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| 1 |
+
# VectraYX Reproducibility Makefile
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| 2 |
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# Reproduces the key experiments from the paper on a single NVIDIA L4 / A10G GPU.
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| 3 |
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#
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# Prerequisites:
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# - Python 3.10+
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# - CUDA 12.1+
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# - pip install -r requirements.txt
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| 8 |
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# - AWS CLI configured (for SageMaker experiments)
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| 9 |
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# - Nano checkpoint: nano_sft_v5.pt (download from HuggingFace, see README)
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# - Base checkpoint: base_phase3_last.pt (download from HuggingFace, see README)
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| 11 |
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#
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+
# Usage:
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| 13 |
+
# make install # install dependencies
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# make bench-nano # run B1-B5 on Nano 42M (requires nano_sft_v5.pt)
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# make bench-base # run B1-B5 on Base 260M (requires base_phase3_last.pt)
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# make lora-nano # LoRA tool-use fine-tune on Nano (local GPU)
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# make lora-base # LoRA tool-use fine-tune on Base (local GPU)
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# make repro # full reproducibility run (bench + lora + bench again)
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# make corpus # regenerate tool_sft_mini_v1.jsonl from scratch
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PYTHON := python3
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NANO_CKPT := checkpoints/nano_sft_v5.pt
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BASE_CKPT := checkpoints/base_phase3_last.pt
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TOKENIZER := checkpoints/vectrayx_bpe.model
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NANO_CFG := configs/nano.json
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BASE_CFG := configs/base.json
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EVAL_DIR := eval_data
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CORPUS := corpus/tool_sft_mini_v1.jsonl
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LORA_OUT := checkpoints/lora_out
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.PHONY: install bench-nano bench-base lora-nano lora-base repro corpus clean help
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| 32 |
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help:
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| 34 |
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@echo "VectraYX Reproducibility Makefile"
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| 35 |
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@echo ""
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| 36 |
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@echo "Targets:"
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| 37 |
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@echo " install Install Python dependencies"
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| 38 |
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@echo " bench-nano Run B1-B5 benchmark on Nano 42M"
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| 39 |
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@echo " bench-base Run B1-B5 benchmark on Base 260M"
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| 40 |
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@echo " lora-nano LoRA fine-tune Nano 42M on tool-use corpus"
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| 41 |
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@echo " lora-base LoRA fine-tune Base 260M on tool-use corpus"
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| 42 |
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@echo " repro Full reproducibility run"
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| 43 |
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@echo " corpus Regenerate tool_sft_mini_v1.jsonl"
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| 44 |
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@echo " clean Remove generated checkpoints and results"
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| 45 |
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| 46 |
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install:
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| 47 |
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pip install -r requirements.txt
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| 48 |
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| 49 |
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# ββ Benchmark ββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ
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| 50 |
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| 51 |
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bench-nano: $(NANO_CKPT) $(TOKENIZER)
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| 52 |
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$(PYTHON) eval/benchmark.py \
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| 53 |
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--config $(NANO_CFG) \
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--tokenizer $(TOKENIZER) \
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| 55 |
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--checkpoint $(NANO_CKPT) \
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| 56 |
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--data-dir $(EVAL_DIR) \
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| 57 |
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--out results/bench_nano_baseline.json
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| 58 |
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@echo "Results: results/bench_nano_baseline.json"
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| 59 |
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bench-base: $(BASE_CKPT) $(TOKENIZER)
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| 61 |
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$(PYTHON) eval/benchmark.py \
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| 62 |
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--config $(BASE_CFG) \
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| 63 |
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--tokenizer $(TOKENIZER) \
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| 64 |
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--checkpoint $(BASE_CKPT) \
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| 65 |
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--data-dir $(EVAL_DIR) \
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| 66 |
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--out results/bench_base_baseline.json
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| 67 |
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@echo "Results: results/bench_base_baseline.json"
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| 68 |
+
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| 69 |
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# ββ LoRA fine-tune βββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ
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| 71 |
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lora-nano: $(NANO_CKPT) $(TOKENIZER) $(CORPUS)
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| 72 |
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mkdir -p $(LORA_OUT)/nano
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| 73 |
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$(PYTHON) training/finetune_lora_tools.py \
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| 74 |
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--config $(NANO_CFG) \
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| 75 |
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--tokenizer $(TOKENIZER) \
