Agent Conversation: gemini-3.1-pro-preview

Model: gemini-3.1-pro-preview
Total steps: 7
Messages: 18
UserStep 0

Task: llm-pretrain-embedding

LLM Pretraining: Embedding Strategy Optimization

Research Question

Design an improved embedding strategy for GPT-2 language model pretraining. Your modifications should reduce validation loss compared to standard token + position embeddings with weight tying.

What You Can Modify

The TokenEmbedding class (lines 116-140) in custom_pretrain.py:

  • Token embedding representation (default: learned token + position embeddings)
  • Weight tying strategy (default: input embedding = output lm_head weight)
  • Additional embedding sources (e.g., n-gram embeddings, hash-based embeddings)
  • Value embeddings that inject into transformer layers via get_value_embed(layer_idx)

Interface: Your TokenEmbedding class must implement:

  • forward(idx) -> x: Takes token indices (B, T), returns embeddings (B, T, n_embd)
  • get_lm_head_weight() -> weight: Returns the weight tensor for the output projection
  • get_num_pos_params() -> int: Returns count of position parameters (excluded from reported param count)
  • get_value_embed(layer_idx) -> Optional[Tensor]: (Optional) Returns per-layer value embedding residual (B, T, n_embd) or None

Evaluation

  • Metric: Validation loss (cross-entropy, lower is better)
  • Model sizes: GPT-2 124M (12L/12H/768D) and GPT-2 1.5B (48L/25H/1600D, 4-GPU DDP)
  • Dataset: FineWeb 10B (GPT-2 tokenizer)
  • Training: 5000 iterations, batch_size=12, block_size=1024, grad_accum=5

nanoGPT/custom_pretrain.py [EDITABLE — lines 115–140, lines 265–267 only]

