messages list | tier int64 | src string |
|---|---|---|
[
{
"role": "user",
"content": "I can see lines moving, its even more gitchy. Fully replicate the anthropic effect.\nAssistant action: WebFetch {\"url\": \"https://news.ycombinator.com/item?id=47427727\", \"prompt\": \"What does this comment thread say about how Anthropic implements their wiggling hand-drawn ... | 1 | Swarm-AI-Research/fable5-traces-sft |
[
{
"role": "user",
"content": "I can see lines moving, its even more gitchy. Fully replicate the anthropic effect.\nAssistant action: WebFetch {\"url\": \"https://news.ycombinator.com/item?id=47427727\", \"prompt\": \"What does this comment thread say about how Anthropic implements their wiggling hand-drawn ... | 1 | Swarm-AI-Research/fable5-traces-sft |
[
{
"role": "user",
"content": "I still see some parts what are way out of the avarage line, it should do this. Take a single line -> slightly stretch some parts out -> make some parts thinner. it should know about the past iteratrions so it can look natral\nAssistant: What you're describing is a bounded rand... | 1 | Swarm-AI-Research/fable5-traces-sft |
[
{
"role": "user",
"content": "its laggy and way top jittery. its not as smooth as I would like it.\nits laggy and way too jittery. its not as smooth as I would like it.\nAssistant: Two fixes: smooth tweened motion instead of hard 100ms jumps, and cheaper filter (1 octave, capped update rate) so it doesn't l... | 1 | Swarm-AI-Research/fable5-traces-sft |
[
{
"role": "user",
"content": "no, like keep the randomnes but make the lines thicker and require the ones next to it to like smoothly transition\nAssistant: Got it: random frame jumps stay, but the noise gets much lower frequency and one octave, so displacement varies smoothly along a stroke (neighbors mov... | 1 | Swarm-AI-Research/fable5-traces-sft |
[
{
"role": "user",
"content": "I want you to read agents.md, then follow this experment. THe STM paper is pasted below, use it. I want you to run a 2B model training run on 10% of fineweb-edu (python3 train.py 2 100), but use STM on the ordinary version of the model (non SLERP, latest checkpoint) and then do... | 1 | Swarm-AI-Research/fable5-traces-sft |
[
{
"role": "user",
"content": "I want you to read agents.md, then follow this experment. THe STM paper is pasted below, use it. I want you to run a 2B model training run on 10% of fineweb-edu (python3 train.py 2 100), but use STM on the ordinary version of the model (non SLERP, latest checkpoint) and then do... | 1 | Swarm-AI-Research/fable5-traces-sft |
[
{
"role": "user",
"content": "I want you to read agents.md, then follow this experment. THe STM paper is pasted below, use it. I want you to run a 2B model training run on 10% of fineweb-edu (python3 train.py 2 100), but use STM on the ordinary version of the model (non SLERP, latest checkpoint) and then do... | 1 | Swarm-AI-Research/fable5-traces-sft |
[
{
"role": "user",
"content": "I want you to read agents.md, then follow this experment. THe STM paper is pasted below, use it. I want you to run a 2B model training run on 10% of fineweb-edu (python3 train.py 2 100), but use STM on the ordinary version of the model (non SLERP, latest checkpoint) and then do... | 1 | Swarm-AI-Research/fable5-traces-sft |
[
{
"role": "user",
"content": "I want you to read agents.md, then follow this experment. THe STM paper is pasted below, use it. I want you to run a 2B model training run on 10% of fineweb-edu (python3 train.py 2 100), but use STM on the ordinary version of the model (non SLERP, latest checkpoint) and then do... | 1 | Swarm-AI-Research/fable5-traces-sft |
[
{
"role": "user",
"content": "I want you to read agents.md, then follow this experment. THe STM paper is pasted below, use it. I want you to run a 2B model training run on 10% of fineweb-edu \n (python3 train.py 2 100), but use STM on the ordinary version of the model (non SLERP, latest checkpoint) a... | 1 | Swarm-AI-Research/fable5-traces-sft |
[
{
"role": "user",
"content": "…[earlier truncated]…\nResult: AGENTS.md\ndocs\njournal.md\nMakefile\nplan.md\npyaot.py\n__pycache__\npyproject.toml\nralph\nralph.py\nREADME.md\nrepro_data.py\nrequirements.txt\nresearch.md\nreslerp.py\nruns\nscores.md\nscripts\nsrc\ntest_filter.py\ntest_hf.py\ntests\ntest_wor... | 1 | Swarm-AI-Research/fable5-traces-sft |
[
{
"role": "user",
