| # Scripts |
|
|
| These are the scripts that produced the runs, copied as they were run (comments in Italian). Paths are absolute |
| paths on the author's machines (`/mnt/mneme-nvme/proxy-ideas/alb-bench-v2`, a Strata install under |
| `/mnt/mneme-nvme/strata-0140`): edit them before rerunning. |
|
|
| ## What you need |
|
|
| - AgentLongBench code (commit `57e13e8` of https://github.com/euReKa025/AgentLongBench) and data |
| (https://huggingface.co/datasets/ign1s/AgentLongBench), laid out as `alb/src/AgentLongBench` and |
| `alb/data/benchmark/...`. Prompts and scorer are the official ones, imported unchanged by `common.py`. |
| - An OpenAI-compatible engine serving the model (here Strata 0.1.40 with Qwen3.8 Flash Next IQ3_XXS, KV int8, |
| 10 experts), started once per window size with `--max-context` equal to the window. |
| - mnemonic-proxy at commit `b8d4920` (0.3.5) for the `proxy` method (`PROXY_SRC`), configured as in |
| `proxy-cfg-D.json`. |
| - The model's tokenizer (`PACK_TOK`), used to count prompt tokens exactly as the engine does. |
|
|
| ## Files |
|
|
| | file | role | |
| |---|---| |
| | `make_main_lists.py` | writes the preregistered job lists for all 25 questions per type and length (100 questions), in `main-64k.txt`, `main-128k.txt` | |
| | `make_clar.py` | builds the clarified questions (`alb-clar/`, same as `questions-clarified/`) and `main-clar-*.txt` | |
| | `main-*.txt`, `main-clar-*.txt` | job lists: one line per job, `<policy> <window> <file> <qid>` (A = full window, D = proxy, B = summary, T = truncation) | |
| | `run_alb_v2.py` | runs one job: builds the official messages, applies the method, asks the question, scores it with the official scorer, writes `result.json` and the logs | |
| | `common.py` | official prompt/parser/scorer imports, token counter, HTTP client | |
| | `meter.py` | engine call counter (`engine_calls.jsonl`) and resource sampler (`resources.jsonl`) | |
| | `normfmt.py` | format-normalised score (secondary; the headline numbers use the raw official score) | |
| | `check_c2.py` | stop rules checked after every job | |
| | `gpu_block_c2.sh`, `gpu_block_clar.sh` | one GPU block: starts the engine per window, runs the jobs of a list in order, stops on a rule; `_c2` was used for the main run, `_clar` (same, with `ALB` pointing at the clarified data) for the confirmation run | |
| | `run_main.sh`, `run_clar.sh` | drivers: one block per length (64k then 128k) | |
| | `build_results.py` | builds `results/*.jsonl` of this dataset from the `result.json` files (extract `logs/*.tar.zst` into `raw/runs/` next to it first) | |
|
|
| ## Rerunning |
|
|
| ``` |
| python make_main_lists.py # or use the lists shipped here |
| python make_clar.py # confirmation run only |
| bash run_main.sh 64k 128k # main run -> runs/main-64k, runs/main-128k |
| bash run_clar.sh 64k 128k # confirmation -> runs/main-clar-64k, runs/main-clar-128k |
| ``` |
|
|
| Sampling is seeded (seed 1), but GPU inference is not bit-for-bit deterministic: expect run-to-run noise (see the |
| dataset README, Limits). |
|
|