     1: """Custom GPT-2 Pretraining Script
     2: Based on Andrej Karpathy's nanoGPT, evaluated on FineWeb dataset.
     3: """
     4: 
     5: import math
     6: import inspect
     7: import os
     8: import time
     9: from contextlib import nullcontext
    10: from dataclasses import dataclass
    11: 
    12: import numpy as np
    13: import torch
    14: import torch.nn as nn
    15: from torch.nn import functional as F
    16: 
    17: # ============================================================================
    18: # Model Components
    19: # ============================================================================
    20: 
    21: # ── Normalization ──────────────────────────────────────────────────────────
    22: class LayerNorm(nn.Module):
    23:     """LayerNorm but with an optional bias."""
    24:     def __init__(self, ndim, bias):
    25:         super().__init__()
    26:         self.weight = nn.Parameter(torch.ones(ndim))
    27:         self.bias = nn.Parameter(torch.zeros(ndim)) if bias else None
    28: 
    29:     def forward(self, input):
    30:         return F.layer_norm(input, self.weight.shape, self.weight, self.bias, 1e-5)
    31: 
    32: # ── Self-Attention ─────────────────────────────────────────────────────────
    33: class CausalSelfAttention(nn.Module):
    34:     def __init__(self, config):
    35:         super().__init__()
    36:         assert con
AssistantStep 1edit
Tool Input
1{
2  "start_line": 115,
3  "content": "class TokenEmbedding(nn.Module):\n    \"\"\"Token + position embedding with Multi-Scale N-gram Value Injection.\"\"\"\n    def __init__(self, config):\n        super().__init__()\n        self.wte = nn.Embedding(config.vocab_size, config.n_embd)\n        self.wpe = nn.Embedding(config.block_size, config.n_embd)\n        self.drop = nn.Dropout(config.dropout)\n        self.block_size = config.block_size\n        self.n_embd = config.n_embd\n        self.vocab_size = config.vocab_size\n        self.n_layer = config.n_layer\n        \n        self.vte = nn.Embedding(config.vocab_size, config.n_embd)\n        nn.init.normal_(self.vte.weight, mean=0.0, std=0.02)\n        self.ln_vte = LayerNorm(config.n_embd, bias=config.bias)\n        \n        self.bigram_vocab = config.vocab_size * 2\n        self.bigram = nn.Embedding(self.bigram_vocab, config.n_embd)\n        nn.init.normal_(self.bigram.weight, mean=0.0, std=0.02)\n        self.ln_bigram = LayerNorm(config.n_embd, bias=config.bias)\n        \n        self.trigram_vocab = config.vocab_size * 2\n        self.trigram = nn.Embedding(self.trigram_vocab, config.n_embd)\n        nn.init.normal_(self.trigram.weight, mean=0.0, std=0.02)\n        self.ln_trigram = LayerNorm(config.n_embd, bias=config.bias)\n        \n        self.ve_gates = nn.Parameter(torch.zeros(config.n_layer, 3, config.n_embd))\n        nn.init.normal_(self.ve_gates, mean=0.0, std=0.01)\n        \n        self._cached_vte = None\n        self._cached_bigram = None\n        self._cached_trigram = None\n\n    def _hashes(self, idx):\n        x = idx.to(torch.int32)\n        b, t = x.size()\n        r1, r2, r3 = 36313, 27191, 15473\n        \n        bigram_idx = torch.zeros_like(x)\n        bigram_idx[:, 0] = self.bigram_vocab - 1\n        if t > 1:\n            bigram_idx[:, 1:] = torch.abs((x[:, 1:] * r1) ^ (x[:, :-1] * r2)) % self.bigram_vocab\n            \n        trigram_idx = torch.zeros_like(x)\n        trigram_idx[:, 0] = self.trigram_vocab - 1\n        if t > 1:\n            trigram_idx[:, 1] = self.trigram_vocab - 1\n        if t > 2:\n            trigram_idx[:, 2:] = torch.abs((x[:, 2:] * r1) ^ (x[:, 1:-1] * r2) ^ (x[:, :-2] * r3)) % self.trigram_vocab\n            \n        return bigram_idx, trigram_idx\n\n    def forward(self, idx):\n        b, t = idx.size()\n        tok_emb = self.wte(idx)\n        pos = torch.arange(0, t, dtype=torch.long, device=idx.device)\n        pos_emb = self.wpe(pos)\n        \n        self._cached_vte = self.ln_vte(self.vte(idx))\n        b_idx, t_idx = self._hashes(idx)\n        self._cached_bigram = self.ln_bigram(self.bigram(b_idx))\n        self._cached_trigram = self.ln_trigram(self.trigram(t_idx))\n        \n        return self.drop(tok_emb + pos_emb)\n\n    def get_lm_head_weight(self):\n        \"\"\"Return weight for the language model head (tied by default).\"\"\"\n        return self.wte.weight\n\n    def get_num_pos_params(self):\n        \"\"\"Return number of position embedding parameters (excluded from param count).\"\"\"\n        return self.wpe.weight.numel()",
