG", "outputId": "da55ef75-064f-4b08-94eb-8176ccd81d98" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "πŸ—οΈ Creating FTPO trainer...\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/usr/local/lib/python3.13/dist-packages/peft/tuners/tuners_utils.py:1377: UserWarning: Model has `tie_word_embeddings=True` and a tied layer is part of the adapter, but `ensure_weight_tying` is not set to True. This can lead to complications, for example when merging the adapter or converting your model to formats other than safetensors. Check the discussion here: https://github.com/huggingface/peft/issues/2777\n", " warnings.warn(msg)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "πŸš€ Starting FTPO training...\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "[transformers] `causal_conv1d_fn` is falling back to its reference PyTorch implementation because `causal_conv1d` is not installed. This is correct but much slower; install `causal_conv1d` for the optimized kernel.\n" ] }, { "data": { "text/html": [ "\n", "
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StepTraining LossValidation Loss
255.3561064.537789
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2822.9122532.258944

" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stderr", "output_type": "stream", "text": [ "/usr/local/lib/python3.13/dist-packages/peft/utils/save_and_load.py:422: UserWarning: Setting `save_embedding_layers` to `True` as embedding layers found in `target_modules`.\n", " warnings.warn(\"Setting `save_embedding_layers` to `True` as embedding layers found in `target_modules`.\")\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "πŸŽ‰ FTPO training complete!\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/usr/local/lib/python3.13/dist-packages/peft/utils/save_and_load.py:422: UserWarning: Setting `save_embedding_layers` to `True` as embedding layers found in `target_modules`.\n", " warnings.warn(\"Setting `save_embedding_layers` to `True` as embedding layers found in `target_modules`.\")\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "πŸ’Ύ FTPO adapter saved to: ./lfm2-antidoom\n" ] } ], "source": [ "from trl import DPOConfig\n", "\n", "ftpo_config = DPOConfig(\n", " output_dir=\"./lfm2-antidoom\",\n", " num_train_epochs=1,\n", " per_device_train_batch_size=4,\n", " gradient_accumulation_steps=4,\n", " learning_rate=1.5e-5,\n", " lr_scheduler_type=\"linear\",\n", " # warmup_ratio is not supported by DPOConfig\n", " weight_decay=0.01,\n", " logging_steps=1,\n", " eval_strategy=\"steps\",\n", " eval_steps=25,\n", " save_strategy=\"epoch\",\n", " bf16=torch.cuda.is_available() and torch.cuda.is_bf16_supported(),\n", " report_to=\"none\",\n", " remove_unused_columns=False,\n", ")\n", "\n", "print(\"πŸ—οΈ Creating FTPO trainer...\")\n", "ftpo_trainer = FTPOTrainer(\n", " model=lora_model,\n", " args=ftpo_config,\n", " train_dataset=train_dataset_ftpo,\n", " eval_dataset=eval_dataset_ftpo,\n", " processing_class=tokenizer,\n", " ftpo_max_length=max_seq_length,\n", ")\n", "\n", "print(\"\\nπŸš€ Starting FTPO training...\")\n", "ftpo_trainer.train()\n", "\n", "print(\"πŸŽ‰ FTPO training complete!\")\n", "\n", "ftpo_trainer.save_model()\n", "print(f\"πŸ’Ύ FTPO adapter saved to: {ftpo_config.output_dir}\")" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 407 }, "id": "GRw47KO8uzxN", "outputId": "afff4dc6-4378-42cf-d2d6-e380240692e2" }, "outputs": [ { "data": { "image/png": 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"text/plain": [ "

" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import matplotlib.pyplot as plt\n", "\n", "log = ftpo_trainer.state.log_history\n", "\n", "train_steps = [e[\"step\"] for e in log if \"loss\" in e]\n", "train_loss = [e[\"loss\"] for e in log if \"loss\" in e]\n", "eval_steps = [e[\"step\"] for e in log if \"eval_loss\" in e]\n", "eval_loss = [e[\"eval_loss\"] for e in log if \"eval_loss\" in e]\n", "\n", "fig, ax = plt.subplots(figsize=(6, 4))\n", "\n", "ax.plot(train_steps, train_loss, label=\"Train loss\", marker=\"o\", markersize=3)\n", "if eval_loss:\n", " ax.plot(eval_steps, eval_loss, label=\"Eval loss\", marker=\"s\", markersize=3)\n", "ax.set_xlabel(\"Step\")\n", "ax.set_ylabel(\"Loss\")\n", "ax.set_title(\"FTPO Loss\")\n", "ax.legend()\n", "ax.grid(True, alpha=0.3)\n", "\n", "plt.tight_layout()\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": { "id": "Y7adkuzxk-VS" }, "source": [ "## Evaluate\n", "\n", "Re-run generation on a few eval prompts and compare the base model (LoRA disabled) vs. the FTPO-trained model (LoRA enabled). The base model should doom-loop on most prompts, while the trained model should break out of doom loops." ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 277, "referenced_widgets": [ "b8a28abd72ad49a686ba66bfb4389c02", "01e3c7a3076b4b139d1a15df69b5ca39", "6d1025adc9f84508a0f74023cec77149", "98a2cfbd03b4479e88f49b8ee936ed6e", "e053f680c26045aba19a1d85168a1d34", "508c5d7d6ad44bcb91ff79acaabbc22c", "8a2ba840e8cb4da5947598ae35769c1a", "97ddf0d03e8f427997ea4e23ba1566f7", "ba3fd911c41147b3b421992ed3a6c413", "53908bd35fbe4c87a3735a4fedb7c328", "9203174b07d7494dab98394585889cab", "bf3310590eaa40ea86e40f5427557761", "d33fc719b66348cb82127b48c1c92fa9", "4d7f3d024ff643a7893fee1dbe39fb14", "3876318e03de462ebc21d072d8c1ef34", "15b5295e57334ec9be47528ac3ce3b1a", "1afe93eadd1a479f970da00702984475", "1021fb13b2314804a9f72196dcd6ecb0", "22b34a1dfc034cbb8479639b1bfcfcd0", "7bc64043b41e40149388d3b26ffa077a", "25ca7275511d44b3b8761b5837057b96", "79fa46e9cf834e768bcf3760a79c16ac" ] }, "id": "X5GqgXU9k-VS", "outputId": "fde90ce1-7986-4b5c-8f23-1be1baa223f4" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "πŸ” Generating with base model (LoRA disabled)...\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "b8a28abd72ad49a686ba66bfb4389c02", "version_major": 2, "version_minor": 0 }, "text/plain": [ "Base model: 0%| | 0/3 [00:00" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "from tqdm.auto import tqdm\n", "from IPython.display import Markdown, display\n", "\n", "# Before/after comparison\n", "NUM_EVAL = 3\n", "eval_prompts = [row[\"full_prompt\"] for row in eval_dataset_ftpo.select(range(NUM_EVAL))]\n", "\n", "# Pass 1: Base model (disable LoRA adapter)\n", "print(\"πŸ” Generating with base model (LoRA disabled)...\")\n", "before_results = []\n", "lora_model.disable_adapter_layers()\n", "for prompt in tqdm(eval_prompts, desc=\"Base model\"):\n", " input_ids = tokenizer(prompt, return_tensors=\"pt\").input_ids.to(lora_model.device)\n", " with torch.no_grad():\n", " output = lora_model.generate(input_ids, max_new_tokens=4000, do_sample=False)\n", " text = tokenizer.decode(output[0][input_ids.shape[1]:], skip_special_tokens=True)\n", " found, hit = find_inner_repetition(text)\n", " before_results.append({\"text\": text, \"doom\": found, \"snippet\": hit.snippet if found else None})\n", "lora_model.enable_adapter_layers()\n", "\n", "# Pass 2: FTPO-trained model (LoRA enabled)\n", "print(\"πŸ” Generating with FTPO-trained model...\")\n", "after_results = []\n", "for prompt in tqdm(eval_prompts, desc=\"FTPO model\"):\n", " input_ids = tokenizer(prompt, return_tensors=\"pt\").input_ids.to(lora_model.device)\n", " with torch.no_grad():\n", " output = lora_model.generate(input_ids, max_new_tokens=4000, do_sample=False)\n", " text = tokenizer.decode(output[0][input_ids.shape[1]:], skip_special_tokens=True)\n", " found, hit = find_inner_repetition(text)\n", " after_results.append({\"text\": text, \"doom\": found, \"snippet\": hit.snippet if found else None})\n", "\n", "# Summary table\n", "before_doom = sum(r[\"doom\"] for r in before_results)\n", "after_doom = sum(r[\"doom\"] for r in after_results)\n", "\n", "rows = []\n", "for i in range(NUM_EVAL):\n", " prompt_preview = eval_prompts[i][:80].replace(\"\\n\", \" \").strip() + \"...\"\n", " b = \"doom loop\" if before_results[i][\"doom\"] else \"clean\"\n", " a = \"doom loop\" if after_results[i][\"doom\"] else \"clean\"\n", " rows.append(f\"| {i+1} | {prompt_preview} | {b} | {a} |\")\n", "\n", "table = \"\\n\".join([\n", " f\"| # | Prompt | Before (base) | After (FTPO) |\",\n", " \"|---|--------|---------------|--------------|\",\n", "] + rows)\n", "\n", "display(Markdown(table))" ] }, { "cell_type": "markdown", "metadata": { "id": "Laz44v9yk-VS" }, "source": [ "## Save merged model\n", "\n", "Merge the extra weights learned with LoRA back into the model to obtain a \"normal\" model checkpoint." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "b1em3OCD_vpH" }, "outputs": [], "source": [ "print(\"\\nπŸ”„ Merging LoRA weights...\")\n", "merged_model = lora_model.merge_and_unload()\n", "merged_model.save_pretrained(\"./lfm2-antidoom-merged\")\n", "tokenizer.save_pretrained(\"./lfm2-antidoom-merged\")\n", "print(\"πŸ’Ύ Merged model saved to: ./lfm2-antidoom-merged\")\n", "\n", "# To push the merged model to the Hugging Face Hub, uncomment:\n", "# merged_model.push_to_hub(\"your-model-path\")\n", "# tokenizer.push_to_hub(\"your-model-path\")" ] }, { "cell_type": "markdown", "metadata": { "id": "uYlSMV6FuzxO" }, "source": [ "## πŸ“– References:\n", "- [Antidoom blog post](https://liquid.ai/blog/antidoom)\n", "- [Antidoom GitHub repo](https://github.com/Liquid4All/antidoom)\n", "- [FTPO / Antislop paper](https://huggingface.co/papers/2510.15061)\n", "- [TRL: Change the training objective](https://huggingface.co/docs/trl/main/en/customization#change-the-training-objective)" ] } ], "metadata": { "_cache_bust": "v2", "_touch": true, "_touch2": true, "accelerator": "GPU", "colab": { "gpuType": "A100", "provenance": [] }, "kernelspec": { "display_name": ".venv", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.14.6" }, "widgets": { "application/vnd.jupyter.widget-state+json": { "state": { "013822d2e7e1430a825a7a18bf4b4774": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "model_name": "DescriptionStyleModel", "state": { "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "DescriptionStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", 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