"content": "…[earlier truncated]…\n of this contract, not optional extras.\n4\t\n5\t## Commands\n6\t\n7\t```bash\n8\tmake setup # create .venv and install pinned dependencies (idempotent)\n9\tmake test # run the full test suite: pytest tests/ -v\n10\tmake lint # ruff format --ch... | 1 | Swarm-AI-Research/fable5-traces-sft |
[
{
"role": "user",
"content": "…[earlier truncated]…\nB model training run on 10% of fineweb-edu \n (python3 train.py 2 100), but use STM on the ordinary version of the model (non SLERP, latest checkpoint) and then do slerp. Log the place on leaderboard. then to STM \n on the best SLERP, then slerp o... | 1 | Swarm-AI-Research/fable5-traces-sft |
[
{
"role": "user",
"content": "…[earlier truncated]…\nrion isn't 'loss is currently high on this sample.' It's 'loss DROPPED a lot on this sample.'\" A sample showing gradual loss improvement is retained despite potentially higher absolute loss.\n\n## Critical Hyperp …[truncated]\nAssistant: Read journal and... | 1 | Swarm-AI-Research/fable5-traces-sft |
[
{
"role": "user",
"content": "Continue from where you left off.\nAssistant: No response requested.\nContinue"
},
{
"role": "assistant",
"content": "<think>\nAlright, the user wants me to continue extracting the STM paper details. I already fetched the HTML, stripped it of markup, and saved the c... | 1 | Swarm-AI-Research/fable5-traces-sft |
[
{
"role": "user",
"content": "…[earlier truncated]…\nT: <system-reminder>[Truncated: PARTIAL view — showing lines 1-341 of 433 total (26924 tokens, cap 25000). Call Read with offset=342 limit=341 for the next page, or Grep to find a specific section. Do NOT answer from this page alone if the answer may be f... | 1 | Swarm-AI-Research/fable5-traces-sft |
[
{
"role": "user",
"content": "…[earlier truncated]…\nltiple epochs with a shrinking dataset.\n\n## Algorithm Overview\n\n**Per-epoch procedure:**\n1. Train model on current dataset for one epoch\n2. Identify samples where loss decreased most dramatically\n3. Remove those samples from the dataset\n4. Proceed... | 1 | Swarm-AI-Research/fable5-traces-sft |
[
{
"role": "user",
"content": "I want you to read agents.md, then follow this experment. THe STM paper is pasted below, use it. I want you to run a 2B model training run on 10% of fineweb-edu (python3 train.py 2 100), but use STM on the ordinary version of the model (non SLERP, latest checkpoint) and then do... | 1 | Swarm-AI-Research/fable5-traces-sft |
[
{
"role": "user",
"content": "Continue from where you left off.\nAssistant: No response requested.\nContinue\nAssistant action: Read {\"file_path\": \"/tmp/stm_paper.txt\"}\nResult: 1\t\n2\t\n3\t\n4\t \n5\t \n6\t STM and the Circle Thing\n7\t \n8\t \n9\t \n10\t \n11\t\n12\t\n13\t \n14\t \n15\t ... | 1 | Swarm-AI-Research/fable5-traces-sft |
[
{
"role": "user",
"content": "Continue from where you left off.\nAssistant: No response requested.\nContinue\nAssistant action: Read {\"file_path\": \"/tmp/stm_paper.txt\"}\nResult: 1\t\n2\t\n3\t\n4\t \n5\t \n6\t STM and the Circle Thing\n7\t \n8\t \n9\t \n10\t \n11\t\n12\t\n13\t \n14\t \n15\t ... | 1 | Swarm-AI-Research/fable5-traces-sft |
[
{
"role": "user",
"content": "Continue from where you left off.\nAssistant: No response requested.\nContinue\nAssistant action: Read {\"file_path\": \"/tmp/stm_paper.txt\"}\nResult: 1\t\n2\t\n3\t\n4\t \n5\t \n6\t STM and the Circle Thing\n7\t \n8\t \n9\t \n10\t \n11\t\n12\t\n13\t \n14\t \n15\t ... | 1 | Swarm-AI-Research/fable5-traces-sft |
[
{
"role": "user",
"content": "Continue from where you left off.\nAssistant: No response requested.\nContinue\nAssistant action: Read {\"file_path\": \"/tmp/stm_paper.txt\"}\nResult: 1\t\n2\t\n3\t\n4\t \n5\t \n6\t STM and the Circle Thing\n7\t \n8\t \n9\t \n10\t \n11\t\n12\t\n13\t \n14\t \n15\t ... | 1 | Swarm-AI-Research/fable5-traces-sft |
[
{
"role": "user",
"content": "…[earlier truncated]…\n4022/ | ckpt: | merges: \nruns/20260610_144411/ | ckpt: step_00000247.pt | merges: champion.pt \nruns/20260610_151011/ | ckpt: | merges: \nruns/20260610_155044/ | ckpt: | merges: \nruns/20260610_155221/ | ckpt: | merges: \nruns/20260610_161128/ | ckpt: st... | 1 | Swarm-AI-Research/fable5-traces-sft |
[
{
"role": "user",
"content": "I still see some parts what are way out of the avarage line, it should do this. Take a single line -> slightly stretch some parts out -> make some parts thinner. it should know about the past iteratrions so it can look natral\nAssistant: What you're describing is a bounded rand... | 1 | Swarm-AI-Research/fable5-traces-sft |
