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vahpetr
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Model Details
Model Provider
vahpetr
Model Tree
openbmb/MiniCPM5-1B
Input Modalities
Output Modalities
Supported Functionality
| axis | base | +LoRA | note |
|---|---|---|---|
| Reasoning (/18) | ~16 | ~12 | ↓ tradeoff |
| IFEval (/10) | ~8 | ~5 | ↓ tradeoff |
| Code (/7) | 7 | 7 | preserved |
| Agentic tool-use (/10) | ~6 | ~5 | ≈ preserved |
| General / Ortho (/23) | ~12 | ~13 | ↑ (customer-service, translation) |
| Russian math battery | low | low | unchanged (1B math-capped) |
What it does well: practical Russian gets noticeably more fluent and coherent — customer-support replies stop mixing English/Chinese and become natural Russian; RU→EN translation is more accurate.
The tradeoff: sharp reasoning and strict instruction-following drop somewhat — an inherent capacity limit of a 1B model (you can't add a language without spending some of its other skills). For strong Russian without the tradeoff, consider a Russian-native model or a larger base.
Use thinking OFF (enable_thinking=false) — the adapter was trained for the non-thinking format.
Transformers (PEFT):
python
from transformers import AutoModelForCausalLM, AutoTokenizerfrom peft import PeftModelbase = AutoModelForCausalLM.from_pretrained("openbmb/MiniCPM5-1B", trust_remote_code=True)model = PeftModel.from_pretrained(base, "vahpetr/MiniCPM5-1B-ru-lora-v3")tok = AutoTokenizer.from_pretrained("openbmb/MiniCPM5-1B", trust_remote_code=True)
llama.cpp (GGUF adapter MiniCPM5-1B-ru-lora-v3.gguf is included):
bash
llama-server -m MiniCPM5-1B-Q8_0.gguf --lora MiniCPM5-1B-ru-lora-v3.gguf --jinja
(A merged, ready-to-run version is at vahpetr/MiniCPM5-1B-ru-v3.)