Upload avg_50m_k2_learnable: 6 file(s)

README.md

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+# FAIRC/token-averaging-avg_50m_k2_learnable
+
+Checkpoint dump from the **token averaging** research project.
+
+- **run name:** `avg_50m_k2_learnable`
+- **results tree:** `results`
+
+## Contents
+
+### Loss logs
+
+- `loss_log.csv`
+
+### Checkpoints
+
+- `checkpoints/final.pt`
+- `checkpoints/step_00050000.pt`
+- `checkpoints/step_00100000.pt`
+
+## Loading a checkpoint
+
+```python
+import torch
+from huggingface_hub import hf_hub_download
+
+path = hf_hub_download('FAIRC/token-averaging-avg_50m_k2_learnable', 'checkpoints/final.pt')
+state = torch.load(path, map_location='cpu', weights_only=False)
+model.load_state_dict(state['model'])  # your OLMAveraged / OLMTransformerBody
+print(state['step'], state['tokens_seen'], state['cumulative_flops'])
+```
+
+These are **not** Hugging Face `transformers` weights. Rebuild the
+architecture from `config.json` → `model_config` (or from
+`experiments/chinchilla/model_configs.py` in the source repo) and load
+the raw `state_dict`.
+
+## Architecture
+
+```json
+{
+  "d_model": 512,
+  "n_heads": 8,
+  "n_layers": 8,
+  "context_len": 1024,
+  "averaging_k": 2,
+  "tie_embeddings": true,
+  "method_name": "learnable_k2",
+  "lr": 0.0002,
+  "warmup_steps": 2000,
+  "target_tokens": 2000000000,
+  "n_params_approx": 50897408
+}
+```

checkpoints/final.pt

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+version https://git-lfs.github.com/spec/v1
+oid sha256:9b887bcb4e3709e29488549ab01ec3b3315d637d0a5f3597c43c7eb24a318f06
+size 599163482

checkpoints/step_00050000.pt

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+version https://git-lfs.github.com/spec/v1
+oid sha256:280179a8530d623541b18ea03dba12bc1ed3367a750eb983c8283a2259f1eeb2
+size 599193114

checkpoints/step_00100000.pt

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+version https://git-lfs.github.com/spec/v1
+oid sha256:32c73d5457ee257d259a14c10229cb30bc9b0319d1682049c161329ec977acab
+size 599193114

config.json

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+{
+  "run_name": "avg_50m_k2_learnable",
+  "results_tag": "canonical",
+  "hf_repo_id": "FAIRC/token-averaging-avg_50m_k2_learnable",
+  "checkpoint_format": {
+    "keys": [
+      "step",
+      "tokens_seen",
+      "cumulative_flops",
+      "model",
+      "optimizer",
+      "scheduler"
+    ],
+    "note": "Raw torch.save dict from experiments/chinchilla/train.py. Load with torch.load(..., map_location='cpu', weights_only=False) and take state['model']. Not a transformers AutoModel checkpoint."
+  },
+  "model_config": {
+    "name": "avg_50m_k2_learnable",
+    "d_model": 512,
+    "n_heads": 8,
+    "n_layers": 8,
+    "context_len": 1024,
+    "averaging_k": 2,
+    "method_name": "learnable_k2",
+    "multi_token_phase_ratio": 0.0,
+    "tie_embeddings": true,
+    "grad_checkpoint": false,
+    "color": "#e67e22",
+    "label": "~50M k=2 learnable pooling",
+    "target_tokens": 2000000000,
+    "lr": 0.0002,
+    "warmup_steps": 2000,
+    "n_params_approx": 50897408
+  }
+}

loss_log.csv

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