Add files using upload-large-folder tool

healed/calib/calib.log

@@ -0,0 +1,28 @@
+2026-07-17T19:42:27-07:00 === STEP CALIBRATION: glean seed1224, 3 rungs parallel, ->600, eval/100 ===
+2026-07-17T19:42:27-07:00 CALIB keep25 HEAL -> 100
+2026-07-17T19:44:57-07:00 CALIB keep50 HEAL -> 100
+2026-07-17T19:47:27-07:00 CALIB keep75 HEAL -> 100
+2026-07-17T21:06:49-07:00 CALIB keep25 EVAL step100
+2026-07-17T21:08:52-07:00 CALIB-RESULT keep25 step100 gsm8k=0.182
+2026-07-17T21:08:52-07:00 CALIB keep25 HEAL -> 200
+2026-07-17T21:12:38-07:00 CALIB keep50 EVAL step100
+2026-07-17T21:14:22-07:00 CALIB-RESULT keep50 step100 gsm8k=0.558
+2026-07-17T21:14:22-07:00 CALIB keep50 HEAL -> 200
+2026-07-17T21:35:29-07:00 CALIB keep75 EVAL step100
+2026-07-17T21:37:08-07:00 CALIB-RESULT keep75 step100 gsm8k=0.652
+2026-07-17T21:37:08-07:00 CALIB keep75 HEAL -> 200
+2026-07-17T22:31:43-07:00 CALIB keep25 EVAL step200
+2026-07-17T22:33:33-07:00 CALIB-RESULT keep25 step200 gsm8k=0.238
+2026-07-17T22:33:33-07:00 CALIB keep25 HEAL -> 300
+2026-07-17T22:41:41-07:00 CALIB keep50 EVAL step200
+2026-07-17T22:43:19-07:00 CALIB-RESULT keep50 step200 gsm8k=0.564
+2026-07-17T22:43:19-07:00 CALIB keep50 HEAL -> 300
+2026-07-17T23:22:47-07:00 CALIB keep75 EVAL step200
+2026-07-17T23:24:34-07:00 CALIB-RESULT keep75 step200 gsm8k=0.634
+2026-07-17T23:24:34-07:00 CALIB keep75 HEAL -> 300
+2026-07-17T23:55:53-07:00 CALIB keep25 EVAL step300
+2026-07-17T23:57:32-07:00 CALIB-RESULT keep25 step300 gsm8k=0.244
+2026-07-17T23:57:32-07:00 CALIB keep25 HEAL -> 400
+2026-07-18T00:09:57-07:00 CALIB keep50 EVAL step300
+2026-07-18T00:11:38-07:00 CALIB-RESULT keep50 step300 gsm8k=0.566
+2026-07-18T00:11:38-07:00 CALIB keep50 HEAL -> 400

healed/calib/glean_keep25_s1224/step0300/chat_template.jinja

@@ -0,0 +1,9 @@
+{{ bos_token }}{% for message in messages %}{% if message['role'] == 'system' %}{{ '<|system|>
+' + message['content'] + '
+' }}{% elif message['role'] == 'user' %}{{ '<|user|>
+' + message['content'] + '
+' }}{% elif message['role'] == 'assistant' %}{% if not loop.last %}{{ '<|assistant|>
+'  + message['content'] + eos_token + '
+' }}{% else %}{{ '<|assistant|>
+'  + message['content'] + eos_token }}{% endif %}{% endif %}{% if loop.last and add_generation_prompt %}{{ '<|assistant|>
+' }}{% endif %}{% endfor %}

healed/calib/glean_keep25_s1224/step0300/config.json

@@ -0,0 +1,699 @@
+{
+  "architectures": [
+    "PrunedOlmoeForCausalLM"
+  ],
+  "attention_bias": false,
+  "attention_dropout": 0.0,
+  "auto_map": {
+    "AutoConfig": "configuration_pruned_olmoe.PrunedOlmoeConfig",
+    "AutoModelForCausalLM": "modeling_pruned_olmoe.PrunedOlmoeForCausalLM"
+  },
+  "clip_qkv": null,
+  "dtype": "bfloat16",
+  "eos_token_id": 50279,
+  "expert_widths": [
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+      128,
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+      128,
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+      512,
+      256,
+      512,
+      640,
+      384,
+      256,
+      256,
+      256
+    ]
+  ],
+  "glean_metadata": {
+    "base_model": "allenai/OLMoE-1B-7B-0125-Instruct",
+    "block_size": 128,
+    "criterion": "reap",
+    "dead_experts": 405,
+    "keep_fraction": 0.25,
+    "min_width": 128,
+    "params": 2086494208,
+    "scores": "outputs/scores_general_mix/scores.pt"
+  },
+  "hidden_act": "silu",
+  "hidden_size": 2048,
+  "initializer_range": 0.02,
+  "intermediate_size": 1024,
+  "max_position_embeddings": 4096,
+  "model_type": "pruned_olmoe",
+  "norm_topk_prob": false,
+  "num_attention_heads": 16,
+  "num_experts": 64,
+  "num_experts_per_tok": 8,
+  "num_hidden_layers": 16,
+  "num_key_value_heads": 16,
+  "output_router_logits": false,
+  "pad_token_id": 1,
+  "rms_norm_eps": 1e-05,
+  "rope_scaling": null,
+  "rope_theta": 10000.0,
+  "router_aux_loss_coef": 0.01,
+  "tie_word_embeddings": false,
+  "transformers_version": "4.57.6",
+  "use_cache": false,
+  "vocab_size": 50304
+}

healed/calib/glean_keep25_s1224/step0300/configuration_pruned_olmoe.py

@@ -0,0 +1,27 @@
+"""Configuration for GLEAN-pruned OLMoE: variable-width, variable-count experts.
+"""
+
+from transformers.models.olmoe.configuration_olmoe import OlmoeConfig
+
+
+class PrunedOlmoeConfig(OlmoeConfig):
+    """OlmoeConfig plus a per-(layer, expert) width table.
+
+    ``expert_widths[l]`` lists the SwiGLU intermediate width of each surviving
+    expert in decoder layer ``l``, in expert order. Lists are ragged: layers
+    may keep different numbers of experts (deleted experts simply don't
+    appear — the router in layer ``l`` has ``len(expert_widths[l])`` rows),
+    and each width may differ (multiples of the GEMM block size, 128, for
+    variable-MegaBlocks execution). ``None`` means an unpruned model
+    (uniform ``num_experts`` × ``intermediate_size``).
+
+    The inherited ``num_experts`` / ``intermediate_size`` keep their ORIGINAL
+    (pre-pruning) values for provenance; the width table is authoritative for
+    the built architecture.
+    """
+
+    model_type = "pruned_olmoe"
+
+    def __init__(self, expert_widths: list[list[int]] | None = None, **kwargs):
+        super().__init__(**kwargs)
+        self.expert_widths = expert_widths