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--resume $(NANO_CKPT) \
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--tool-corpus $(CORPUS) \
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--out $(LORA_OUT)/nano \
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| 79 |
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--lora-rank 16 --lora-alpha 32 \
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| 80 |
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--batch-size 16 --grad-accum 4 \
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| 81 |
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--epochs 5 --lr 2e-4 --seed 42
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$(PYTHON) eval/run_inference_lora.py \
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--base-checkpoint $(NANO_CKPT) \
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--lora-checkpoint $(LORA_OUT)/nano/final_lora_only.pt \
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| 85 |
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--config $(NANO_CFG) \
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--tokenizer $(TOKENIZER) \
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--data-dir $(EVAL_DIR) \
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--out results/bench_nano_lora_s42.json
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| 89 |
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@echo "Results: results/bench_nano_lora_s42.json"
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| 90 |
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lora-base: $(BASE_CKPT) $(TOKENIZER) $(CORPUS)
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| 92 |
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mkdir -p $(LORA_OUT)/base
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| 93 |
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$(PYTHON) training/finetune_lora_tools.py \
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| 94 |
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--config $(BASE_CFG) \
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| 95 |
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--tokenizer $(TOKENIZER) \
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| 96 |
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--resume $(BASE_CKPT) \
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| 97 |
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--tool-corpus $(CORPUS) \
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--out $(LORA_OUT)/base \
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--lora-rank 16 --lora-alpha 32 \
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| 100 |
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--batch-size 8 --grad-accum 8 \
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| 101 |
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--epochs 5 --lr 2e-4 --seed 42
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| 102 |
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$(PYTHON) eval/run_inference_lora.py \
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| 103 |
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--base-checkpoint $(BASE_CKPT) \
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| 104 |
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--lora-checkpoint $(LORA_OUT)/base/final_lora_only.pt \
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| 105 |
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--config $(BASE_CFG) \
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| 106 |
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--tokenizer $(TOKENIZER) \
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| 107 |
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--data-dir $(EVAL_DIR) \
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| 108 |
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--out results/bench_base_lora_s42.json
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| 109 |
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@echo "Results: results/bench_base_lora_s42.json"
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| 110 |
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| 111 |
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# ββ Full reproducibility run βββββββββββββββββββββββββββββββββββββββββββββββββββ
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+
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repro: install bench-nano bench-base lora-nano lora-base
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| 114 |
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@echo ""
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| 115 |
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@echo "=== Reproducibility Run Complete ==="
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| 116 |
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@echo "Expected results (from paper, Table 3):"
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| 117 |
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@echo " Nano baseline B4=0.000"
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| 118 |
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@echo " Base baseline B4=0.000"
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| 119 |
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@echo " Nano + LoRA B4=0.145 Β± 0.046 (seed 42: 0.220)"
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| 120 |
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@echo " Base + LoRA B4=0.580"
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| 121 |
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@echo ""
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| 122 |
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@echo "Your results:"
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| 123 |
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@$(PYTHON) -c "import json; \
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| 124 |
+
r = {k: json.load(open(f'results/{k}.json')) for k in \
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| 125 |
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['bench_nano_baseline','bench_base_baseline','bench_nano_lora_s42','bench_base_lora_s42'] \
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| 126 |
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if __import__('pathlib').Path(f'results/{k}.json').exists()}; \
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| 127 |
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[print(f' {k}: B4={v.get(\"B4_tooluse\",\"N/A\")}') for k,v in r.items()]"
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| 128 |
+
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| 129 |
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# ββ Corpus generation ββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ
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| 130 |
+
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| 131 |
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corpus:
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| 132 |
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$(PYTHON) corpus/build_mini_tool_corpus.py \
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| 133 |
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--size 2801 \
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| 134 |
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--out corpus/tool_sft_mini_v1_repro.jsonl
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| 135 |
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@echo "Generated: corpus/tool_sft_mini_v1_repro.jsonl"
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| 136 |
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@echo "Note: compare with corpus/tool_sft_mini_v1.jsonl (released version)"
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| 137 |
+
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| 138 |
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# ββ Cleanup ββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ
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| 139 |
+
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| 140 |
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clean:
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| 141 |
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rm -rf checkpoints/lora_out results/
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| 142 |
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@echo "Cleaned generated files. Checkpoints preserved."
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