This repository contains a LoRA adapter for mistralai/Ministral-3-14B-Base-2512 trained for the Past-Creates-Future (PCF) algorithm-idea generation workflow.
The model is trained on the same detail-to-detail PCF task as the Qwen3 detail-to-detail adapter:
existing method detail + method delta -> new method detail
It is intended as an alternative base-model experiment for structured algorithm-idea generation.
Model Details
Base model: mistralai/Ministral-3-14B-Base-2512
Adapter type: LoRA / PEFT
Task type: causal language modeling
LoRA rank: 16
LoRA alpha: 16
LoRA dropout: 0.05
Target modules: all major linear projection modules (q_proj, k_proj, v_proj, o_proj, gate_proj, up_proj, down_proj)
Training dtype: bf16
Intended Use
This adapter is designed for algorithm-research ideation. It can be used to:
generate structured descriptions of candidate algorithm variants;
apply mechanism-level method deltas to existing algorithms;
compare PCF behavior across different 14B-scale base models;
produce implementation-oriented method sketches for later human validation.
The model is not a replacement for experimental evaluation. It proposes candidate mechanisms; a researcher still needs to implement and benchmark them.
Out-of-Scope Use
Do not use this adapter as evidence that a generated algorithm is correct, optimal, safe, or empirically better. It may generate plausible details that are incomplete or wrong.
Input Format
The training format uses structured method details with these fields:
The expected output is a valid JSON object with the method-detail fields.
Usage
Ministral-3 models may require recent transformers support. The local PCF inference script uses AutoModelForImageTextToText when the model config reports model_type == "mistral3".
The dataset was built from algorithm papers. The pipeline extracts structured method details for old and new methods, summarizes method deltas, filters examples by confidence, and formats examples as:
existing detail + delta detail -> new detail
This version uses a 30k-character paper extraction context and a confidence threshold of 0.6.
Training Configuration
Epochs: 3
Per-device batch size: 1
Gradient accumulation steps: 16
Learning rate: 1e-4
Scheduler: cosine
Max sequence length: 4096
Optimizer: paged AdamW 8-bit
Gradient checkpointing: enabled
Liger kernel: enabled
Hardware used locally: single NVIDIA GPU with 24 GB VRAM
Evaluation
This adapter was compared against the Qwen3 detail-to-detail adapter on held-out PCF examples. It is useful as a base-model comparison point, while the Qwen3 detail-to-detail adapter has been the stronger default choice in local PCF experiments so far.
The uploaded adapter was downloaded back from Hugging Face and verified against the local upload source by SHA256 for adapter_config.json, adapter_model.safetensors, and README.md.
Limitations
This is an experimental adapter for structured algorithm ideation.
It can generate plausible but unverified algorithm mechanisms.
It may require newer transformers support than the Qwen3 adapters.
Output should be reviewed and experimentally validated before being used in research claims.