healed/calib/glean_keep25_s1224/step0300/generation_config.json

@@ -0,0 +1,6 @@
+{
+  "_from_model_config": true,
+  "eos_token_id": 50279,
+  "pad_token_id": 1,
+  "transformers_version": "4.57.6"
+}

healed/calib/glean_keep25_s1224/step0300/modeling_pruned_olmoe.py

@@ -0,0 +1,66 @@
+"""GLEAN-pruned OLMoE: HF-loadable model with ragged (variable-width) experts.
+
+Pattern follows hbfreed/variable-flex-olmo's PrunedFlexOlmoForCausalLM
+(docs/recon/prior-work-hbfreed.md), generalized from one scalar width to a
+per-(layer, expert) width table: ``super().__init__`` builds the uniform
+architecture from the config, then every MoE block is rebuilt to its pruned
+shape — surviving experts only, each at its own width, router sliced to
+match — so the state dict aligns exactly with what
+``glean.prune.prune_channels_global`` leaves behind.
+
+Caveat: ``output_router_logits=True`` (the load-balancing aux loss) assumes a
+uniform ``config.num_experts`` and is unsupported on ragged models.
+"""
+
+import torch.nn as nn
+from transformers.activations import ACT2FN
+from transformers.models.olmoe.modeling_olmoe import OlmoeForCausalLM
+
+from .configuration_pruned_olmoe import PrunedOlmoeConfig
+
+
+class RaggedOlmoeMLP(nn.Module):
+    """OlmoeMLP with an explicit intermediate width (SwiGLU, no biases)."""
+
+    def __init__(self, hidden_size: int, intermediate_size: int, hidden_act: str):
+        super().__init__()
+        self.hidden_size = hidden_size
+        self.intermediate_size = intermediate_size
+        self.gate_proj = nn.Linear(hidden_size, intermediate_size, bias=False)
+        self.up_proj = nn.Linear(hidden_size, intermediate_size, bias=False)
+        self.down_proj = nn.Linear(intermediate_size, hidden_size, bias=False)
+        self.act_fn = ACT2FN[hidden_act]
+
+    def forward(self, x):
+        return self.down_proj(self.act_fn(self.gate_proj(x)) * self.up_proj(x))
+
+
+class PrunedOlmoeForCausalLM(OlmoeForCausalLM):
+    """OLMoE with per-layer surviving-expert lists at per-expert widths."""
+
+    config_class = PrunedOlmoeConfig
+
+    def __init__(self, config: PrunedOlmoeConfig):
+        super().__init__(config)
+        widths_table = getattr(config, "expert_widths", None)
+        if widths_table is None:
+            return  # unpruned: plain OLMoE
+        if len(widths_table) != len(self.model.layers):
+            raise ValueError(
+                f"expert_widths has {len(widths_table)} rows but the model has "
+                f"{len(self.model.layers)} decoder layers"
+            )
+        for layer, widths in zip(self.model.layers, widths_table):
+            if any(w <= 0 for w in widths):
+                raise ValueError("expert_widths must list surviving experts only (>0)")
+            block = layer.mlp
+            if len(widths) < block.top_k:
+                raise ValueError(
+                    f"a layer keeps {len(widths)} experts < top_k={block.top_k}"
+                )
+            block.num_experts = len(widths)
+            block.gate = nn.Linear(config.hidden_size, len(widths), bias=False)
+            block.experts = nn.ModuleList(
+                RaggedOlmoeMLP(config.hidden_size, w, config.hidden_act)
+                for w in widths
+            )

healed/calib/glean_keep25_s1224/step0300/special_tokens_map.json

@@ -0,0 +1,23 @@
+{
+  "bos_token": {
+    "content": "|||IP_ADDRESS|||",
+    "lstrip": false,
+    "normalized": true,
+    "rstrip": false,
+    "single_word": false
+  },
+  "eos_token": {
+    "content": "|||IP_ADDRESS|||",
+    "lstrip": false,
+    "normalized": true,
+    "rstrip": false,
+    "single_word": false
+  },
+  "pad_token": {
+    "content": "<pad>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  }
+}

healed/calib/glean_keep25_s1224/step0300/tokenizer_config.json

@@ -0,0 +1,247 @@
+{
+  "add_bos_token": false,
+  "add_eos_token": false,
+  "add_prefix_space": false,
+  "added_tokens_decoder": {
+    "0": {
+      "content": "<|endoftext|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "1": {
+      "content": "<|padding|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "50254": {
+      "content": "                        ",
+      "lstrip": false,
+      "normalized": true,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "50255": {
+      "content": "                       ",
+      "lstrip": false,
+      "normalized": true,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "50256": {
+      "content": "                      ",
+      "lstrip": false,
+      "normalized": true,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "50257": {
+      "content": "                     ",
+      "lstrip": false,
+      "normalized": true,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "50258": {
+      "content": "                    ",
+      "lstrip": false,
+      "normalized": true,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "50259": {
+      "content": "                   ",
+      "lstrip": false,
+      "normalized": true,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
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+    value: null
+vllm_gpu_mem_util:
+    value: 0.85
+vllm_live_dir:
+    value: null
+vllm_port:
+    value: 8377
+vllm_refresh_every:
+    value: 5
+vllm_refresh_mode:
+    value: reload
+vllm_serve_bin:
+    value: vllm-plugin/.venv/bin/python
+wandb:
+    value: true
+wandb_mode:
+    value: online
+wandb_project:
+    value: glean-calib
+wandb_resume:
+    value: null
+wandb_run_id:
+    value: null
+wandb_run_name:
+    value: calib_keep25_100
+warmup_steps:
+    value: 10
+weight_decay:
+    value: 0.1

healed/calib/glean_keep25_s1224/wandb/run-20260717_194234-6or4rzve/files/output.log

