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 (/workspace/usecase-runner/ollama/ollama_agent.py), 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/score.py 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 75% comes from these checks (how Trust % works).
- Run date is not in the future : passed
- Model is in the dated pricing table : warning (warning)
- 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
- ·
unknown_model_or_price: ollama/qwen2.5:7b not in pricing table for 2026-10-20
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}}