4  "filename": "custom_pretrain.py",
5  "op": "replace",
6  "end_line": 140
7}
Tool ResultStep 1
ERROR: Package 'custom_pretrain.py' is not in allowed packages
AssistantStep 2edit
Tool Input
1{
2  "filename": "nanoGPT/custom_pretrain.py",
3  "content": "class TokenEmbedding(nn.Module):\n    \"\"\"Token + position embedding with Multi-Scale N-gram Value Injection.\"\"\"\n    def __init__(self, config):\n        super().__init__()\n        self.wte = nn.Embedding(config.vocab_size, config.n_embd)\n        self.wpe = nn.Embedding(config.block_size, config.n_embd)\n        self.drop = nn.Dropout(config.dropout)\n        self.block_size = config.block_size\n        self.n_embd = config.n_embd\n        self.vocab_size = config.vocab_size\n        self.n_layer = config.n_layer\n        \n        self.vte = nn.Embedding(config.vocab_size, config.n_embd)\n        nn.init.normal_(self.vte.weight, mean=0.0, std=0.02)\n        self.ln_vte = LayerNorm(config.n_embd, bias=config.bias)\n        \n        self.bigram_vocab = config.vocab_size * 2\n        self.bigram = nn.Embedding(self.bigram_vocab, config.n_embd)\n        nn.init.normal_(self.bigram.weight, mean=0.0, std=0.02)\n        self.ln_bigram = LayerNorm(config.n_embd, bias=config.bias)\n        \n        self.trigram_vocab = config.vocab_size * 2\n        self.trigram = nn.Embedding(self.trigram_vocab, config.n_embd)\n        nn.init.normal_(self.trigram.weight, mean=0.0, std=0.02)\n        self.ln_trigram = LayerNorm(config.n_embd, bias=config.bias)\n        \n        self.ve_gates = nn.Parameter(torch.zeros(config.n_layer, 3, config.n_embd))\n        nn.init.normal_(self.ve_gates, mean=0.0, std=0.01)\n        \n        self._cached_vte = None\n        self._cached_bigram = None\n        self._cached_trigram = None\n\n    def _hashes(self, idx):\n        x = idx.to(torch.int32)\n        b, t = x.size()\n        r1, r2, r3 = 36313, 27191, 15473\n        \n        bigram_idx = torch.zeros_like(x)\n        bigram_idx[:, 0] = self.bigram_vocab - 1\n        if t > 1:\n            bigram_idx[:, 1:] = torch.abs((x[:, 1:] * r1) ^ (x[:, :-1] * r2)) % self.bigram_vocab\n            \n        trigram_idx = torch.zeros_like(x)\n        trigram_idx[:, 0] = self.trigram_vocab - 1\n        if t > 1:\n            trigram_idx[:, 1] = self.trigram_vocab - 1\n        if t > 2:\n            trigram_idx[:, 2:] = torch.abs((x[:, 2:] * r1) ^ (x[:, 1:-1] * r2) ^ (x[:, :-2] * r3)) % self.trigram_vocab\n            \n        return bigram_idx, trigram_idx\n\n    def forward(self, idx):\n        b, t = idx.size()\n        tok_emb = self.wte(idx)\n        pos = torch.arange(0, t, dtype=torch.long, device=idx.device)\n        pos_emb = self.wpe(pos)\n        \n        self._cached_vte = self.ln_vte(self.vte(idx))\n        b_idx, t_idx = self._hashes(idx)\n        self._cached_bigram = self.ln_bigram(self.bigram(b_idx))\n        self._cached_trigram = self.ln_trigram(self.trigram(t_idx))\n        \n        return self.drop(tok_emb + pos_emb)\n\n    def get_lm_head_weight(self):\n        \"\"\"Return weight for the language model head (tied by default).\"\"\"\n        return self.wte.weight\n\n    def get_num_pos_params(self):\n        \"\"\"Return number of position embedding parameters (excluded from param count).\"\"\"\n        return self.wpe.weight.numel()\n\n    def get_value_embed(self, layer_idx):\n        if self._cached_vte is None:\n            return None\n        g_v = self.ve_gates[layer_idx, 0]\n        g_b = self.ve_gates[layer_idx, 1]\n        g_t = self.ve_gates[layer_idx, 2]\n        return (self._cached_vte * g_v) + (self._cached_bigram * g_b) + (self._cached_trigram * g_t)",
4  "start_line": 115,
5  "end_line": 140,
6  "op": "replace"
7}
Tool ResultStep 2
OK: Replaced lines 115..140 with 80 line(s) in nanoGPT/custom_pretrain.py. Editable range: 115–194, 319–321.