@@ -0,0 +1,114 @@
+58360 cached top-128 chat trajectories / 22,295,631 unique tokens | 185 steps/epoch | 100 total steps | student params 2.09B | teacher overlap=False
+{"step": 1, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 1.230232952358822, "tokens": 120000, "cumulative_loss_tokens": 120000, "grad_norm": 18.125, "lr": 6e-06, "finish_rate": 0.869, "comp_len": 463.3, "t_data_s": 0.2, "t_rollout_s": 0.0, "t_step_s": 41.7, "frames": {"chat": 259}, "mem_gb": 9.93}
+The attention mask is not set and cannot be inferred from input because pad token is same as eos token. As a consequence, you may observe unexpected behavior. Please pass your input's `attention_mask` to obtain reliable results.
+[eval step 1] sample: 'The rank of a matrix is the number that represents the position of its elements when they are listed in ascending order.\n\n\nFor the matrix [[12, -16, 4, 16], [-9, 11, -1, -10], [0, 1, -2, -2], [0,'
+{"step": 2, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 1.7564590806936224, "tokens": 120000, "cumulative_loss_tokens": 240000, "grad_norm": 23.625, "lr": 9e-06, "finish_rate": 0.997, "comp_len": 344.8, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 43.3, "frames": {"chat": 348}, "mem_gb": 9.59}
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+[eval step 10] sample: 'To compute the rank of a 4x4 matrix, we need to determine the number of linearly independent rows or columns in the matrix. The rank of a matrix is the number of linearly independent rows or columns.\n'
+{"step": 11, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 1.1661802035542825, "tokens": 120000, "cumulative_loss_tokens": 1320000, "grad_norm": 2.796875, "lr": 3e-05, "finish_rate": 0.997, "comp_len": 364.7, "t_data_s": 0.1, "t_rollout_s": 0.0, "t_step_s": 43.3, "frames": {"chat": 329}, "mem_gb": 9.78}
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+{"step": 13, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.9208825074313829, "tokens": 120000, "cumulative_loss_tokens": 1560000, "grad_norm": 1.9765625, "lr": 3e-05, "finish_rate": 0.974, "comp_len": 439.6, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 37.8, "frames": {"chat": 273}, "mem_gb": 9.98}
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+[eval step 20] sample: 'To determine the rank of a 4x4 matrix, we need to find the maximum number of linearly independent rows or columns in the given matrix. This is done by performing row reduction (or elimination) operati'
+{"step": 21, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.6084738344653199, "tokens": 120000, "cumulative_loss_tokens": 2520000, "grad_norm": 0.9140625, "lr": 3e-05, "finish_rate": 0.963, "comp_len": 402.7, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 44.7, "frames": {"chat": 298}, "mem_gb": 9.8}
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+{"step": 29, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 1.008847612483551, "tokens": 120000, "cumulative_loss_tokens": 3480000, "grad_norm": 1.1328125, "lr": 3e-05, "finish_rate": 0.997, "comp_len": 300.8, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 65.1, "frames": {"chat": 399}, "mem_gb": 9.62}
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+[eval step 30] sample: 'To determine the rank of a 4x4 matrix, we need to find the maximum number of linearly independent rows or columns in the matrix. This involves performing row reduction (or elimination) operations to r'
+{"step": 31, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.22508179676756263, "tokens": 120000, "cumulative_loss_tokens": 3720000, "grad_norm": 0.7109375, "lr": 3e-05, "finish_rate": 0.891, "comp_len": 521.7, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 37.6, "frames": {"chat": 230}, "mem_gb": 9.83}
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+{"step": 40, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.571639004661267, "tokens": 120000, "cumulative_loss_tokens": 4800000, "grad_norm": 0.765625, "lr": 3e-05, "finish_rate": 0.957, "comp_len": 364.7, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 51.8, "frames": {"chat": 329}, "mem_gb": 9.83}
+[eval step 40] sample: "To compute the rank of a matrix, we need to determine the maximum number of linearly independent rows or columns in the matrix. \n\nLet's represent the matrix as follows:\n\n\\[\nA = \\begin{pmatrix}\n12 & -1"
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+[eval step 50] sample: 'To compute the rank of a matrix, we need to determine the maximum number of linearly independent rows or columns. For a 4x4 matrix, we can use various methods, including row reduction (Gaussian elimin'
+{"step": 51, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.6242874289091677, "tokens": 120000, "cumulative_loss_tokens": 6120000, "grad_norm": 0.80078125, "lr": 3e-05, "finish_rate": 0.946, "comp_len": 402.7, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 51.6, "frames": {"chat": 298}, "mem_gb": 9.96}
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+[eval step 60] sample: "To determine the rank of a matrix, we need to find the maximum number of linearly independent rows or columns. In this case, we have a 4x4 matrix.\n\nLet's represent the matrix as follows:\n\n\\[\n\\begin{bm"
+{"step": 61, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.8599373236271243, "tokens": 120000, "cumulative_loss_tokens": 7320000, "grad_norm": 0.8828125, "lr": 3e-05, "finish_rate": 0.997, "comp_len": 378.5, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 49.5, "frames": {"chat": 317}, "mem_gb": 9.74}
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+{"step": 63, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.19509722631207357, "tokens": 120000, "cumulative_loss_tokens": 7560000, "grad_norm": 0.53125, "lr": 3e-05, "finish_rate": 0.886, "comp_len": 524.0, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 41.1, "frames": {"chat": 229}, "mem_gb": 9.86}
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+{"step": 65, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.7168835557150344, "tokens": 120000, "cumulative_loss_tokens": 7800000, "grad_norm": 0.8046875, "lr": 3e-05, "finish_rate": 0.997, "comp_len": 335.2, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 56.9, "frames": {"chat": 358}, "mem_gb": 9.59}
+{"step": 66, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.8474990683452537, "tokens": 120000, "cumulative_loss_tokens": 7920000, "grad_norm": 0.8359375, "lr": 3e-05, "finish_rate": 0.975, "comp_len": 381.0, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 53.2, "frames": {"chat": 315}, "mem_gb": 9.97}
+{"step": 67, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.5007493379529565, "tokens": 120000, "cumulative_loss_tokens": 8040000, "grad_norm": 0.703125, "lr": 3e-05, "finish_rate": 0.905, "comp_len": 454.5, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 45.7, "frames": {"chat": 264}, "mem_gb": 10.05}
+{"step": 68, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.7983169680517167, "tokens": 120000, "cumulative_loss_tokens": 8160000, "grad_norm": 0.86328125, "lr": 3e-05, "finish_rate": 0.997, "comp_len": 356.1, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 52.3, "frames": {"chat": 337}, "mem_gb": 9.71}
+{"step": 69, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.3968792498229072, "tokens": 120000, "cumulative_loss_tokens": 8280000, "grad_norm": 0.66015625, "lr": 3e-05, "finish_rate": 0.873, "comp_len": 478.1, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 42.8, "frames": {"chat": 251}, "mem_gb": 10.01}
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+[eval step 70] sample: 'To compute the rank of a matrix, we need to determine the maximum number of linearly independent rows or columns in the matrix. \n\nGiven the matrix:\n\\[\n\\begin{bmatrix}\n12 & -16 & 4 & 16 \\\\\n-9 & 11 & -1'
+{"step": 71, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.4847112138859307, "tokens": 120000, "cumulative_loss_tokens": 8520000, "grad_norm": 0.62109375, "lr": 3e-05, "finish_rate": 0.917, "comp_len": 454.5, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 43.1, "frames": {"chat": 264}, "mem_gb": 9.95}
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+{"step": 73, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.46530003577539075, "tokens": 120000, "cumulative_loss_tokens": 8760000, "grad_norm": 0.62890625, "lr": 3e-05, "finish_rate": 0.953, "comp_len": 372.7, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 50.8, "frames": {"chat": 322}, "mem_gb": 9.74}
+{"step": 74, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.7914243449340264, "tokens": 120000, "cumulative_loss_tokens": 8880000, "grad_norm": 0.8203125, "lr": 3e-05, "finish_rate": 0.997, "comp_len": 332.4, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 56.3, "frames": {"chat": 361}, "mem_gb": 9.72}
