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README.md

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+---
+base_model: unsloth/Llama-3.2-1B-Instruct
+library_name: peft
+pipeline_tag: text-generation
+tags:
+- latent-reasoning
+- colar
+- research
+---
+# colar-coding-l1b
+
+single-track CoLaR, coding 域(符号执行/coding_mix), warm-start 自 colar-gsm, compress=5
+
+- **Base model:** `unsloth/Llama-3.2-1B-Instruct`
+- **Files:** `colar_coding.ckpt`, `hparams.yaml`
+
+## Loading (PyTorch-Lightning checkpoint — NOT AutoModel-loadable)
+Weights live under the top-level key `['state_dict']` and only fit the custom CoLaR scaffold (base LLM + `[PAD]` resize + r128 q/v LoRA + a `LatentPolicy` MLP), loaded `strict=False`. Load the base separately and splice this `state_dict` in. Runtime env:
+```
+COLAR_BASE=<base>  COLAR_CKPT=colar-gsm/colar_best.ckpt  COLAR_EMB_STD=0.018  COLAR_COMPRESS=5  COLAR_MAXLAT=64  TORCH_FORCE_NO_WEIGHTS_ONLY_LOAD=1
+```
+`TORCH_FORCE_NO_WEIGHTS_ONLY_LOAD=1` is required for these older Lightning ckpts.

colar_coding.ckpt

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+version https://git-lfs.github.com/spec/v1
+oid sha256:e03131e8382eba1670857eec1979c89225c439b62160e6fdba2b39e7aedb0af2
+size 121741061

hparams.yaml

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+model_kwargs:
+  model_id: Llama-3.2-1B-Instruct
+  sft_method: colar
+  chat_template: false
+  do_lora: true
+  lora_config:
+    r: 128
+    lora_alpha: 32
+  latent_cot_config:
+    ce_weight: 1
+    embed_modeling_weight: 1
+    embed_modeling_loss: mse
+    entropy_weight: 0
+    pred_embed_forward_weight: 0
+    max_compression_factor: 5
+    pred_compressed_cot: true
+    sqrt_mean: true
+  latent_policy_config:
+    lp_determinisitc: false
+    lp_intermediate_size: 2048
+  latent_generation_config:
+    max_n_latent_forward: 64
+    latent_temperature: 1.0
+    compression_factor: 5
+  answer_generation_config:
+    max_new_tokens: 16
+    do_sample: true
+    top_p: 0.9
+    temperature: 1.0
+  do_rl: false
+  rl_config:
+    average_per_token_loss: false
+    random_speed_in_group: false
+    filter_dataset: false
+    exp_batch_size: 8
+    group_size: 8
+    punish_latent_length: false
+    clip_grad_norm: 1.0
+    clip_eps: 0.2
+    use_latent_loss: true
+    use_answer_loss: true
+    n_train_samples_per_epoch: 512
+training_kwargs:
+  optimizer:
+    target: torch.optim.AdamW
+    lr: 0.0001
+    weight_decay: 0.01
+  use_scheduler: false
+  scheduler:
+    target: constant_schedule_with_warmup
+    warmup_steps: 1000
+all_config:
+  trainer:
+    target: lightning.pytorch.trainer.Trainer
+    devices:
+    - 0
+    max_steps: -1
+    check_val_every_n_epoch: 5
+    log_every_n_steps: 10
+    num_sanity_val_steps: 2
+    gradient_clip_val: 1.0
+    reload_dataloaders_every_n_epochs: 0
+    accumulate_grad_batches: 4
+    precision: bf16-mixed
+    use_distributed_sampler: true
+    strategy: auto
+    logger:
+      target: lightning.pytorch.loggers.TensorBoardLogger
+      save_dir: logs/colar
+      name: qsa-coding_mix
+      version: 20260802-200100_983467_coding_gsmwarm
+    max_epochs: 25
+  callbacks:
+  - target: lightning.pytorch.callbacks.ModelCheckpoint
+    save_last: true
+    save_top_k: 3
+    mode: max
+    monitor: monitor
+    auto_insert_metric_name: false
+    filename: epoch{epoch}__step{step}__monitor{monitor:.3f}
+    save_weights_only: true
+  seed: null
+  model:
+    target: src.models.colar.LitCoLaR
+    model_kwargs:
+      model_id: Llama-3.2-1B-Instruct
+      sft_method: colar
+      chat_template: false
+      do_lora: true
+      lora_config:
+        r: 128
+        lora_alpha: 32
+      latent_cot_config:
+        ce_weight: 1
+        embed_modeling_weight: 1
+        embed_modeling_loss: mse
+        entropy_weight: 0
+        pred_embed_forward_weight: 0
+        max_compression_factor: 5
+        pred_compressed_cot: true
+        sqrt_mean: true
+      latent_policy_config:
+        lp_determinisitc: false
+        lp_intermediate_size: 2048
+      latent_generation_config:
+        max_n_latent_forward: 64
+        latent_temperature: 1.0
+        compression_factor: 5
+      answer_generation_config:
+        max_new_tokens: 16
+        do_sample: true
+        top_p: 0.9
+        temperature: 1.0
+      do_rl: false
+      rl_config:
+        average_per_token_loss: false
+        random_speed_in_group: false
+        filter_dataset: false
+        exp_batch_size: 8
+        group_size: 8
+        punish_latent_length: false
+        clip_grad_norm: 1.0
+        clip_eps: 0.2
+        use_latent_loss: true
+        use_answer_loss: true
+        n_train_samples_per_epoch: 512
+    training_kwargs:
+      optimizer:
+        target: torch.optim.AdamW
+        lr: 0.0001
+        weight_decay: 0.01
+      use_scheduler: false
+      scheduler:
+        target: constant_schedule_with_warmup
+        warmup_steps: 1000
+  dataloader:
+    batch_size: 4
+    val_batch_size: 32
+    num_workers: 8
+    pin_memory: true
+    persistent_workers: true
+  data_module:
+    target: src.datasets.qsa.QSADataModule
+    dataset_name: coding_mix
+    tiny_dataset: false
+    epoch_scaling: 1
+  args:
+    model: colar
+    dataset: qsa
+    trainer: default
+    devices: '0'
+    no_log: false
+    log_suffix: coding_gsmwarm
+    resume_ckpt_path: null
+    load_ckpt_path: /content/colar_hf/logs/colar/qsa-gsm/colar-final/checkpoints/colar_best.ckpt
+    workspace_path: /content/ws
+    do_test: false
+    test_ckpt_path: ''
+    test_times: 5
+    seed: 0
+  unkown_args:
+    dataset_name: coding_mix
+    model_id: Llama-3.2-1B-Instruct
+    batch_size: '4'
+    accumulate_grad_batches: '4'
+    max_epochs: '25'
+    check_val_every_n_epoch: '5'