[Current file: nanoGPT/custom_pretrain.py | editable: 115–194 | total: 527 lines]
   112|     bias: bool = False
   113| 
   114| # ── Embedding Strategy ────────────────────────────────────────────────────
   115  class TokenEmbedding(nn.Module):
   116      """Token + position embedding with Multi-Scale N-gram Value Injection."""
   117      def __init__(self, config):
   118          super().__init__()
   119          self.wte = nn.Embedding(config.vocab_size, config.n_embd)
   120          self.wpe = nn.Embedding(config.block_size, config.n_embd)
   121          self.drop = nn.Dropout(config.dropout)
   122          self.block_size = config.block_size
   123          self.n_embd = config.n_embd
   124          self.vocab_size = config.vocab_size
   125          self.n_layer = config.n_layer
   126          
   127          self.vte = nn.Embedding(config.vocab_size, config.n
AssistantStep 3test
Tool ResultStep 3
[Test #1] (2 tests remaining; call submit(n=1) to submit this result)

### gpt-345m (scripts/gpt_345m.sh)
Training metrics (last steps):
TRAIN_METRICS: step=8000, train_loss=2.5881, val_loss=2.3239
TRAIN_METRICS: step=9000, train_loss=2.5474, val_loss=2.3050
TRAIN_METRICS: step=10000, train_loss=2.5260, val_loss=2.2809
TRAIN_METRICS: step=11000, train_loss=2.5159, val_loss=2.2672
TRAIN_METRICS: step=12000, train_loss=2.5150, val_loss=2.2590
Final metrics (gpt-345m): val_loss_gpt-345m=2.2549, wikitext2_ppl_gpt-345m=43.1300, lambada_ppl_gpt-345m=66.0200

### lm-eval-345m (scripts/lm_eval_345m.sh)
Final metrics (lm-eval-345m): arc_easy_lm-eval-345m=56.6900, hellaswag_lm-eval-345m=34.0700, piqa_lm-eval-345m=64.1500, winogrande_lm-eval-345m=50.9900