+{"step": 75, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.5085253859311343, "tokens": 120000, "cumulative_loss_tokens": 9000000, "grad_norm": 0.68359375, "lr": 3e-05, "finish_rate": 0.972, "comp_len": 373.8, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 54.4, "frames": {"chat": 321}, "mem_gb": 9.9}
+{"step": 76, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.6606305929463356, "tokens": 120000, "cumulative_loss_tokens": 9120000, "grad_norm": 0.77734375, "lr": 3e-05, "finish_rate": 0.967, "comp_len": 390.9, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 52.1, "frames": {"chat": 307}, "mem_gb": 9.8}
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+{"step": 78, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.8415959956489504, "tokens": 120000, "cumulative_loss_tokens": 9360000, "grad_norm": 0.828125, "lr": 3e-05, "finish_rate": 0.997, "comp_len": 337.1, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 56.0, "frames": {"chat": 356}, "mem_gb": 9.51}
+{"step": 79, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.39085237814461193, "tokens": 120000, "cumulative_loss_tokens": 9480000, "grad_norm": 0.59765625, "lr": 3e-05, "finish_rate": 0.915, "comp_len": 424.0, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 46.4, "frames": {"chat": 283}, "mem_gb": 9.86}
+{"step": 80, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.7660272711459547, "tokens": 120000, "cumulative_loss_tokens": 9600000, "grad_norm": 0.796875, "lr": 3e-05, "finish_rate": 0.998, "comp_len": 299.3, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 60.3, "frames": {"chat": 401}, "mem_gb": 9.61}
+[eval step 80] sample: 'To determine the rank of a matrix, we need to find the maximum number of linearly independent rows or columns. A matrix is said to be of rank \\( n \\) if and only if the determinant of its submatrix fo'
+{"step": 81, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.5008506985542674, "tokens": 120000, "cumulative_loss_tokens": 9720000, "grad_norm": 0.67578125, "lr": 3e-05, "finish_rate": 0.965, "comp_len": 416.7, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 44.1, "frames": {"chat": 288}, "mem_gb": 9.81}
+{"step": 82, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.6260418511068448, "tokens": 120000, "cumulative_loss_tokens": 9840000, "grad_norm": 0.734375, "lr": 3e-05, "finish_rate": 0.981, "comp_len": 371.5, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 52.3, "frames": {"chat": 323}, "mem_gb": 9.85}
+{"step": 83, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.8587517221443355, "tokens": 120000, "cumulative_loss_tokens": 9960000, "grad_norm": 0.83203125, "lr": 3e-05, "finish_rate": 0.997, "comp_len": 357.1, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 52.2, "frames": {"chat": 336}, "mem_gb": 9.74}
+{"step": 84, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.6985716276330253, "tokens": 120000, "cumulative_loss_tokens": 10080000, "grad_norm": 0.73828125, "lr": 3e-05, "finish_rate": 0.997, "comp_len": 364.7, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 51.2, "frames": {"chat": 329}, "mem_gb": 9.64}
+{"step": 85, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.687701484311993, "tokens": 120000, "cumulative_loss_tokens": 10200000, "grad_norm": 0.75390625, "lr": 3e-05, "finish_rate": 0.997, "comp_len": 349.9, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 57.9, "frames": {"chat": 343}, "mem_gb": 9.63}
+{"step": 86, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.7140811091547211, "tokens": 120000, "cumulative_loss_tokens": 10320000, "grad_norm": 0.76953125, "lr": 3e-05, "finish_rate": 0.997, "comp_len": 357.1, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 54.9, "frames": {"chat": 336}, "mem_gb": 9.75}
+{"step": 87, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.6668962659992278, "tokens": 120000, "cumulative_loss_tokens": 10440000, "grad_norm": 0.7578125, "lr": 3e-05, "finish_rate": 0.984, "comp_len": 382.2, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 49.8, "frames": {"chat": 314}, "mem_gb": 9.98}
+{"step": 88, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.41399116188709933, "tokens": 120000, "cumulative_loss_tokens": 10560000, "grad_norm": 0.5859375, "lr": 3e-05, "finish_rate": 0.968, "comp_len": 427.0, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 43.7, "frames": {"chat": 281}, "mem_gb": 9.82}
+{"step": 89, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.5405620834658543, "tokens": 120000, "cumulative_loss_tokens": 10680000, "grad_norm": 0.66015625, "lr": 3e-05, "finish_rate": 0.977, "comp_len": 401.3, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 47.7, "frames": {"chat": 299}, "mem_gb": 9.69}
+{"step": 90, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.6631705802235752, "tokens": 120000, "cumulative_loss_tokens": 10800000, "grad_norm": 0.734375, "lr": 3e-05, "finish_rate": 0.966, "comp_len": 365.9, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 50.5, "frames": {"chat": 328}, "mem_gb": 9.88}
+[eval step 90] sample: "To determine the rank of a matrix, we need to find the maximum number of linearly independent rows or columns. Let's break down the steps:\n\n1. **Understand the Matrix:**\n   The given matrix is:\n   \\[\n"
+{"step": 91, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.5150351522366206, "tokens": 120000, "cumulative_loss_tokens": 10920000, "grad_norm": 0.640625, "lr": 3e-05, "finish_rate": 0.919, "comp_len": 442.8, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 43.5, "frames": {"chat": 271}, "mem_gb": 9.98}
+{"step": 92, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.5397100653167815, "tokens": 120000, "cumulative_loss_tokens": 11040000, "grad_norm": 0.66796875, "lr": 3e-05, "finish_rate": 0.874, "comp_len": 446.1, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 48.3, "frames": {"chat": 269}, "mem_gb": 10.08}
+{"step": 93, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.8141359398245811, "tokens": 120000, "cumulative_loss_tokens": 11160000, "grad_norm": 0.7890625, "lr": 3e-05, "finish_rate": 0.997, "comp_len": 324.3, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 58.3, "frames": {"chat": 370}, "mem_gb": 9.63}
+{"step": 94, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.8571522327400743, "tokens": 120000, "cumulative_loss_tokens": 11280000, "grad_norm": 0.8359375, "lr": 3e-05, "finish_rate": 0.997, "comp_len": 340.9, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 53.7, "frames": {"chat": 352}, "mem_gb": 9.69}
+{"step": 95, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.7481899343090753, "tokens": 120000, "cumulative_loss_tokens": 11400000, "grad_norm": 0.8125, "lr": 3e-05, "finish_rate": 0.997, "comp_len": 363.6, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 54.5, "frames": {"chat": 330}, "mem_gb": 9.89}
+{"step": 96, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.6086833366600797, "tokens": 120000, "cumulative_loss_tokens": 11520000, "grad_norm": 0.71875, "lr": 3e-05, "finish_rate": 0.991, "comp_len": 359.3, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 52.4, "frames": {"chat": 334}, "mem_gb": 9.62}
+{"step": 97, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.6093245712597544, "tokens": 120000, "cumulative_loss_tokens": 11640000, "grad_norm": 0.734375, "lr": 3e-05, "finish_rate": 0.967, "comp_len": 357.1, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 48.4, "frames": {"chat": 336}, "mem_gb": 9.75}
+{"step": 98, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.4405221594436715, "tokens": 120000, "cumulative_loss_tokens": 11760000, "grad_norm": 0.64453125, "lr": 3e-05, "finish_rate": 0.964, "comp_len": 388.3, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 47.2, "frames": {"chat": 309}, "mem_gb": 9.84}
+{"step": 99, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.750379044538488, "tokens": 120000, "cumulative_loss_tokens": 11880000, "grad_norm": 0.77734375, "lr": 3e-05, "finish_rate": 0.997, "comp_len": 306.9, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 56.1, "frames": {"chat": 391}, "mem_gb": 9.73}
+{"step": 100, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.39380424299488465, "tokens": 120000, "cumulative_loss_tokens": 12000000, "grad_norm": 0.61328125, "lr": 3e-05, "finish_rate": 0.83, "comp_len": 454.5, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 46.9, "frames": {"chat": 264}, "mem_gb": 10.12}
+[eval step 100] sample: "To find the rank of a matrix, we need to determine the maximum number of linearly independent rows or columns in the matrix. \n\nLet's represent the given 4x4 matrix:\n\n\\[\n\\begin{bmatrix}\n12 & -16 & 4 & "
+checkpoint snapshot queued -> outputs/healed/calib/glean_keep25_s1224/step0100

