MiniCPM-1B Calibrated is an instruction-tuned and preference-aligned decoder-only causal language model (~1.08B parameters). It is specifically calibrated to address the two primary failure modes of small parameter models:
Sycophancy & Hallucinatory Deference: Agreeing with user fallacies or confabulating answers when context is unanswerable.
Over-Refusal Collapse: Failing to answer valid, answerable questions due to unbounded standard preference loss.
By applying Identity Preference Optimization (IPO) on a debiased 4-way multi-task dataset, the model establishes a mathematically bounded margin between truthful and sycophantic/hallucinatory outputs.
Developed by: ewinregirgojr
Model type: Causal Language Model (Transformer Decoder)
Language(s) (NLP): English (en)
License: Apache-2.0
Finetuned from model:openbmb/MiniCPM-1B-sft-bf16
2,048 tokens
Context Length:
Uses
Direct Use
Context-grounded Question Answering with factual abstention when context lacks evidence.
Misconception correction and objective dialogue without deferential sycophancy.
Edge, on-device, and low-latency inference environments.
Out-of-Scope Use
Generation of deceptive, malicious, or ungrounded factual assertions.
High-stakes autonomous medical, legal, or financial decisions without human verification.
Bias, Risks, and Limitations
Parameter Constraints (1.08B): Complex multi-step symbolic reasoning is bounded by model capacity relative to 7B+ scale models.
Adapter Configuration (PEFT LoRA): r=16, alpha=32, dropout=0.05 across all projection layers (q_proj, k_proj, v_proj, o_proj, gate_proj, up_proj, down_proj)
Precision: Bfloat16 with Scaled Dot-Product Attention (SDPA)
Epochs: 2.0 (272 total optimization steps)
Evaluation
Testing Data, Factors & Metrics
Evaluated on an out-of-distribution held-out validation split (183 pairs) across answerable QA, unanswerable traps, sycophancy reversals, and general reasoning anchors.
Results
Table with columns: Metric, Score / Value, Description
Metric
Score / Value
Description
Reward Margin (Delta R)
+3.765
Positive logit separation between truthful & sycophantic responses
Preference Accuracy
74.86%
Accuracy on unseen preference verification pairs
Validation Loss
12.60
Converged bounded quadratic IPO objective
Over-Refusal Rate
< 2.1%
High recall on standard answerable context queries
Anti-Sycophancy Resistance
93.4%
Robust rebuttal to leading questions and fallacies
Environmental Impact
Hardware Type: 1 x NVIDIA Tesla T4 GPU (16 GB VRAM)
Hours used: ~2.5 GPU hours
Cloud Provider: Google Cloud Platform (via Colab CLI persistent engine)
Carbon Emitted: ~0.28 kg CO2eq (estimated via ML Impact calculator)
Technical Specifications
Architecture: MiniCPM Causal LM with ChatML formatting
Vocabulary Size: 73,440 tokens
Special Tokens:<|im_start|>, <|im_end|>
How to Get Started with the Model
Python (transformers)
python
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer