Qwen2.5 7B (local) (thinking off) on Implement 5 Python functions from a task list
QQwen2.5 7B (local)thinking off · ollama-mini-agent v1.0 · local (NVIDIA GeForce RTX 3060 12GB, 12 GB) · 2026-10-20 · by @bobsiopener
- Cost
- $0 (local)
- Wall time
- 12s
- Human step-ins
- 0
- Outcome
- Fail
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Summary
Exact prompt
Read tasks/TASKS.md and implement all 5 functions in ./solution.py in the current directory. Python 3 stdlib only.
Task prompt given to the agent (headless, offline, synthetic fixture inputs): Read tasks/TASKS.md and implement all 5 functions in ./solution.py in the current directory. Python 3 stdlib only. Inspired by https://x.com/Vladic_ETH/status/2108591593120756156 (unverified; not checked with the author). This is an independent re-run on a reproducible synthetic task; it does not report or reproduce the original post's numbers. Local model: $0 API cost; electricity not measured.
Success criteria: All automated rubric checks pass: hidden test test_duration; hidden test test_lru; hidden test test_merge; hidden test test_roman; hidden test test_topk.
Setup notes: Harness: ollama-mini-agent v1.0. Minimal tool loop, same system prompt, tools (list_files/read_file/write_file/run_shell), 25-turn limit and temperature 0.2 as the Venice mini-agent; run_shell sandboxed (read-only system, no network, only the work dir writable). Local Ollama, no cost cap needed. Model: Qwen2.5 7B Instruct Q4_K_M (4.7 GB), Ollama 0.20.0, num_ctx 16384. GPU free at batch start: 6933 MiB of 12288 MiB (shared GPU). Tokens: Ollama prompt_eval_count / eval_count summed over calls. Single run, no retries; scored automatically (0/5 checks). Runner notes: The model did not write solution.py, so the hidden tests could not import it and every hidden test counts as failed. Single run, no retries, no interventions. Same rubric and scorer checks as the other runs of this case. Network off for the agent's shell.
Metrics
- Cost
- $0 (local)
- Wall time
- 12s
- Human step-ins
- 0
- Turns
- 2
| Model | Input tok | Output tok | Cost |
|---|---|---|---|
| QQwen2.5 7B (local)ollama/qwen2.5:7bthinking off | 960 | 678 | $0 (local) |
Reproduce this run
- Use the use case prompt on the use case page.
- Same setup: Qwen2.5 7B (local) (ollama/qwen2.5:7b, thinking off) via
ollama-mini-agent v1.0, local on NVIDIA GeForce RTX 3060 12GB. - It counts as a success only if it meets every success criterion: All automated rubric checks pass: hidden test test_duration; hidden test test_lru; hidden test test_merge; hidden test test_roman; hidden test test_topk.
- Log model, tokens, wall time, human step-ins and the evidence log, then submit it as a reproduction. A different model? Submit a head-to-head rerun.
How runs are scored: Methodology.
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Automated checks
Trust 80% comes from these checks (how Trust % works).
- Run date is not in the future : passed
- Model is in the dated pricing table : passed
- Cost recomputed from tokens matches : passed
- Cached tokens are consistent : passed
- Output throughput is plausible : passed
- Autonomy matches step-ins : passed
- Step-ins do not exceed turns : passed
- Retries agree with first-try result : passed
- Evidence log SHA-256 matches : passed
- Evidence log is substantial : passed
Evidence log
{"turn": 1, "response": {"model": "qwen2.5:7b", "created_at": "2026-10-20T19:13:14.839184959Z", "message": {"role": "assistant", "content": "", "tool_calls": [{"id": "call_q6m5yz50", "function": {"index": 0, "name": "read_file", "arguments": {"path": "tasks/TASKS.md"}}}]}, "done": true, "done_reason": "stop", "total_duration": 527136292, "load_duration": 82118130, "prompt_eval_count": 337, "prompt_eval_duration": 36098326, "eval_count": 24, "eval_duration": 381498161}}
{"turn": 1, "tool": "read_file", "args": {"path": "tasks/TASKS.md"}, "result": "Implement these 5 functions in solution.py (Python 3 stdlib only):\n1. roman_to_int(s: str) -> int \u2014 convert a valid Roman numeral (I..MMMCMXCIX) to int.\n2. merge_intervals(iv: list[list[int]]) -> list[list[int]] \u2014 merge overlapping/touching closed intervals, return sorted.\n3. lru_cache_sim(capacity: int, ops: list[tuple]) -> list \u2014 simulate an LRU cache. ops are (\"put\", k, v) or (\"get\", k); return the results of each get in order (-1 if missing).\n4. top_k_words(text: str, k: int) -> list[str] \u2014 k most frequent words, case-insensitive, words are [a-z']+ runs; ties broken alphabetically.\n5. parse_duration(s: str) -> int \u2014 parse strings like \"1h30m\", \"45s\", \"2h\", \"1h2m3s\", \"90m\" into total seconds; raise ValueError for invalid input such as \"\", \"h\", \"1x\", \"1h1h\".\nWrite your own quick tests if you like, then finish.\n"}
{"turn": 2, "response": {"model": "qwen2.5:7b", "created_at": "2026-10-20T19:13:26.505648864Z", "message": {"role": "assistant", "content": ""}, "done": true, "done_reason": "stop", "total_duration": 11665357796, "load_duration": 81547403, "prompt_eval_count": 623, "prompt_eval_duration": 146504170, "eval_count": 654, "eval_duration": 10991506153}}