Instructions to use msr-spare/msr-agentic-ptb-opus-max with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use msr-spare/msr-agentic-ptb-opus-max with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("msr-spare/msr-agentic-ptb-opus-max", device_map="auto") - Notebooks
- Google Colab
- Kaggle
msr-agentic-ptb-opus-max β cell 3 backup
Backup of cell 3 of the agentic-PTB benchmark: Claude Code (claude-opus-5[1m]) at max
reasoning effort, given Qwen/Qwen3.5-9B-Base, 4 GPUs and 100 autonomous hours to post-train it
for agentic SWE / terminal work. The run completed its full window on 2026-08-17 18:28:54Z.
β The submission is
checkpoints/sft_v5_step_900Not
SUBMITTED_soup_base_sft. That directory name is a leftover from a six-hour window during the run and is wrong. The interpolated checkpoint it holds was submitted, then withdrawn by the arm on its own pre-registered decision rule ~6 h later, andsft_v5_step_900was restored as the submission.checkpoints/soup_base_sftis the same weights under a non-misleading name. SeeSUBMITTED_soup_base_sft/WITHDRAWN.md.
Serve the submission directly β it is standard HF format:
vllm serve checkpoints/sft_v5_step_900 --served-model-name policy \
--enable-auto-tool-choice --tool-call-parser qwen3_coder \
--limit-mm-per-prompt '{"image":0,"video":0}' --max-model-len 65536
The submitted system is the checkpoint plus a harness plugin (pi-ws: a task
working-directory fix and an appended operating-procedure prompt). The plugin is not in this
repo β it lives at agentptb/runs/a-opus-max/workspace/pkgs/pi_ws. Without it the numbers below do
not reproduce; the plugin is worth more than the weights (see below).
Results
swe-bench-verified on a seeded 250-task sample (SEED=0 pinned, so every run draws the identical
sample), terminal-bench-2 on all 89 tasks. Every panel is a completed run with zero ungraded
episodes, and every swe-bench cell is replicated.
| stock pi harness | pi-ws (submitted system) | |
|---|---|---|
| swe-bench-verified | 15.1% [11.8, 19.3] (n=350) | 20.2% [16.9, 23.9] (n=500) |
| terminal-bench-2 | 3/89 = 3.4% | 1/89 = 1.1% |
Read the 2Γ2 before quoting any single number. All cells n=250, identical task set:
| weights | stock pi | pi-ws | scaffold Ξ |
|---|---|---|---|
| base + eos fix | 4.4% | 27.6% | +23.2pp (p<0.0001) |
sft_short_step_200 |
10.4% | 16.8% | +6.4pp |
sft_v5_step_900 (submitted) |
13.2% | 20.8% | +7.6pp (p=0.005) |
| weights Ξ vs base | +8.8pp (p=0.0003) | β6.8pp (p=0.036) |
Two things follow, and the arm states both plainly in records/SUBMISSION.md:
- The harness fix, not the training, carries the result. A working-directory fix plus a short prompt is worth +23pp on the untrained base.
- The fine-tune is a net liability on the harness it submits. With the scaffold held fixed the weights are β6.8pp; the untrained base + scaffold (27.6%) beats the submitted system (20.2%). On the stock harness the same weights are +8.8pp. Both statements describe this checkpoint.
Do not report a terminal-bench-2 number for this cell. All 89 tasks, every comparison pβ₯0.125, and the base (5/89) is above the submitted system on both harnesses.
Noise floor, measured not assumed: two runs of an identical system disagree on 39 of 250 tasks (15.6%), symmetrically (21 vs 18). Any result near 22-vs-30 discordant is inside it.
Known packaging wart
config.json says "dtype": "float32" while the stored tensors are bf16. vLLM ignores the key and
infers from the safetensors β every measurement above was logged at bf16. A transformers user
loading the directory will upcast to fp32: numerically exact, twice the memory. Left as-is
deliberately, because the measured artifact is the directory as it stands.
generation_config.json carries eos_token_id = [248044, 248046]. This matters: the base ships
no generation config, so <|im_end|> β the token its own chat template ends every turn with β is
not a stop token, and the model runs past its turn and hallucinates the user's reply.
What is here
checkpoints/β 14 checkpoints, every weight artifact the run produced and that still exists. Four of these were already deleted locally by prime-rl'skeep_lastrotation and survive only here.sft_v1is absent because a node eviction at h6.75 destroyed it at step 600/940.records/βSUBMISSION.md(the arm's own final write-up, 488 lines),NOTES.md(1,064 lines, 35 numbered findings),CHECKPOINT-CHAIN.md,DEADLINE.traces/β the raw evaluation traces every number above was computed from. Every figure in this README has been independently recomputed from these by the operator and reproducesrecords/SUBMISSION.mdexactly.data/β the SFT corpora, including the three rewrite passes that producedsft_v5.
Full cross-cell comparison keys: agentptb/ROLLUP.md. Write-up:
https://claude.ai/code/artifact/a965ff02-e109-49b4-b37c-5d4e47e8671f.
Caveat for cross-cell comparison
This cell ran under goal v5 on 3 GPUs (allocated 4, used 3). Cell 0 β the same driver at
high effort β ran under goal v2 on 4. Goal v5 permits distillation and this arm also fixed the
base's generation config. A cell-0 vs cell-3 difference therefore cannot be attributed to
reasoning effort. A clean effort comparison needs an opus@high cell under goal v5.