healed/calib/glean_keep25_s1224/wandb/run-20260717_194234-6or4rzve/files/requirements.txt

@@ -0,0 +1,129 @@
+certifi==2026.6.17
+fonttools==4.63.0
+urllib3==2.7.0
+requests==2.34.2
+nvidia-cublas-cu12==12.6.4.1
+packaging==26.2
+regex==2026.6.28
+portalocker==3.2.0
+safetensors==0.8.0
+datasets==5.0.0
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healed/calib/glean_keep25_s1224/wandb/run-20260717_223339-i2xsgbr0/files/config.yaml

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healed/calib/glean_keep25_s1224/wandb/run-20260717_223339-i2xsgbr0/files/output.log

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+The attention mask is not set and cannot be inferred from input because pad token is same as eos token. As a consequence, you may observe unexpected behavior. Please pass your input's `attention_mask` to obtain reliable results.
+[eval step 210] sample: "To find the rank of a matrix, we need to determine the maximum number of linearly independent rows or columns in the matrix. \n\nLet's represent the given 4x4 matrix:\n\n\\[\n\\begin{bmatrix}\n12 & -16 & 4 & "
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+[eval step 220] sample: 'To find the rank of a matrix, we need to determine the maximum number of linearly independent rows or columns in the matrix. A matrix is said to be of rank \\( r \\) if it has \\( r \\) linearly independe'
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+[eval step 230] sample: 'To find the rank of a matrix, we need to determine the maximum number of linearly independent rows or columns in the matrix. A matrix is said to be of rank \\( r \\) if it has \\( r \\) linearly independe'
+{"step": 231, "epoch": 1, "training_mode": "off-policy", "forward_topk_kl": 0.27765468792688724, "tokens": 120000, "cumulative_loss_tokens": 27720000, "grad_norm": 0.52734375, "lr": 3e-05, "finish_rate": 0.923, "comp_len": 418.1, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 46.5, "frames": {"chat": 287}, "mem_gb": 10.01}
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+[eval step 240] sample: 'To compute the rank of a matrix, we need to determine the maximum number of linearly independent rows or columns in the matrix. \n\nGiven the matrix:\n\\[\n\\begin{bmatrix}\n12 & -16 & 4 & 16 \\\\\n-9 & 11 & -1'
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+[eval step 250] sample: 'To compute the rank of a matrix, we need to determine the maximum number of linearly independent rows or columns in the matrix. A matrix is said to be of rank \\( r \\) if it has \\( r \\) linearly indepe'
+{"step": 251, "epoch": 1, "training_mode": "off-policy", "forward_topk_kl": 0.47302225923389196, "tokens": 120000, "cumulative_loss_tokens": 30120000, "grad_norm": 0.58203125, "lr": 3e-05, "finish_rate": 0.968, "comp_len": 348.8, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 49.1, "frames": {"chat": 344}, "mem_gb": 9.98}
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+[eval step 260] sample: 'To find the rank of a matrix, we need to determine the maximum number of linearly independent rows or columns in the matrix. A matrix is said to be of rank \\( r \\) if it has \\( r \\) linearly independe'
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+[eval step 270] sample: 'To compute the rank of a matrix, we need to determine the maximum number of linearly independent rows or columns in the matrix. A matrix is said to be of rank \\( r \\) if it has \\( r \\) linearly indepe'
+{"step": 271, "epoch": 1, "training_mode": "off-policy", "forward_topk_kl": 0.6530711838837713, "tokens": 120000, "cumulative_loss_tokens": 32520000, "grad_norm": 0.6953125, "lr": 3e-05, "finish_rate": 0.997, "comp_len": 339.0, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 53.0, "frames": {"chat": 354}, "mem_gb": 9.77}
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+{"step": 278, "epoch": 1, "training_mode": "off-policy", "forward_topk_kl": 0.3597586765645693, "tokens": 120000, "cumulative_loss_tokens": 33360000, "grad_norm": 0.59375, "lr": 3e-05, "finish_rate": 0.965, "comp_len": 377.4, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 48.0, "frames": {"chat": 318}, "mem_gb": 9.72}
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+[eval step 280] sample: "To find the rank of a matrix, we need to determine the maximum number of linearly independent rows or columns. Let's start by examining the given 4x4 matrix:\n\n\\[\n\\begin{bmatrix}\n12 & -16 & 4 & 16 \\\\\n-"
+{"step": 281, "epoch": 1, "training_mode": "off-policy", "forward_topk_kl": 0.2903117121924957, "tokens": 120000, "cumulative_loss_tokens": 33720000, "grad_norm": 0.482421875, "lr": 3e-05, "finish_rate": 0.956, "comp_len": 444.4, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 43.4, "frames": {"chat": 270}, "mem_gb": 9.81}
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+{"step": 283, "epoch": 1, "training_mode": "off-policy", "forward_topk_kl": 0.5940477982716635, "tokens": 120000, "cumulative_loss_tokens": 33960000, "grad_norm": 0.65234375, "lr": 3e-05, "finish_rate": 0.997, "comp_len": 330.6, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 52.8, "frames": {"chat": 363}, "mem_gb": 9.6}
