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mhla/
gpt1900-d34-contradiction-rl-v11

Name: gpt1900-d34-contradiction-rl-v11
Availability: InStock

reinforcement-learning

mit

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GPT-1900 Contradiction RL v11

The best-performing GPT-1900 model. Trained with reinforcement learning to reason about physics contradictions — experimental observations that challenge its pre-1900 understanding of the world.

When presented with evidence for the photoelectric effect, blackbody radiation, or radioactive decay, this model reasons through why its 19th-century physics fails to explain the observations — and sometimes arrives at the threshold of 20th-century discoveries.

Physics eval score: 1.25 (peak). Photoelectric effect: 4/5. Elevator thought experiment: 3/5.

Training

Method: REINFORCE with contradiction reward
Base: Instruct v3 safe (physics CLM expanded → v3 SFT safe)
Steps: 839 (24 checkpoints saved, peak at steps 560/630/735)
Eval data: mhla/gpt1900-contradiction-eval

Training Chain

GPT-1900 base (22B tokens pre-1900 text)
  → Physics CLM expanded (continued pretraining on pre-1905 physics texts)
    → Instruct v3 safe (SFT on filtered conversations)
      → Contradiction RL v11  ← you are here (BEST)

Architecture

Custom GPT with RoPE, QK-norm, ReLU² activation, value embeddings (ResFormer), and per-layer residual/skip scalars. Built with the nanochat framework.

Parameter	Value
Parameters	3.29B
Layers	34
Hidden dim	2176
Attention heads	17 (query) / 17 (kv)
Head dim	128
Context length	2048 tokens
Vocab size	32,768 (BPE, GPT-4 style split pattern)

Notes

Generation parameters: You may need to play with temperature to get good results. The chat default is 0.6; the physics eval uses 0.7 with top_k=50.

This is an RL model trained with physics reasoning rewards. It should only be expected to perform well on physics prompts. For general conversation, use the SFT model (gpt1900-instruct-v3-sft).

Quick Start

python

import torch, json
from nanochat.gpt import GPT, GPTConfig
from nanochat.tokenizer import RustBPETokenizer

tokenizer = RustBPETokenizer.from_directory("tokenizer")

with open("meta_000839.json") as f:
    meta = json.load(f)

config = GPTConfig(**meta["model_config"])
with torch.device("meta"):
    model = GPT(config)
model.to_empty(device="cuda")
model.init_weights()

state_dict = torch.load("model_000839.pt", map_location="cuda")
state_dict = {k.removeprefix("_orig_mod."): v for k, v in state_dict.items()}
model.load_state_dict(state_dict, strict=True, assign=True)
model.eval()

Chat