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Alogotron
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Model Details
Model Provider
Alogotron
Model Tree
Qwen/Qwen2.5-7B-Instruct
Input Modalities
Output Modalities
Supported Functionality
Deterministic (greedy) generation, 512 new tokens, judged: valid = parses as JSON dict with all four keys, known tool name, dict tool_args; invalid = parses but violates shape; stall = no parseable JSON object.
| model | validity | invalid | stall | n |
|---|---|---|---|---|
| Qwen2.5-7B-Instruct base | 1.7% (1) | 98.3% (58) | 0% (0) | 59 |
| + this adapter | 84.7% (50) | 0% (0) | 15.3% (9)* | 59 |
All 9 tuned stalls are 512-token-cap truncations in a single held-out chat with unusually long tool_args (valid JSON starts, cut mid-object) - harness cap, not model failure. Base emitted 58 near-miss JSONs (invalid) and only 1 fully valid call. Full protocol + per-record details in eval report / eval-.json.
python
from peft import PeftModelfrom transformers import AutoModelForCausalLM, AutoTokenizerbase = AutoModelForCausalLM.from_pretrained('Qwen/Qwen2.5-7B-Instruct', device_map='auto')model = PeftModel.from_pretrained(base, 'Alogotron/a0-toolcall-lora')# system prompt: instruct EXACTLY ONE JSON object with thoughts/headline/tool_name/tool_argsUse to harden small agent models against tool-call format stalls in Agent Zero-style loops. Trained on one agent household's conversations — tool distribution skews to code_execution (66%); JSON arguments generalize but niche tool semantics may not. Not a general-instruct tune.