[
{
"role": "user",
"content": "…[earlier truncated]…\nss drops, do it again. STM says nah. After that first pass, look at which samples caused the biggest sudden drops in loss, and yank those out. Keep the rest. Train again. Repeat.\n20\tThe reasoning: a sample that produces a huge plummet in loss is the mod... | 1 | Swarm-AI-Research/fable5-traces-sft |
[
{
"role": "user",
"content": "…[earlier truncated]…\neb-edu\"\n15\tFINEWEB_CONFIG = \"sample-100BT\"\n16\tDEFAULT_QUALITY_PERCENT = 10.0\n17\t\n18\tFINEWEB_SCORE_DISTRIBUTION = {\n19\t 5: 0.12,\n20\t 4: 0.28,\n21\t 3: 0.60,\n22\t}\n23\t\n24\tBLIMP_DATASET = \"nyu-mll/blimp\"\n25\tARC_DATASET = \"al... | 1 | Swarm-AI-Research/fable5-traces-sft |
[
{
"role": "user",
"content": "…[earlier truncated]…\nbooleans) get emitted as strings and the client-side parser rejects them. Load the tool first: call ToolSearch with query \"select:TaskCreate\", then retry this call. For reference, this tool's input schema is: {\"$schema\":\"https://json-schema.org/draft... | 1 | Swarm-AI-Research/fable5-traces-sft |
[
{
"role": "user",
"content": "…[earlier truncated]…\nBatchPlan, shrink_after_oom\n19\tfrom src.mythosmini.hardware.detect import DeviceInfo, autocast_dtype\n20\tfrom src.mythosmini.model.transformer import MythosMini\n21\tfrom src.mythosmini.telemetry.progress import emit_log_line\n22\tfrom src.mythosmini.t... | 1 | Swarm-AI-Research/fable5-traces-sft |
[
{
"role": "user",
"content": "Continue from where you left off.\nAssistant: No response requested.\nContinue\nAssistant action: Read {\"file_path\": \"/tmp/stm_paper.txt\"}\nResult: 1\t\n2\t\n3\t\n4\t \n5\t \n6\t STM and the Circle Thing\n7\t \n8\t \n9\t \n10\t \n11\t\n12\t\n13\t \n14\t \n15\t ... | 1 | Swarm-AI-Research/fable5-traces-sft |
[
{
"role": "user",
"content": "…[earlier truncated]…\nchema\",\"t …[truncated]\nAssistant action: TaskCreate {\"prompt\": \"Experiment 1: STM on step_00007630 (ordinary latest checkpoint), then SLERP tournament; log leaderboard placement\"}\nResult: <tool_use_error>InputValidationError: TaskCreate failed due... | 1 | Swarm-AI-Research/fable5-traces-sft |
[
{
"role": "user",
"content": "…[earlier truncated]…\nin your prompt, typed parameters (arrays, numbers, booleans) get emitted as strings and the client-side parser rejects them. Load the tool first: call ToolSearch with query \"select:TaskCreate\", then retry this call. For reference, this tool's input sche... | 1 | Swarm-AI-Research/fable5-traces-sft |
[
{
"role": "user",
"content": "…[earlier truncated]…\n:\ntraining.jsonl\n{\"step\": 2000, \"tokens\": 262144000, \"loss\": 1.2188239097595215, \"lr\": 0.003, \"micro_batch\": 256, \"grad_accum\": 1, \"checkpoint\": \"step_00002000.pt\"}\n{\"step\": 4000, \"tokens\": 524288000, \"loss\": 1.1074821949005127, \... | 1 | Swarm-AI-Research/fable5-traces-sft |
[
{
"role": "user",
"content": "…[earlier truncated]…\ntionError: TaskCreate failed due to the following issues:\nThe required parameter `subject` is missing\nThe required parameter `description` is missing\nAn unexpected parameter `prompt` was provided\n\nThis call used Agent-tool parameters (`prompt`/`subag... | 1 | Swarm-AI-Research/fable5-traces-sft |
[
{
"role": "user",
"content": "…[earlier truncated]…\ne history. Without the schema in your prompt, typed parameters (arrays, numbers, booleans) get emitted as strings and the client-side parser rejects them. Load the tool first: call ToolSearch with query \"select:TaskCreate\", then retry this call. For ref... | 1 | Swarm-AI-Research/fable5-traces-sft |
[
{
"role": "user",
"content": "…[earlier truncated]…\nema in your prompt, typed parameters (arrays, numbers, booleans) get emitted as strings and the client-side parser rejects them. Load the tool first: call ToolSearch with query \"select:TaskCreate\", then retry this call. For reference, this tool's input ... | 1 | Swarm-AI-Research/fable5-traces-sft |
[
{
"role": "user",
"content": "…[earlier truncated]…\nrt (\\n StmConfig,\\n per_sample_loss,\\n run_stm,\\n select_survivors,\\n stm_epoch,\\n)\\n\\n\\ndef tiny_model() -> MythosMini:\\n config = ModelConfig(\\n dim=16,\\n n_heads=2,\\n prelude_layers=1,\\n coda_... | 1 | Swarm-AI-Research/fable5-traces-sft |
[
{
"role": "user",
"content": "…[earlier truncated]…\nnt.py:16:class MergeCandidate:\nsrc/mythosmini/merging/tournament.py:26:def improves_any_category(child: BenchmarkScores, best: BenchmarkScores) -> bool:\nsrc/mythosmini/merging/tournament.py:30:def category_best(candidates: list[MergeCandidate]) -> Bench... | 1 | Swarm-AI-Research/fable5-traces-sft |