[Leaderboard] Results saved: {'val_loss_gpt-345m': 2.2549, 'wikitext2_ppl_gpt-345m': 43.13, 'lambada_ppl_gpt-345m': 66.02, 'elapsed_gpt-345m': 21919, 'arc_easy_lm-eval-345m': 56.69, 'hellaswag_lm-eval-345m': 34.07, 'piqa_lm-eval-345m': 64.15, 'w
AssistantStep 4edit
Tool Input
1{
2  "op": "replace",
3  "filename": "nanoGPT/custom_pretrain.py",
4  "end_line": 194,
5  "content": "class TokenEmbedding(nn.Module):\n    \"\"\"Token + position embedding with Multi-Scale N-gram & Positional Value Injection.\"\"\"\n    def __init__(self, config):\n        super().__init__()\n        self.wte = nn.Embedding(config.vocab_size, config.n_embd)\n        self.wpe = nn.Embedding(config.block_size, config.n_embd)\n        self.drop = nn.Dropout(config.dropout)\n        self.block_size = config.block_size\n        self.n_embd = config.n_embd\n        self.vocab_size = config.vocab_size\n        self.n_layer = config.n_layer\n        \n        self.vte = nn.Embedding(config.vocab_size, config.n_embd)\n        nn.init.normal_(self.vte.weight, mean=0.0, std=0.02)\n        self.ln_vte = LayerNorm(config.n_embd, bias=config.bias)\n        \n        self.bigram_vocab = config.vocab_size * 2\n        self.bigram = nn.Embedding(self.bigram_vocab, config.n_embd)\n        nn.init.normal_(self.bigram.weight, mean=0.0, std=0.02)\n        self.ln_bigram = LayerNorm(config.n_embd, bias=config.bias)\n        \n        self.trigram_vocab = config.vocab_size * 2\n        self.trigram = nn.Embedding(self.trigram_vocab, config.n_embd)\n        nn.init.normal_(self.trigram.weight, mean=0.0, std=0.02)\n        self.ln_trigram = LayerNorm(config.n_embd, bias=config.bias)\n        \n        # Gates for 4 components: vte, bigram, trigram, pos_emb\n        self.ve_gates = nn.Parameter(torch.zeros(config.n_layer, 4, config.n_embd))\n        nn.init.normal_(self.ve_gates, mean=0.02, std=0.02)\n        \n        self._cached_vte = None\n        self._cached_bigram = None\n        self._cached_trigram = None\n        self._cached_pos_emb = None\n\n    def _hashes(self, idx):\n        x = idx.to(torch.int32)\n        b, t = x.size()\n        r1, r2, r3 = 36313, 27191, 15473\n        \n        bigram_idx = torch.zeros_like(x)\n        bigram_idx[:, 0] = self.bigram_vocab - 1\n        if t > 1:\n            bigram_idx[:, 1:] = torch.abs((x[:, 1:] * r1) ^ (x[:, :-1] * r2)) % self.bigram_vocab\n            \n        trigram_idx = torch.zeros_like(x)\n        trigram_idx[:, 0] = self.trigram_vocab - 1\n        if t > 1:\n            trigram_idx[:, 1] = self.trigram_vocab - 1\n        if t > 2:\n            trigram_idx[:, 2:] = torch.abs((x[:, 2:] * r1) ^ (x[:, 1:-1] * r2) ^ (x[:, :-2] * r3)) % self.trigram_vocab\n            \n        return bigram_idx, trigram_idx\n\n    def forward(self, idx):\n        b, t = idx.size()\n        tok_emb = self.wte(idx)\n        pos = torch.arange(0, t, dtype=torch.long, device=idx.device)\n        self._cached_pos_emb = self.wpe(pos)\n        pos_emb = self._cached_pos_emb\n        \n        self._cached_vte = self.ln_vte(self.vte(idx))\n        b_idx, t_idx = self._hashes(idx)\n        self._cached_bigram = self.ln_bigram(self.bigram(b_idx))\n        self._cached_trigram = self.ln_trigram(self.trigram(t_idx))\n        \n        return self.drop(tok_emb + pos_emb)\n\n    def get_lm_head_weight(self):\n        \"\"\"Return weight for the language model head (tied by default).\"\"\"\n        return self.wte.weight\n\n    def get_num_pos_params(self):\n        \"\"\"Return number of position embedding parameters (excluded from param count).\"\"\"\n        return self.wpe.weight.numel()\n\n    def get_value_embed(self, layer_idx):\n        if self._cached_vte is None:\n            return None\n        g_v = self.ve_gates[layer_idx, 0]\n        g_b = self.ve_gates[layer_idx, 1]\n        g_t = self.ve_gates[layer_idx, 2]\n        g_p = self.ve_gates[layer_idx, 3]\n        \n        inj = (self._cached_vte * g_v) + (self._cached_bigram * g_b) + \\\n              (self._cached_trigram * g_t) + (self._cached_pos_emb * g_p)\n        return inj",
6  "start_line": 115
7}
Tool ResultStep 4
OK: Replaced lines 115..194 with 87 line(s) in nanoGPT/custom_pretrain.py. Editable range: 115–201, 326–328.

[Current file: nanoGPT/custom_pretrain.py | editable: 115–201 | total: 534 lines]
   112|     bias: bool = False
   113| 
   114| # ── Embedding Strategy ────────────────────────────────────────────────────
   115  class TokenEmbedding(nn.Module):
   116      """Token + position embedding with Multi-Scale N-gram & Positional Value Injection."""
   117      def __init__(self, config):
   118          super().__init__()
   119          self.wte = nn.Embedding(config.vocab_size, config.n_embd)
   120          self.wpe = nn.Embedding(config.block_size, config.n_embd)
   121          self.drop = nn.Dropout(config.dropout)
   122          self.block_size = config.block_size
   123          self.n_embd = config.n_embd
   124          self.vocab_size = config.vocab_size
   125          self.n_layer = config.n_layer
   126          
   127          self.vte = nn.Embedding(config.vocab_s
AssistantStep 5test
AssistantStep 5test
AssistantStep 5test
Tool ResultStep 5
[Test #2] (1 test remaining; call submit(n=N) to choose which test result to submit as final)

