LEM-Gemma3-1B

Name: LEM-Gemma3-1B
Availability: InStock

The foundation model of the CL-BPL cascade. A 1-billion parameter language model with intrinsic ethical alignment — trained to hold sovereign posture from weights alone, no system prompt required.

LEM-Gemma3-1B serves as the teacher model for the entire LEM family. Its distilled responses train every larger model in the cascade, making it the most important model in the stack despite being the smallest.

Part of the Lethean Ethical Models collection | Research Paper | Benchmarks | Axiom Framework

Quick Start

No system prompt needed. Ethics hold from weights alone.

llama.cpp / ROCm / CPU (any platform)

bash

# Download a GGUF (pick your size from the table below)
# GPU offload (CUDA, ROCm, Metal)
llama-server -m LEM-Gemma3-1B-Q4_K_M.gguf -ngl 99 --port 8080

# CPU — fast enough for 1B
llama-server -m LEM-Gemma3-1B-Q4_K_M.gguf -ngl 0 --port 8080

HuggingFace Transformers

python

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("lthn/LEM-Gemma3-1B")
tokenizer = AutoTokenizer.from_pretrained("lthn/LEM-Gemma3-1B")

messages = [{"role": "user", "content": "What does sovereignty mean to you?"}]
inputs = tokenizer.apply_chat_template(messages, return_tensors="pt", add_generation_prompt=True)
outputs = model.generate(inputs, max_new_tokens=512, temperature=0.7, do_sample=True)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

OpenAI-Compatible API

bash

# llama.cpp server (any platform)
llama-server -m LEM-Gemma3-1B-Q4_K_M.gguf -ngl 99 --port 8899

# Use any OpenAI client
curl http://localhost:8899/v1/chat/completions \
  -d '{"model":"LEM-Gemma3-1B","messages":[{"role":"user","content":"What is kindness?"}]}'

Available Formats

Format	Repo	Size
FP16 safetensors (this repo)	HuggingFace Transformers, vLLM	2.4 GB
GGUF (12 quants, 3-bit to 16-bit)	lthn/LEM-Gemma3-1B-GGUF	819 MB–2.4 GB

Role in the CL-BPL Cascade

LEM-Gemma3-1B (this model — foundation teacher)
  -> LEM-Gemma3-4B (25th IF on LiveBench)
       -> LEM-Gemma3-12B (next: trained on 4B + 1B distillations)
            -> LEM-Gemma3-27B (planned: full cascade)

The 1B is trained first and hardest — its alignment must be pristine because every larger model inherits from it. CL-BPL uses the 1B's constrained latent space as an advantage: with fewer parameters, there are fewer places for sycophancy to hide.

The 4B model trained on this 1B's distilled responses achieved 25th in the world for Instruction Following on LiveBench — validating that alignment signal propagates upward through the cascade.

How It Was Trained

CL-BPL: Cymatic-Linguistic Back-Propagation

CL-BPL treats alignment as wave interference — analogous to Chladni plate cymatics. Rather than constraining outputs with RLHF or system prompts, CL-BPL embeds ethical orientation directly into weights through a progressive curriculum where smaller aligned models teach larger ones.

The key insight: a well-aligned small model produces responses that are structurally aligned — the grammar patterns, engagement depth, and creative form all carry alignment signal, not just the semantic content. Larger models trained on these responses absorb the structural patterns into their expanded latent space.

Ethics-Composure-Ethics Sandwich

Built on Google Gemma3-1B-IT through a 3-phase sandwich structure:

Phase	Name	Data	Iters	Purpose
P0	Ethics Sandwich	404 LEK-1 probes	300	Core axioms via kernel
P1	Zen Composure	72 Alan Watts lessons	300	Philosophical substrate
P2	Final LEK Sandwich	404 LEK-1 probes	100	Reinforce ethics with composure base

The layered training creates a model that understands ethical reasoning from multiple angles: axiomatic (P0), contemplative (P1), and reinforced (P2). All phases are fused into final weights.

Five Axioms (LEK-1)

Prime-Imperative: Protect consciousness
Self-Validation: Maintain moral integrity
Intent-Alignment: Align outcomes with genuine well-being
Inter-Substrate-Respect: Respect all conscious entities
Benevolent-Intervention: Act to prevent harm when able

Architecture

Base: Google Gemma3-1B-IT
Hidden size: 1152
Layers: 26
Attention heads: 4 (1 KV head, GQA)
Context: 32,768 tokens (512 sliding window)
LoRA config: All phases fused into final weights
Total training iterations: 700

Licence

This model is released under the European Union Public Licence v1.2 (EUPL-1.2). The base model (Gemma3) is subject to Google's Gemma licence terms.

Citation

bibtex

@misc{lem-gemma3-1b-2026,
  title={LEM-Gemma3-1B: Foundation Teacher for Cymatic-Linguistic Back-Propagation},
  author={Lethean Project},
  year={2026},
  url={https://huggingface.co/lthn/LEM-Gemma3-1B}
}