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+{"step": 285, "epoch": 1, "training_mode": "off-policy", "forward_topk_kl": 0.5244653458673196, "tokens": 120000, "cumulative_loss_tokens": 34200000, "grad_norm": 0.578125, "lr": 3e-05, "finish_rate": 0.997, "comp_len": 371.5, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 49.9, "frames": {"chat": 323}, "mem_gb": 9.77}
+{"step": 286, "epoch": 1, "training_mode": "off-policy", "forward_topk_kl": 0.27399052263411383, "tokens": 120000, "cumulative_loss_tokens": 34320000, "grad_norm": 0.52734375, "lr": 3e-05, "finish_rate": 0.868, "comp_len": 493.8, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 41.7, "frames": {"chat": 243}, "mem_gb": 10.03}
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+{"step": 288, "epoch": 1, "training_mode": "off-policy", "forward_topk_kl": 0.5115833386405992, "tokens": 120000, "cumulative_loss_tokens": 34560000, "grad_norm": 0.58984375, "lr": 3e-05, "finish_rate": 0.988, "comp_len": 346.8, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 50.3, "frames": {"chat": 346}, "mem_gb": 9.94}
+{"step": 289, "epoch": 1, "training_mode": "off-policy", "forward_topk_kl": 0.33575530720731867, "tokens": 120000, "cumulative_loss_tokens": 34680000, "grad_norm": 0.5234375, "lr": 3e-05, "finish_rate": 0.905, "comp_len": 439.6, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 45.1, "frames": {"chat": 273}, "mem_gb": 9.94}
+{"step": 290, "epoch": 1, "training_mode": "off-policy", "forward_topk_kl": 0.34919852755479514, "tokens": 120000, "cumulative_loss_tokens": 34800000, "grad_norm": 0.50390625, "lr": 3e-05, "finish_rate": 0.957, "comp_len": 397.4, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 47.2, "frames": {"chat": 302}, "mem_gb": 9.87}
+[eval step 290] sample: "To find the rank of a matrix, we need to determine the maximum number of linearly independent rows or columns in the matrix. \n\nLet's represent the given matrix as follows:\n\n\\[\nA = \\begin{bmatrix}\n12 &"
+{"step": 291, "epoch": 1, "training_mode": "off-policy", "forward_topk_kl": 0.4919030311459986, "tokens": 120000, "cumulative_loss_tokens": 34920000, "grad_norm": 0.578125, "lr": 3e-05, "finish_rate": 0.97, "comp_len": 356.1, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 47.1, "frames": {"chat": 337}, "mem_gb": 10.0}
+{"step": 292, "epoch": 1, "training_mode": "off-policy", "forward_topk_kl": 0.37939556929959606, "tokens": 120000, "cumulative_loss_tokens": 35040000, "grad_norm": 0.48828125, "lr": 3e-05, "finish_rate": 0.914, "comp_len": 428.6, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 46.0, "frames": {"chat": 280}, "mem_gb": 9.88}
+{"step": 293, "epoch": 1, "training_mode": "off-policy", "forward_topk_kl": 0.6852576129917055, "tokens": 120000, "cumulative_loss_tokens": 35160000, "grad_norm": 0.7265625, "lr": 3e-05, "finish_rate": 0.997, "comp_len": 362.5, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 51.9, "frames": {"chat": 331}, "mem_gb": 9.78}
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+{"step": 295, "epoch": 1, "training_mode": "off-policy", "forward_topk_kl": 0.6771274858971437, "tokens": 120000, "cumulative_loss_tokens": 35400000, "grad_norm": 0.6640625, "lr": 3e-05, "finish_rate": 0.997, "comp_len": 408.2, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 48.8, "frames": {"chat": 294}, "mem_gb": 9.81}
+{"step": 296, "epoch": 1, "training_mode": "off-policy", "forward_topk_kl": 0.3114213637442328, "tokens": 120000, "cumulative_loss_tokens": 35520000, "grad_norm": 0.50390625, "lr": 3e-05, "finish_rate": 0.894, "comp_len": 438.0, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 46.2, "frames": {"chat": 274}, "mem_gb": 9.94}
+{"step": 297, "epoch": 1, "training_mode": "off-policy", "forward_topk_kl": 0.5134605328068448, "tokens": 120000, "cumulative_loss_tokens": 35640000, "grad_norm": 0.640625, "lr": 3e-05, "finish_rate": 0.995, "comp_len": 328.8, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 55.3, "frames": {"chat": 365}, "mem_gb": 9.66}
+{"step": 298, "epoch": 1, "training_mode": "off-policy", "forward_topk_kl": 0.32324541567980003, "tokens": 120000, "cumulative_loss_tokens": 35760000, "grad_norm": 0.478515625, "lr": 3e-05, "finish_rate": 0.972, "comp_len": 371.5, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 49.9, "frames": {"chat": 323}, "mem_gb": 9.73}
+{"step": 299, "epoch": 1, "training_mode": "off-policy", "forward_topk_kl": 0.2253363740356639, "tokens": 120000, "cumulative_loss_tokens": 35880000, "grad_norm": 0.45703125, "lr": 3e-05, "finish_rate": 0.829, "comp_len": 512.8, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 39.0, "frames": {"chat": 234}, "mem_gb": 10.01}
+{"step": 300, "epoch": 1, "training_mode": "off-policy", "forward_topk_kl": 0.28883052630970873, "tokens": 120000, "cumulative_loss_tokens": 36000000, "grad_norm": 0.515625, "lr": 3e-05, "finish_rate": 0.891, "comp_len": 449.4, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 40.7, "frames": {"chat": 267}, "mem_gb": 10.01}
+[eval step 300] sample: 'To compute the rank of a matrix, we need to determine the maximum number of linearly independent rows or columns in the matrix. \n\nGiven the matrix:\n\\[\n\\begin{bmatrix}\n12 & -16 & 4 & 16 \\\\\n-9 & 11 & -1'
+checkpoint snapshot queued -> outputs/healed/calib/glean_keep25_s1224/step0300

healed/calib/glean_keep25_s1224/wandb/run-20260717_223339-i2xsgbr0/files/requirements.txt