### gpt-345m (scripts/gpt_345m.sh)
Training metrics (last steps):
TRAIN_METRICS: step=8000, train_loss=2.5890, val_loss=2.3305
TRAIN_METRICS: step=9000, train_loss=2.5602, val_loss=2.3004
TRAIN_METRICS: step=10000, train_loss=2.5348, val_loss=2.2872
TRAIN_METRICS: step=11000, train_loss=2.5144, val_loss=2.2699
TRAIN_METRICS: step=12000, train_loss=2.5131, val_loss=2.2604
Final metrics (gpt-345m): val_loss_gpt-345m=2.2649, wikitext2_ppl_gpt-345m=43.5400, lambada_ppl_gpt-345m=70.1300

### lm-eval-345m (scripts/lm_eval_345m.sh)
ERROR: Checkpoint not found: /scratch/gpfs/CHIJ/bohan/MLS-Bench/.saves/llm-pretrain-embedding/gemini-3.1-pro-preview_20260408_123016/seed_42/ckpt_gpt-345m.pt


[Leaderboard] Results saved: {'val_loss_gpt-345m': 2.2649, 'wikitext2_ppl_gpt-345m': 43.54, 'lambada_ppl_gpt-345m': 70.13, 'elapsed_gpt-345m': 21912, 'elapsed_lm-eval-345m': 1}
AssistantStep 6edit
Tool Input
1{
2  "filename": "nanoGPT/custom_pretrain.py",
3  "op": "replace",
4  "content": "class TokenEmbedding(nn.Module):\n    \"\"\"Token + position embedding with Multi-Scale N-gram Value Injection.\"\"\"\n    def __init__(self, config):\n        super().__init__()\n        self.wte = nn.Embedding(config.vocab_size, config.n_embd)\n        self.wpe = nn.Embedding(config.block_size, config.n_embd)\n        self.drop = nn.Dropout(config.dropout)\n        self.block_size = config.block_size\n        self.n_embd = config.n_embd\n        self.vocab_size = config.vocab_size\n        self.n_layer = config.n_layer\n        \n        self.vte = nn.Embedding(config.vocab_size, config.n_embd)\n        nn.init.normal_(self.vte.weight, mean=0.0, std=0.02)\n        self.ln_vte = LayerNorm(config.n_embd, bias=config.bias)\n        \n        self.bigram_vocab = config.vocab_size * 2\n        self.bigram = nn.Embedding(self.bigram_vocab, config.n_embd)\n        nn.init.normal_(self.bigram.weight, mean=0.0, std=0.02)\n        self.ln_bigram = LayerNorm(config.n_embd, bias=config.bias)\n        \n        self.trigram_vocab = config.vocab_size * 2\n        self.trigram = nn.Embedding(self.trigram_vocab, config.n_embd)\n        nn.init.normal_(self.trigram.weight, mean=0.0, std=0.02)\n        self.ln_trigram = LayerNorm(config.n_embd, bias=config.bias)\n        \n        self.ve_gates = nn.Parameter(torch.zeros(config.n_layer, 3, config.n_embd))\n        nn.init.normal_(self.ve_gates, mean=0.0, std=0.01)\n        \n        self._cached_vte = None\n        self._cached_bigram = None\n        self._cached_trigram = None\n\n    def _hashes(self, idx):\n        x = idx.to(torch.int32)\n        b, t = x.size()\n        r1, r2, r3 = 36313, 27191, 15473\n        \n        bigram_idx = torch.zeros_like(x)\n        bigram_idx[:, 0] = self.bigram_vocab - 1\n        if t > 1:\n            bigram_idx[:, 1:] = torch.abs((x[:, 1:] * r1) ^ (x[:, :-1] * r2)) % self.bigram_vocab\n            \n        trigram_idx = torch.zeros_like(x)\n        trigram_idx[:, 0] = self.trigram_vocab - 1\n        if t > 1:\n            trigram_idx[:, 1] = self.trigram_vocab - 1\n        if t > 2:\n            trigram_idx[:, 2:] = torch.abs((x[:, 2:] * r1) ^ (x[:, 1:-1] * r2) ^ (x[:, :-2] * r3)) % self.trigram_vocab\n            \n        return bigram_idx, trigram_idx\n\n    def forward(self, idx):\n        b, t = idx.size()\n        tok_emb = self.wte(idx)\n        pos = torch.arange(0, t, dtype=torch.long, device=idx.device)\n        pos_emb = self.wpe(pos)\n        \n        self._cached_vte = self.ln_vte(self.vte(idx))\n        b_idx, t_idx = self._hashes(idx)\n        self._cached_bigram = self.ln_bigram(self.bigram(b_idx))\n        self._cached_trigram = self.ln_trigram(self.trigram(t_idx))\n        \n        return self.drop(tok_emb + pos_emb)\n\n    def get_lm_head_weight(self):\n        \"\"\"Return weight for the language model head (tied by default).\"\"\"\n        return self.wte.weight\n\n    def get_num_pos_params(self):\n        \"\"\"Return number of position embedding parameters (excluded from param count).\"\"\"\n        return self.wpe.weight.numel()\n\n    def get_value_embed(self, layer_idx):\n        if self._cached_vte is None:\n            return None\n        g_v = self.ve_gates[layer_idx, 0]\n        g_b = self.ve_gates[layer_idx, 1]\n        g_t = self.ve_gates[layer_idx, 2]\n        return (self._cached_vte * g_v) + (self._cached_bigram * g_b) + (self._cached_trigram * g_t)",
5  "start_line": 115,
6  "end_line": 201
7}
Tool ResultStep 6
OK: Replaced lines 115..201 with 80 line(s) in nanoGPT/custom_pretrain.py. Editable range: 115–194.