[
{
"role": "user",
"content": "…[earlier truncated]…\n164\ndrwxrwxr-x 2 lane lane 4096 Jun 10 19:45 .\ndrwxrwxr-x 16 lane lane 4096 Jun 10 20:19 ..\n-rw-rw-r-- 1 lane lane 6031638 Jun 10 20:05 fineweb_q0.040000_n64000000.tmp\n-rw-rw-r-- 1 lane lane 128000000 Jun 10 19:45 fineweb_q0.100000_n640... | 1 | Swarm-AI-Research/fable5-traces-sft |
[
{
"role": "user",
"content": "…[earlier truncated]…\ne lane 262144 Jun 10 20:22 synthetic.u16\nmemory.used [MiB], memory.total [MiB]\n776 MiB, 32607 MiB\nAssistant: Cache and GPU ready. Write the experiment entry script `stm.py`:\nAssistant action: Write {\"file_path\": \"/home/user/MythosMini/stm.py\", ... | 1 | Swarm-AI-Research/fable5-traces-sft |
[
{
"role": "user",
"content": "…[earlier truncated]…\n.cache import cache_path_for\\nfrom src.mythosmini.hardware.detect import autocast_dtype, describe_device, detect_device\\nfrom src.mythosmini.leaderboard.fetch import fetch_leaderboard\\nfrom src.mythosmini.model.transformer import MythosMini\\nfrom src.... | 1 | Swarm-AI-Research/fable5-traces-sft |
[
{
"role": "user",
"content": "…[earlier truncated]…\nITY_PERCENT = 1.0\\nSTM_STEP_STRIDE = 100\\nFALLBACK_BASE_STEP = 9000\\n\\n\\ndef parse_args(argv: lis …[truncated]\nResult: File created successfully at: /home/user/MythosMini/stm.py (file state is current in your context — no need to Read it back)\nAssi... | 1 | Swarm-AI-Research/fable5-traces-sft |
[
{
"role": "user",
"content": "…[earlier truncated]…\n[truncated]\nResult: File created successfully at: /home/user/MythosMini/stm.py (file state is current in your context — no need to Read it back)\nAssistant action: Bash {\"command\": \"make lint 2>&1 | tail -8 && .venv/bin/python -m pytest tests/ -q 2>&1... | 1 | Swarm-AI-Research/fable5-traces-sft |
[
{
"role": "user",
"content": "…[earlier truncated]…\nash input={\"command\": \"make fix 2>&1 | tail -2 && make lint 2>&1 | tail -4\", \"description\": \"Fix imports and relint\"}\nResult: .venv/bin/ruff check --fix src/ tests/ ralph.py train.py\nFound 1 error (1 fixed, 0 remaining).\n src/mythosmini/traini... | 1 | Swarm-AI-Research/fable5-traces-sft |
[
{
"role": "user",
"content": "<task-notification>\n<task-id>bi08vsm7e</task-id>\n<summary>Monitor event: \"STM exp1 progress and failures\"</summary>\n<event>gen2_gen1_gen0_step_00008230+step_00006000@t0.20+gen0_step_00008230+step_00008130@t0.20@t0.35+gen1_gen0_step_00008230+step_00006000@t0.20+gen0_step_00... | 1 | Swarm-AI-Research/fable5-traces-sft |
[
{
"role": "user",
"content": "<task-notification>\n<task-id>bi08vsm7e</task-id>\n<summary>Monitor event: \"STM exp1 progress and failures\"</summary>\n<event>gen2_gen1_gen0_step_00008230+step_00006000@t0.20+gen0_step_00008230+step_00008130@t0.20@t0.35+gen1_gen0_step_00008230+step_00006000@t0.20+gen0_step_00... | 1 | Swarm-AI-Research/fable5-traces-sft |
[
{
"role": "user",
"content": "<task-notification>\n<task-id>bi08vsm7e</task-id>\n<summary>Monitor event: \"STM exp1 progress and failures\"</summary>\n<event>gen2_gen1_gen0_step_00008230+step_00008130@t0.20+gen0_step_00008230+step_00008130@t0.50@t0.65+gen1_gen0_step_00008230+step_00008130@t0.50+gen0_step_00... | 1 | Swarm-AI-Research/fable5-traces-sft |
[
{
"role": "user",
"content": "<task-notification>\n<task-id>bi08vsm7e</task-id>\n<summary>Monitor event: \"STM exp1 progress and failures\"</summary>\n<event>gen3_gen2_gen1_gen0_step_00008230+step_00006000@t0.20+gen0_step_00008230+step_00008130@t0.20@t0.35+gen1_gen0_step_00008230+step_00008130@t0.20+gen0_st... | 1 | Swarm-AI-Research/fable5-traces-sft |
[
{
"role": "user",
"content": "…[earlier truncated]…\n byte_tokenize\n20\t\n21\tCACHE_DTYPE = np.uint16\n22\t\n23\t\n24\tclass _TokenQueue(Protocol):\n25\t def get(self) -> list[int] | None: ...\n26\t\n27\t\n28\tdef cache_path_for(quality_percent: float, target_tokens: int, cache_dir: Path) -> Path:\n29\t... | 1 | Swarm-AI-Research/fable5-traces-sft |
[
{
"role": "user",
"content": "<task-notification>\n<task-id>bfa97quu7</task-id>\n<tool-use-id>toolu_01GDfhev7z8sUToGJgpR1KRC</tool-use-id>\n<output-file>/tmp/claude-1000/-home-lane-MythosMini/e45c1355-dff7-4e8e-a94d-fb378adb9a74/tasks/bfa97quu7.output</output-file>\n<status>completed</status>\n<summary>Back... | 1 | Swarm-AI-Research/fable5-traces-sft |
[
{
"role": "user",
"content": "<task-notification>\n<task-id>bfa97quu7</task-id>\n<tool-use-id>toolu_01GDfhev7z8sUToGJgpR1KRC</tool-use-id>\n<output-file>/tmp/claude-1000/-home-lane-MythosMini/e45c1355-dff7-4e8e-a94d-fb378adb9a74/tasks/bfa97quu7.output</output-file>\n<status>completed</status>\n<summary>Back... | 1 | Swarm-AI-Research/fable5-traces-sft |
[
{
"role": "user",
"content": "<task-notification>\n<task-id>b7pkdavdf</task-id>\n<tool-use-id>toolu_01Xm4KK1tfRMWxZhxRhq3A4r</tool-use-id>\n<output-file>/tmp/claude-1000/-home-lane-MythosMini/e45c1355-dff7-4e8e-a94d-fb378adb9a74/tasks/b7pkdavdf.output</output-file>\n<status>completed</status>\n<summary>Back... | 1 | Swarm-AI-Research/fable5-traces-sft |
[
{
"role": "user",
"content": "<task-notification>\n<task-id>bfa97quu7</task-id>\n<tool-use-id>toolu_01GDfhev7z8sUToGJgpR1KRC</tool-use-id>\n<output-file>/tmp/claude-1000/-home-lane-MythosMini/e45c1355-dff7-4e8e-a94d-fb378adb9a74/tasks/bfa97quu7.output</output-file>\n<status>completed</status>\n<summary>Back... | 1 | Swarm-AI-Research/fable5-traces-sft |
[
{
"role": "user",
"content": "WHat if you do multiple STM rounds instead of one? Please run more tests and find how much and to what extent it helps. Use the paper for refrence.\nAssistant: Paper's recursive STM: after a full pass, reset the pool to everything, tighten the mastery bar, run again — gains big... | 1 | Swarm-AI-Research/fable5-traces-sft |
[
{
"role": "user",
"content": "<task-notification>\n<task-id>bi08vsm7e</task-id>\n<summary>Monitor event: \"STM exp1 progress and failures\"</summary>\n<event>gen2_gen1_gen0_step_00008230+step_00006000@t0.20+gen0_step_00008230+step_00008130@t0.35@t0.20+gen0_step_00008130+step_00006000@t0.80@t0.50: wiki_ppl 5... | 1 | Swarm-AI-Research/fable5-traces-sft |
[
{
"role": "user",
"content": "WHat if you do multiple STM rounds instead of one? Please run more tests and find how much and to what extent it helps. Use the paper for refrence.\nAssistant: Paper's recursive STM: after a full pass, reset the pool to everything, tighten the mastery bar, run again — gains big... | 1 | Swarm-AI-Research/fable5-traces-sft |
[
{
"role": "user",
"content": "WHat if you do multiple STM rounds instead of one? Please run more tests and find how much and to what extent it helps. Use the paper for refrence.\nAssistant: Paper's recursive STM: after a full pass, reset the pool to everything, tighten the mastery bar, run again — gains big... | 1 | Swarm-AI-Research/fable5-traces-sft |
[
{
"role": "user",
"content": "WHat if you do multiple STM rounds instead of one? Please run more tests and find how much and to what extent it helps. Use the paper for refrence.\nAssistant: Paper's recursive STM: after a full pass, reset the pool to everything, tighten the mastery bar, run again — gains big... | 1 | Swarm-AI-Research/fable5-traces-sft |
[
{
"role": "user",
"content": "…[earlier truncated]…\n{\"replace_all\": false, \"file_path\": \"/home/user/MythosMini/src/mythosmini/training/stm.py\", \"old_string\": \" grad_clip: float = 1.0\\n seed: int = 1337\", \"new_string\": \" grad_clip: float = 1.0\\n seed: int = 1337\\n floor_tighte... | 1 | Swarm-AI-Research/fable5-traces-sft |
[
{
"role": "user",
"content": "WHat if you do multiple STM rounds instead of one? Please run more tests and find how much and to what extent it helps. Use the paper for refrence.\nAssistant: Paper's recursive STM: after a full pass, reset the pool to everything, tighten the mastery bar, run again — gains big... | 1 | Swarm-AI-Research/fable5-traces-sft |
[
{
"role": "user",
"content": "<task-notification>\n<task-id>b57c4fsf1</task-id>\n<tool-use-id>toolu_01791FHTTaR5qeXgiGn9DaZV</tool-use-id>\n<output-file>/tmp/claude-1000/-home-lane-MythosMini/e45c1355-dff7-4e8e-a94d-fb378adb9a74/tasks/b57c4fsf1.output</output-file>\n<status>completed</status>\n<summary>Back... | 1 | Swarm-AI-Research/fable5-traces-sft |
[
{
"role": "user",
"content": "<task-notification>\n<task-id>b57c4fsf1</task-id>\n<tool-use-id>toolu_01791FHTTaR5qeXgiGn9DaZV</tool-use-id>\n<output-file>/tmp/claude-1000/-home-lane-MythosMini/e45c1355-dff7-4e8e-a94d-fb378adb9a74/tasks/b57c4fsf1.output</output-file>\n<status>completed</status>\n<summary>Back... | 1 | Swarm-AI-Research/fable5-traces-sft |
[
{
"role": "user",
"content": "<task-notification>\n<task-id>b57c4fsf1</task-id>\n<tool-use-id>toolu_01791FHTTaR5qeXgiGn9DaZV</tool-use-id>\n<output-file>/tmp/claude-1000/-home-lane-MythosMini/e45c1355-dff7-4e8e-a94d-fb378adb9a74/tasks/b57c4fsf1.output</output-file>\n<status>completed</status>\n<summary>Back... | 1 | Swarm-AI-Research/fable5-traces-sft |
[
{
"role": "user",
"content": "<task-notification>\n<task-id>b57c4fsf1</task-id>\n<tool-use-id>toolu_01791FHTTaR5qeXgiGn9DaZV</tool-use-id>\n<output-file>/tmp/claude-1000/-home-lane-MythosMini/e45c1355-dff7-4e8e-a94d-fb378adb9a74/tasks/b57c4fsf1.output</output-file>\n<status>completed</status>\n<summary>Back... | 1 | Swarm-AI-Research/fable5-traces-sft |
[
{
"role": "user",
"content": "Sick, now apply the combination that gives the best results to the default training pipeline.\nAssistant: Best combination: **3-round recursive STM after training, before benchmark/SLERP**. Wiring it into `train.py`. Per AGENTS.md training-change rules this needs a full 2B veri... | 1 | Swarm-AI-Research/fable5-traces-sft |
[
{
"role": "user",
"content": "<task-notification>\n<task-id>b57c4fsf1</task-id>\n<tool-use-id>toolu_01791FHTTaR5qeXgiGn9DaZV</tool-use-id>\n<output-file>/tmp/claude-1000/-home-lane-MythosMini/e45c1355-dff7-4e8e-a94d-fb378adb9a74/tasks/b57c4fsf1.output</output-file>\n<status>completed</status>\n<summary>Back... | 1 | Swarm-AI-Research/fable5-traces-sft |
[
{
"role": "user",
"content": "Sick, now apply the combination that gives the best results to the default training pipeline.\nAssistant: Best combination: **3-round recursive STM after training, before benchmark/SLERP**. Wiring it into `train.py`. Per AGENTS.md training-change rules this needs a full 2B veri... | 1 | Swarm-AI-Research/fable5-traces-sft |
[
{
"role": "user",
"content": "Sick, now apply the combination that gives the best results to the default training pipeline.\nAssistant: Best combination: **3-round recursive STM after training, before benchmark/SLERP**. Wiring it into `train.py`. Per AGENTS.md training-change rules this needs a full 2B veri... | 1 | Swarm-AI-Research/fable5-traces-sft |
[
{
"role": "user",
"content": "Sick, now apply the combination that gives the best results to the default training pipeline.\nAssistant: Best combination: **3-round recursive STM after training, before benchmark/SLERP**. Wiring it into `train.py`. Per AGENTS.md training-change rules this needs a full 2B veri... | 1 | Swarm-AI-Research/fable5-traces-sft |
[
{
"role": "user",
"content": "Sick, now apply the combination that gives the best results to the default training pipeline.\nAssistant: Best combination: **3-round recursive STM after training, before benchmark/SLERP**. Wiring it into `train.py`. Per AGENTS.md training-change rules this needs a full 2B veri... | 1 | Swarm-AI-Research/fable5-traces-sft |
[
{
"role": "user",
"content": "…[earlier truncated]…\n_all\": false, \"file_path\": \"/home/user/MythosMini/src/mythosmini/training/stm.py\", \"old_string\": \"from collections.abc import Callable\\nfrom dataclasses import dataclass, replace\\nfrom pathlib import Path\\n\\nimport numpy as np\\nimport torch\\... | 1 | Swarm-AI-Research/fable5-traces-sft |
[
{
"role": "user",
"content": "…[earlier truncated]…\nnd\": \"sed -n '8,150p' research.md\", \"description\": \"Read papers section\"}\nResult: ## Section 1: Papers (72)\n\n# TinyStories: How Small Can Language Models Be and Still Speak Coherent English?\n- https://arxiv.org/abs/2305.07759\n- Shows models un... | 1 | Swarm-AI-Research/fable5-traces-sft |
[
{
"role": "user",
"content": "…[earlier truncated]…\natic study of distillation-based post-training pipelines for SLMs — what to distill (logits, data, reasoning traces) at which stage. A recipe paper for our post-training phase.\n150:### Recent Papers (2025–2026)\n194:- Compress teacher chain-of-thought tr... | 1 | Swarm-AI-Research/fable5-traces-sft |
[
{
"role": "user",
"content": "…[earlier truncated]…\nd]\nAssistant action: Bash {\"command\": \"sed -n '150,260p' research.md\", \"description\": \"Read recent papers section\"}\nResult: ### Recent Papers (2025–2026)\n\n# IMU-1: Sample-Efficient Pre-training of Small Language Models\n- https://arxiv.org/pdf... | 1 | Swarm-AI-Research/fable5-traces-sft |
[
{
"role": "user",
"content": "…[earlier truncated]…\n# Hourglass FFN Architecture\n\nBased on the PDF content, here's the hourglass FFN design:\n\n## Layer Shapes\n\nThe hourglass FFN inverts the conventional FFN architecture. Where standard FFNs expand then contract (d → 4d → d), the hourglass contracts th... | 1 | Swarm-AI-Research/fable5-traces-sft |
