dont evaluate the sampling evals during SFT they are too slow. keep the multiple choice evals. delete unused imports

scripts/chat_sft.py

@@ -11,7 +11,6 @@ torchrun --standalone --nproc_per_node=8 -m scripts.chat_sft
 
 import os
 os.environ["PYTORCH_CUDA_ALLOC_CONF"] = "expandable_segments:True"
-import copy
 
 import wandb
 import torch
@@ -23,11 +22,9 @@ from nanochat.checkpoint_manager import save_checkpoint
 from nanochat.engine import Engine
 from scripts.chat_eval import run_chat_eval
 
-from tasks.common import TaskMixture, TaskSequence
-from tasks.mmlu import MMLU
+from tasks.common import TaskMixture
 from tasks.arc import ARC
 from tasks.gsm8k import GSM8K
-from tasks.humaneval import HumanEval
 from tasks.smoltalk import SmolTalk
 
 # -----------------------------------------------------------------------------
@@ -186,7 +183,7 @@ for step in range(num_iterations):
         })
         model.train()
 
-    # evlauate MMLU accuracy
+    # evlauate accuracy of the multiple choice tasks (which are quick to run)
     if last_step or (step > 0 and step % eval_metrics_every == 0):
         model.eval()
         metrics = {}
@@ -194,8 +191,6 @@ for step in range(num_iterations):
             # note that because these are inside no_grad, we can usually afford to at least ~2X the batch size
             metrics["mmlu_acc"] = run_chat_eval("MMLU", model, tokenizer, engine, batch_size=device_batch_size*2, max_problems=1024)
             metrics["arc_easy_acc"] = run_chat_eval("ARC-Easy", model, tokenizer, engine, batch_size=device_batch_size*2, max_problems=1024)
-            metrics["gsm8k_acc"] = run_chat_eval("GSM8K", model, tokenizer, engine, max_problems=64)
-            metrics["humaneval_acc"] = run_chat_eval("HumanEval", model, tokenizer, engine, max_problems=64)
         metrics_str = ', '.join(f'{k}: {v:.6f}' for k, v in metrics.items())
         print0(f"Step {step:05d} | {metrics_str}")
         wandb_run.log({