初始化项目，由ModelHub XC社区提供模型

Model: iamrahulreddy/Quintus
Source: Original Platform

configs/config.yaml

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+# Quintus Distillation Pipeline
+# Run profile: online full-vocabulary KD, 8B teacher -> 1.7B-Base student.
+# Data: ~90K English-only samples from DistilQwen_100k.
+
+data:
+  dataset_path: "<REDACTED_ON_PURPOSE>"
+  num_samples: 90234
+  max_seq_len: 4096
+  stream_shuffle_buffer_size: 20000
+  stream_shuffle_seed: 25
+
+model:
+  teacher: "Qwen/Qwen3-8B"
+  student: "Qwen/Qwen3-1.7B-Base"
+
+  # The instruct tokenizer carries the chat template used to format the base
+  # student into assistant-style training examples.
+  tokenizer: "Qwen/Qwen3-1.7B"
+
+  teacher_revision: "main"
+  student_revision: "main"
+  tokenizer_revision: "main"
+  allow_remote_code: false
+
+training:
+  # Schedule
+  num_epochs: 1
+  validation_ratio: 0.02
+  split_seed: 25
+
+  # Optimizer
+  learning_rate: 5.0e-6
+  weight_decay: 0.1
+  warmup_ratio: 0.05
+
+  # Loss mix used by src/losses.py:
+  # total = alpha * CE + (1 - alpha) * KD
+  alpha: 0.3
+  temperature: 2.0
+
+  # Online KD streams full-vocabulary teacher logits. top_k is retained for
+  # offline-KD compatibility/provenance checks.
+  top_k: 8
+  online_kd_token_chunk_size: 2048
+
+  # Conservative B200 profile. Effective batch = 4 * 2 = 8.
+  # If VRAM headroom is comfortable and Liger is installed, try 8 * 1.
+  micro_batch_size: 4
+  grad_accum_steps: 2
+  gradient_checkpointing: false
+  compile_model: false
+  fused_adamw: true
+
+  dataloader_workers: 8
+  prefetch_factor: 2
+
+  sequence_packing:
+    enabled: true
+    pack_length: 4096
+    mask_first_token_after_separator: true
+
+hub:
+  # Prefer HF_TOKEN or huggingface-cli login for real runs.
+  token: null
+  username: "<REDACTED_ON_PURPOSE>"
+  repo_name: "<REDACTED_ON_PURPOSE>"
+
+paths:
+  teacher_dir: "<REDACTED_ON_PURPOSE>"
+  student_dir: "<REDACTED_ON_PURPOSE>"
+  tokenizer_dir: "<REDACTED_ON_PURPOSE>"
+  tokenized_dir: "<REDACTED_ON_PURPOSE>"
+  logits_dir: "<REDACTED_ON_PURPOSE>"
+  distilled_dir: "<REDACTED_ON_PURPOSE>"
+  log_file: "<REDACTED_ON_PURPOSE>"
+  system_info: "<REDACTED_ON_PURPOSE>"
+  loss_csv: "<REDACTED_ON_PURPOSE>"