[
{
"role": "user",
"content": "…[earlier truncated]…\nved context recall accuracy from 10% to 98% in needle-in-a-haystack tasks.\n\n## Warm-up Proportion\n\nThe research demonstrates that \"front-loading as little as 0.1 to 0.3% procedural data significantly outperforms standard pretraining\" on natural lang... | 1 | Swarm-AI-Research/fable5-traces-sft |
[
{
"role": "user",
"content": "…[earlier truncated]…\nproportional reduction in computational cost (FLOPs).\n\n## Mechanisms\n\nThe app …[truncated]\nResult: # Output Embedding Centering (OEC) - Summary\n\nBased on the arXiv abstract provided, here's what can be determined about OEC:\n\n## What is Centered\n... | 1 | Swarm-AI-Research/fable5-traces-sft |
[
{
"role": "user",
"content": "…[earlier truncated]…\nzation method\n\n## Timing and Frequency\nThe abstract does not specify whether centering occurs at every training step, at intervals, or at specific phases. This implementation detail is not covered in the available excerpt.\n\n## Formula\nThe exact math... | 1 | Swarm-AI-Research/fable5-traces-sft |
[
{
"role": "user",
"content": "…[earlier truncated]…\nAULT_STM_ROUNDS = 3\n126:def stm_phase(paths: RunPaths, quality_percent: float) -> None:\n131: print(f\"stm phase: {DEFAULT_STM_ROUNDS} recursive rounds from {latest.name}\")\n133: run_stm_from_checkpoint(latest, paths.checkpoints, cache_path, StmCo... | 1 | Swarm-AI-Research/fable5-traces-sft |
[
{
"role": "user",
"content": "…[earlier truncated]…\n176,10,9)]:\\n config = ModelConfig(dim=dim, n_heads=heads, ffn_hidden=ffn, prelude_layers=prelude, coda_layers=coda)\\n print(dim, heads, ffn, prelude, coda, count_parameters(MythosMini(config)))\\nprint(\\\"base\\\", count_parameters(MythosMini(Mo... | 1 | Swarm-AI-Research/fable5-traces-sft |
[
{
"role": "user",
"content": "…[earlier truncated]…\n dim: int, hidden: int) -> None:\n41: def __init__(self, config: ModelConfig) -> None:\n66: def __init__(self, config: ModelConfig) -> None:\n80: def __init__(self, config: ModelConfig, max_loops: int) -> None:\n97:class MythosMini(nn.Module):\n9... | 1 | Swarm-AI-Research/fable5-traces-sft |
[
{
"role": "user",
"content": "…[earlier truncated]…\nall) Bash input={\"command\": \".venv/bin/python train.py 2 > /tmp/r0_baseline.log 2>&1; echo \\\"exit: $?\\\"\", \"description\": \"R0: baseline 2B at 10% quality, full pipeline\", \"run_in_background\": true}\nResult: Command running in background with ... | 1 | Swarm-AI-Research/fable5-traces-sft |
[
{
"role": "user",
"content": "…[earlier truncated]…\nt={\"replace_all\": false, \"file_path\": \"/home/user/MythosMini/src/mythosmini/training/loop.py\", \"old_string\": \" torch.nn.utils.clip_grad_norm_(deps.model.parameters(), config.grad_clip)\\n optimizer.step()\\n latest_loss = mea... | 1 | Swarm-AI-Research/fable5-traces-sft |
[
{
"role": "user",
"content": "…[earlier truncated]…\nbool:\n if int_score == 5:\n threshold = percent / 0.12\n elif int_score == 4:\n64 4 176 8 8 888576\n64 4 192 …[truncated]\nAssistant action: Write {\"file_path\": \"/home/user/MythosMini/tests/test_procedural.py\", \"content\": \"from pathli... | 1 | Swarm-AI-Research/fable5-traces-sft |
[
{
"role": "user",
"content": "…[earlier truncated]…\nedural.py (file state is current in your context — no need to Read it back)\nAssistant: Now OEC: config flag + centering in loop. Tests first:\nAssistant action: Bash {\"command\": \"cat >> tests/test_stm.py << 'EOF'\\n\\n\\ndef test_center_output_embeddi... | 1 | Swarm-AI-Research/fable5-traces-sft |
[
{
"role": "user",
"content": "…[earlier truncated]…\nerate_dyck_sequence — src/mythosmini/data/procedural.py\\n\\n**Purpose:** Generate one balanced Dyck-language byte sequence over four bracket pairs ()[]{}<> (Procedural Pretraining, arXiv 2601.21725): random open/close walk bounded by max_depth, closing e... | 1 | Swarm-AI-Research/fable5-traces-sft |
[
{
"role": "user",
"content": "…[earlier truncated]…\nndowBuffer, build_token_cache, cache_path_for\\nfrom src.mythosmini.data.procedural import build_procedural_cache\\nfrom src.mythosmini.hardware.detect import DeviceInfo, describe_device, detect_device\\nfrom src.mythosmini.leaderboard.fetch import fetch_... | 1 | Swarm-AI-Research/fable5-traces-sft |
[
{
"role": "user",
"content": "…[earlier truncated]…\nc/mythosmini/training/stm.py has been updated successfully. (file state is current in your context — no need to Read it back)\nAssistant action: Bash {\"command\": \"make lint > /tmp/lint.log 2>&1; echo \\\"lint: $?\\\"; make pyaot 2>&1 | tail -1; .venv/b... | 1 | Swarm-AI-Research/fable5-traces-sft |