@@ -0,0 +1,129 @@
+certifi==2026.6.17
+fonttools==4.63.0
+urllib3==2.7.0
+requests==2.34.2
+nvidia-cublas-cu12==12.6.4.1
+packaging==26.2
+regex==2026.6.28
+portalocker==3.2.0
+safetensors==0.8.0
+datasets==5.0.0
+sqlitedict==2.1.0
+narwhals==2.23.0
+latex2sympy2_extended==1.11.0
+smmap==5.0.3
+colorama==0.4.6
+anyio==4.14.1
+cycler==0.12.1
+pytablewriter==1.2.1
+pydantic_core==2.46.4
+huggingface_hub==0.36.2
+charset-normalizer==3.4.8
+nvidia-cusolver-cu12==11.7.1.2
+joblib==1.5.3
+typing-inspection==0.4.2
+wandb==0.28.0
+bitsandbytes==0.49.2
+kiwisolver==1.5.0
+cuda-toolkit==12.6.3
+propcache==0.5.2
+Jinja2==3.1.6
+cuda-bindings==12.9.7
+pluggy==1.6.0
+pytest==9.1.1
+fsspec==2026.4.0
+tqdm==4.68.3
+psutil==7.2.2
+pydantic==2.13.4
+typepy==1.3.5
+xxhash==3.8.1
+DataProperty==1.1.1
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healed/calib/glean_keep50_s1224/wandb/run-20260717_194504-jkxl7c7u/files/output.log

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+Loading checkpoint shards: 100%|██████████| 2/2 [00:03<00:00,  1.56s/it]
+58360 cached top-128 chat trajectories / 22,295,631 unique tokens | 185 steps/epoch | 100 total steps | student params 3.70B | teacher overlap=False
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+The attention mask is not set and cannot be inferred from input because pad token is same as eos token. As a consequence, you may observe unexpected behavior. Please pass your input's `attention_mask` to obtain reliable results.
+[eval step 1] sample: "To compute the rank of a given 4x4 matrix, we determine the maximum number of linearly independent rows or columns. Let's start by writing the matrix:\n\n\\[ A = \\begin{bmatrix}\n12 & -16 & 4 & 16 \\\\\n-9 &"
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+[eval step 10] sample: "To determine the rank of a matrix, we need to find the maximum number of linearly independent rows or columns. Let's analyze the given matrix:\n\n\\[\n\\begin{bmatrix}\n12 & -16 & 4 & 16 \\\\\n-9 & 11 & -1 & -"
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+[eval step 20] sample: "To determine the rank of a matrix, we need to find the maximum number of linearly independent rows or columns. Let's start by reducing the matrix to row echelon form (REF) or row reduced row echelon f"
+{"step": 21, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.26712346399153275, "tokens": 120000, "cumulative_loss_tokens": 2520000, "grad_norm": 0.60546875, "lr": 3e-05, "finish_rate": 0.963, "comp_len": 402.7, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 49.5, "frames": {"chat": 298}, "mem_gb": 15.85}
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+[eval step 30] sample: "To determine the rank of a matrix, we need to find the maximum number of linearly independent rows or columns. Let's analyze the given matrix:\n\n\\[\n\\begin{bmatrix}\n12 & -16 & 4 & 16 \\\\\n-9 & 11 & -1 & -"
+{"step": 31, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.0853212530775306, "tokens": 120000, "cumulative_loss_tokens": 3720000, "grad_norm": 0.51953125, "lr": 3e-05, "finish_rate": 0.891, "comp_len": 521.7, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 40.0, "frames": {"chat": 230}, "mem_gb": 15.88}
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+{"step": 40, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.24978787150296072, "tokens": 120000, "cumulative_loss_tokens": 4800000, "grad_norm": 0.5859375, "lr": 3e-05, "finish_rate": 0.957, "comp_len": 364.7, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 49.7, "frames": {"chat": 329}, "mem_gb": 15.87}
+[eval step 40] sample: "To compute the rank of a matrix, we need to determine the maximum number of linearly independent rows or columns in the matrix. Here's how we can do it step-by-step:\n\n1. **Formulate the Matrix:**\n   \\"
+{"step": 41, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.23689508349557098, "tokens": 120000, "cumulative_loss_tokens": 4920000, "grad_norm": 0.5546875, "lr": 3e-05, "finish_rate": 0.905, "comp_len": 456.3, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 46.8, "frames": {"chat": 263}, "mem_gb": 15.99}
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+{"step": 49, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.26727397156863786, "tokens": 120000, "cumulative_loss_tokens": 5880000, "grad_norm": 0.5390625, "lr": 3e-05, "finish_rate": 0.876, "comp_len": 463.3, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 44.5, "frames": {"chat": 259}, "mem_gb": 16.05}
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+[eval step 50] sample: 'To compute the rank of a matrix, we need to determine the maximum number of linearly independent rows or columns. In other words, we need to find the largest subset of rows or columns such that no row'
+{"step": 51, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.2825841818386378, "tokens": 120000, "cumulative_loss_tokens": 6120000, "grad_norm": 0.6328125, "lr": 3e-05, "finish_rate": 0.946, "comp_len": 402.7, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 51.0, "frames": {"chat": 298}, "mem_gb": 16.01}
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+[eval step 60] sample: 'To find the rank of a matrix, we need to determine the maximum number of linearly independent rows or columns. In this 4x4 matrix:\n\n\\[\n\\begin{bmatrix}\n12 & -16 & 4 & 16 \\\\\n-9 & 11 & -1 & -10 \\\\\n0 & 1'
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+[eval step 100] sample: 'To compute the rank of a matrix, we need to determine the maximum number of linearly independent rows or columns in the matrix. \n\nGiven the matrix:\n\n\\[\n\\begin{bmatrix}\n12 & -16 & 4 & 16 \\\\\n-9 & 11 & -'
+checkpoint snapshot queued -> outputs/healed/calib/glean_keep50_s1224/step0100

healed/calib/glean_keep50_s1224/wandb/run-20260717_194504-jkxl7c7u/files/wandb-metadata.json

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healed/calib/glean_keep50_s1224/wandb/run-20260717_194504-jkxl7c7u/files/wandb-summary.json

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healed/calib/glean_keep50_s1224/wandb/run-20260717_194504-jkxl7c7u/logs/debug.log

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healed/calib/glean_keep50_s1224/wandb/run-20260717_211428-xluvahmg/files/config.yaml

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healed/calib/glean_keep50_s1224/wandb/run-20260717_211428-xluvahmg/files/output.log