[Current file: nanoGPT/custom_pretrain.py | editable: 115–194 | total: 527 lines]
   112|     bias: bool = False
   113| 
   114| # ── Embedding Strategy ────────────────────────────────────────────────────
   115  class TokenEmbedding(nn.Module):
   116      """Token + position embedding with Multi-Scale N-gram Value Injection."""
   117      def __init__(self, config):
   118          super().__init__()
   119          self.wte = nn.Embedding(config.vocab_size, config.n_embd)
   120          self.wpe = nn.Embedding(config.block_size, config.n_embd)
   121          self.drop = nn.Dropout(config.dropout)
   122          self.block_size = config.block_size
   123          self.n_embd = config.n_embd
   124          self.vocab_size = config.vocab_size
   125          self.n_layer = config.n_layer
   126          
   127          self.vte = nn.Embedding(config.vocab_size, config.n_embd)
AssistantStep 7submit
Tool Input
1{
2  "n": 1
3}
Tool ResultStep 7
[submit] Submitting result from test #1 as final.

### gpt-345m (scripts/gpt_345m.sh)
Training metrics (last steps):
TRAIN_METRICS: step=8000, train_loss=2.5881, val_loss=2.3239
TRAIN_METRICS: step=9000, train_loss=2.5474, val_loss=2.3050
TRAIN_METRICS: step=10000, train_loss=2.5260, val_loss=2.2809
TRAIN_METRICS: step=11000, train_loss=2.5159, val_loss=2.2672
TRAIN_METRICS: step=12000, train_loss=2.5150, val_loss=2.2590
Final metrics (gpt-345m): val_loss_gpt-345m=2.2549, wikitext2_ppl_gpt-345m=43.1300, lambada_ppl_gpt-345m=66.0200

### lm-eval-345m (scripts/lm_eval_345m.sh)
Final metrics (lm-eval-345m): arc_easy_lm-eval-345m=56.6900, hellaswag_lm-eval-345m=34.0700, piqa_lm-eval-345m=64.1500, winogrande_lm-eval-345m=50.9900

[submit] Finalized test #1 as final.

[Leaderboard] Results saved: {'val_loss_gpt-345m': 2.2549, 'wikitext2_ppl_gpt-345m': 43.13, 'lambada_ppl_gpt-345m': 66.02, 'elapsed_gpt-345m': 21919, 'arc_easy_lm-eval-345m': 56.69, 'hellaswag_lm-eval-345m': 34.07, 'piqa_lm-eva