[
{
"role": "user",
"content": "…[earlier truncated]…\nimport (\\\"\\\"\\\", \\\"\\\"\\\"import torch\\nfrom src.mythosmini.checkpointing.saver import BackgroundSaver\\nfrom src.mythosmini.config import (\\\"\\\"\\\")\\ntext = text.replace(\\\"\\\"\\\" buffer = GpuWindowBuffer(cache_path, train_config.seq_... | 1 | Swarm-AI-Research/fable5-traces-sft |
[
{
"role": "user",
"content": "<task-notification>\n<task-id>bmpqoq5ga</task-id>\n<tool-use-id>toolu_01E5bZ7Z5HnGDxT8cQt1SLNo</tool-use-id>\n<output-file>/tmp/claude-1000/-home-lane-MythosMini/e24a84d9-b09d-4531-97ce-3c078924b269/tasks/bmpqoq5ga.output</output-file>\n<status>completed</status>\n<summary>Back... | 1 | Swarm-AI-Research/fable5-traces-sft |
[
{
"role": "user",
"content": "…[earlier truncated]…\n6-06-10.\\n\\n### build_procedural_cache — src/mythosmini/data/procedural.py\\n\\n**Purpose:** Write a uint16 token cache of EOS-separated Dyck sequ …[truncated]\nResult: lint: 2\n174 | )\n |\n\nFound 2 errors.\nmake: *** [Makefile:16: lint... | 1 | Swarm-AI-Research/fable5-traces-sft |
[
{
"role": "user",
"content": "<task-notification>\n<task-id>bmpqoq5ga</task-id>\n<tool-use-id>toolu_01E5bZ7Z5HnGDxT8cQt1SLNo</tool-use-id>\n<output-file>/tmp/claude-1000/-home-lane-MythosMini/e24a84d9-b09d-4531-97ce-3c078924b269/tasks/bmpqoq5ga.output</output-file>\n<status>completed</status>\n<summary>Back... | 1 | Swarm-AI-Research/fable5-traces-sft |
[
{
"role": "user",
"content": "…[earlier truncated]…\nprint(\n172 | f\"stm round {round_index} epoch {epoch}: pool {pool_size} \"\n173 | f\"mean loss {mean_loss:.4f} saved {saved.name}\"\n174 | )\nFound 2 errors.\nmake: *** [Makefile:16: lint] …[truncated]\nAssista... | 1 | Swarm-AI-Research/fable5-traces-sft |
[
{
"role": "user",
"content": "<task-notification>\n<task-id>bmpqoq5ga</task-id>\n<tool-use-id>toolu_01E5bZ7Z5HnGDxT8cQt1SLNo</tool-use-id>\n<output-file>/tmp/claude-1000/-home-lane-MythosMini/e24a84d9-b09d-4531-97ce-3c078924b269/tasks/bmpqoq5ga.output</output-file>\n<status>completed</status>\n<summary>Back... | 1 | Swarm-AI-Research/fable5-traces-sft |
[
{
"role": "user",
"content": "<task-notification>\n<task-id>bmpqoq5ga</task-id>\n<tool-use-id>toolu_01E5bZ7Z5HnGDxT8cQt1SLNo</tool-use-id>\n<output-file>/tmp/claude-1000/-home-lane-MythosMini/e24a84d9-b09d-4531-97ce-3c078924b269/tasks/bmpqoq5ga.output</output-file>\n<status>completed</status>\n<summary>Back... | 1 | Swarm-AI-Research/fable5-traces-sft |
[
{
"role": "user",
"content": "<task-notification>\n<task-id>bmpqoq5ga</task-id>\n<tool-use-id>toolu_01E5bZ7Z5HnGDxT8cQt1SLNo</tool-use-id>\n<output-file>/tmp/claude-1000/-home-lane-MythosMini/e24a84d9-b09d-4531-97ce-3c078924b269/tasks/bmpqoq5ga.output</output-file>\n<status>completed</status>\n<summary>Back... | 1 | Swarm-AI-Research/fable5-traces-sft |
[
{
"role": "user",
"content": "<task-notification>\n<task-id>bmpqoq5ga</task-id>\n<tool-use-id>toolu_01E5bZ7Z5HnGDxT8cQt1SLNo</tool-use-id>\n<output-file>/tmp/claude-1000/-home-lane-MythosMini/e24a84d9-b09d-4531-97ce-3c078924b269/tasks/bmpqoq5ga.output</output-file>\n<status>completed</status>\n<summary>Back... | 1 | Swarm-AI-Research/fable5-traces-sft |
[
{
"role": "user",
"content": "<task-notification>\n<task-id>bwzts9wgo</task-id>\n<tool-use-id>toolu_01Ac6u27noTdh7VSes9QpHJY</tool-use-id>\n<output-file>/tmp/claude-1000/-home-lane-MythosMini/e24a84d9-b09d-4531-97ce-3c078924b269/tasks/bwzts9wgo.output</output-file>\n<status>completed</status>\n<summary>Back... | 1 | Swarm-AI-Research/fable5-traces-sft |
[
{
"role": "user",
"content": "<task-notification>\n<task-id>bwzts9wgo</task-id>\n<tool-use-id>toolu_01Ac6u27noTdh7VSes9QpHJY</tool-use-id>\n<output-file>/tmp/claude-1000/-home-lane-MythosMini/e24a84d9-b09d-4531-97ce-3c078924b269/tasks/bwzts9wgo.output</output-file>\n<status>completed</status>\n<summary>Back... | 1 | Swarm-AI-Research/fable5-traces-sft |
[
{
"role": "user",
"content": "<task-notification>\n<task-id>bbq5po02i</task-id>\n<tool-use-id>toolu_01DzzJbENhKBPKUkuGFmwaX2</tool-use-id>\n<output-file>/tmp/claude-1000/-home-lane-MythosMini/e24a84d9-b09d-4531-97ce-3c078924b269/tasks/bbq5po02i.output</output-file>\n<status>completed</status>\n<summary>Back... | 1 | Swarm-AI-Research/fable5-traces-sft |
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