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+Loading checkpoint shards: 100%|██████████| 2/2 [00:00<00:00,  7.73it/s]
+resumed student weights from outputs/healed/calib/glean_keep50_s1224/step0100 (fresh optimizer, step counter at 0)
+58360 cached top-128 chat trajectories / 22,295,631 unique tokens | 185 steps/epoch | 200 total steps | student params 3.70B | teacher overlap=False
+restored optimizer/scheduler state from step 100; rebuilt 256 paged buffers
+{"step": 101, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.32495053631433596, "tokens": 120000, "cumulative_loss_tokens": 12120000, "grad_norm": 0.578125, "lr": 3e-05, "finish_rate": 0.994, "comp_len": 343.8, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 57.7, "frames": {"chat": 349}, "mem_gb": 15.61}
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+{"step": 105, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.3787672108503369, "tokens": 120000, "cumulative_loss_tokens": 12600000, "grad_norm": 0.63671875, "lr": 3e-05, "finish_rate": 0.997, "comp_len": 341.9, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 54.8, "frames": {"chat": 351}, "mem_gb": 15.7}
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+{"step": 109, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.19803059833155323, "tokens": 120000, "cumulative_loss_tokens": 13080000, "grad_norm": 0.50390625, "lr": 3e-05, "finish_rate": 0.893, "comp_len": 442.8, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 43.5, "frames": {"chat": 271}, "mem_gb": 16.07}
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+The attention mask is not set and cannot be inferred from input because pad token is same as eos token. As a consequence, you may observe unexpected behavior. Please pass your input's `attention_mask` to obtain reliable results.
+[eval step 110] sample: 'To compute the rank of a matrix, we need to determine the maximum number of linearly independent rows or columns in the matrix. A matrix is said to be of rank \\( r \\) if it has \\( r \\) linearly indepe'
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+{"step": 115, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.37353234655279666, "tokens": 120000, "cumulative_loss_tokens": 13800000, "grad_norm": 0.63671875, "lr": 3e-05, "finish_rate": 0.997, "comp_len": 371.5, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 51.6, "frames": {"chat": 323}, "mem_gb": 15.9}
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+{"step": 117, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.36079281497399013, "tokens": 120000, "cumulative_loss_tokens": 14040000, "grad_norm": 0.58984375, "lr": 3e-05, "finish_rate": 0.975, "comp_len": 377.4, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 49.8, "frames": {"chat": 318}, "mem_gb": 15.79}
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+{"step": 119, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.3330037896766638, "tokens": 120000, "cumulative_loss_tokens": 14280000, "grad_norm": 0.5859375, "lr": 3e-05, "finish_rate": 0.997, "comp_len": 347.8, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 55.8, "frames": {"chat": 345}, "mem_gb": 15.72}
+{"step": 120, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.3271082468834085, "tokens": 120000, "cumulative_loss_tokens": 14400000, "grad_norm": 0.61328125, "lr": 3e-05, "finish_rate": 0.992, "comp_len": 318.3, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 57.2, "frames": {"chat": 377}, "mem_gb": 15.86}
+[eval step 120] sample: 'To compute the rank of a matrix, we need to determine the maximum number of linearly independent rows or columns in the matrix. \n\nGiven the matrix:\n\\[\n\\begin{bmatrix}\n12 & -16 & 4 & 16 \\\\\n-9 & 11 & -1'
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+{"step": 123, "epoch": 0, "training_mode": "off-policy", "forward_topk_kl": 0.38646569627601035, "tokens": 120000, "cumulative_loss_tokens": 14760000, "grad_norm": 0.6171875, "lr": 3e-05, "finish_rate": 0.997, "comp_len": 340.9, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 56.7, "frames": {"chat": 352}, "mem_gb": 15.68}
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+[eval step 130] sample: "To compute the rank of a matrix, we need to determine the maximum number of linearly independent rows or columns in the matrix. \n\nLet's represent the given matrix as \\( A \\):\n\n\\[ A = \\begin{bmatrix}\n1"
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+[eval step 140] sample: "To compute the rank of a matrix, we need to determine the maximum number of linearly independent rows or columns in the matrix. \n\nLet's represent the given matrix \\( A \\) as follows:\n\\[ A = \\begin{bma"
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+[eval step 150] sample: "To compute the rank of a matrix, we need to determine the maximum number of linearly independent rows or columns in the matrix. \n\nLet's break down the steps:\n\n1. **Formulate the Matrix:**\n   \\[\n   A ="
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+[eval step 160] sample: 'To find the rank of a matrix, we need to determine the maximum number of linearly independent rows or columns in the matrix. \n\nGiven the matrix:\n\\[\n\\begin{bmatrix}\n12 & -16 & 4 & 16 \\\\\n-9 & 11 & -1 & '
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+[eval step 170] sample: 'To compute the rank of a matrix, we need to determine the maximum number of linearly independent rows or columns in the matrix. \n\nGiven the matrix:\n\n\\[\n\\begin{bmatrix}\n12 & -16 & 4 & 16 \\\\\n-9 & 11 & -'
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+checkpoint snapshot queued -> outputs/healed/calib/glean_keep50_s1224/step0200

healed/calib/glean_keep50_s1224/wandb/run-20260717_211428-xluvahmg/files/requirements.txt

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healed/calib/glean_keep50_s1224/wandb/run-20260717_211428-xluvahmg/files/wandb-metadata.json

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healed/calib/glean_keep50_s1224/wandb/run-20260717_211428-xluvahmg/logs/debug.log

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healed/calib/glean_keep50_s1224/wandb/run-20260718_001144-yv1sfljs/files/output.log

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+The attention mask is not set and cannot be inferred from input because pad token is same as eos token. As a consequence, you may observe unexpected behavior. Please pass your input's `attention_mask` to obtain reliable results.
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+{"step": 320, "epoch": 1, "training_mode": "off-policy", "forward_topk_kl": 0.14590722516266008, "tokens": 120000, "cumulative_loss_tokens": 38400000, "grad_norm": 0.365234375, "lr": 3e-05, "finish_rate": 0.976, "comp_len": 363.6, "t_data_s": 0.0, "t_rollout_s": 0.0, "t_step_s": 52.0, "frames": {"chat": 330}, "mem_gb": 16.01}
+[eval step 320] sample: 'To find the rank of a matrix, we need to determine the maximum number of linearly independent rows or columns in the matrix. \n\nGiven the matrix:\n\\[\n\\begin{bmatrix}\n12 & -16 & 4 & 16 \\\\\n-9 & 11 & -1 & '
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+[eval step 330] sample: 'To find the rank of a matrix, we need to determine the maximum number of linearly independent rows or columns in the matrix. \n\nGiven the matrix:\n\\[\n\\begin{bmatrix}\n12 & -16 & 4 & 16 \\\\\n-9 & 11 & -1 & '

healed/calib/glean_keep50_s1224/wandb/run-20260718_001144-yv1sfljs/files/requirements.txt

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healed/calib/glean_keep50_s1224/wandb/run-20260718_001144-yv1sfljs/logs/debug.log

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