onebee-gf-sft-v0
LoRA SFT checkpoint on gemma-4-E2B-it, Day-4 v0 scale (4 personas, 202 examples) — early-stage baseline, superseded by sft-v1.
Model Overview
Early-scale LoRA SFT checkpoint on top of gemma-4-E2B-it, trained on 202 examples generated from 4 personas. This was the Day-4 baseline before the project scaled up data 10x for sft-v1 — kept published for reproducibility of the v0-scale results, not recommended as a starting point for new work.
GGUF quantizations available: this project's current-best checkpoint is also published as 12-level GGUF quantizations for
llama.cpp-based on-device inference (quantized fromdpo-v1-scale, not this checkpoint).
Model Details
| Property | Details |
|---|---|
| Model | onebee-gf-sft-v0 |
| Parameters | ~2B effective (base) + LoRA rank 16 adapter |
| Architecture | Gemma4 (multimodal, text + vision) |
| Base Model | google/gemma-4-E2B-it |
| Language | English |
| Context Length | 131,072 tokens (inherited from base model) |
| Training Method | LoRA SFT (memory-aware conversational data: persona + retrieved memories + recent turns → response) |
| License | Apache-2.0 (inherited from base model) |
Intended Use
Intended Use
Reproducing this project's v0-scale results (docs/day4_sft_results.md). Not recommended as a base for new work — use onebee-gf-sft-v1 or onebee-gf-distill-v1 instead.
Out-of-Scope Use
Not evaluated or intended for: safety-critical decisions, medical/legal/financial advice, or any deployment where a wrong or overconfident answer causes real harm. This is a research artifact from an open-source project studying post-training and memory architecture on small models — see the project README for the full research framing before using it in any production context.
Capabilities
- Companion-persona conversational responses conditioned on a small set of retrieved memories
- No meaningful preference-alignment (DPO) applied at this stage
Quick Start
Installation
pip install transformers torch
Usage
from transformers import AutoModelForCausalLM, AutoProcessor
model = AutoModelForCausalLM.from_pretrained("arrochi112/onebee-gf-sft-v0")
processor = AutoProcessor.from_pretrained("arrochi112/onebee-gf-sft-v0")
messages = [
{"role": "system", "content": "You are a warm AI companion who remembers this user."},
{"role": "user", "content": "What conference did I say I was attending?"},
]
inputs = processor.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt")
output = model.generate(**inputs, max_new_tokens=128)
print(processor.decode(output[0], skip_special_tokens=True))
Evaluation
Scored against PMB (Personalized Memory Benchmark), 688 adversarial probes across 8 categories, with an LLM judge under dual-order (position-bias-controlled) scoring plus a rule-based abstention detector.
| System | pra_lenient | UAR |
|---|---|---|
| B (SFT v0, no memory) | 0.16% | 16.25% |
| E (SFT v0 + memory) | 17.76% | 33.75% |
Full methodology, all numbers, and honest limitations:
docs/day4_sft_results.md.
Limitations
Small data scale (202 examples, 4 personas) — superseded by sft-v1's 10x-larger, rebalanced dataset. Known false-abstention issues at this scale, root-caused and fixed in the v1 pass (docs/proper_scale_results.md, docs/model_quirks.md #16-17).
This project reports negative/inconclusive results as honestly as positive ones — read the linked docs before assuming any number here is a clean win.
Other Checkpoints From This Project
| Repo | Description |
|---|---|
| onebee-gf-sft-v0 | Day 4 v0 SFT (202 examples) |
| onebee-gf-sft-v1 | Proper-scale SFT (2232 examples) |
| onebee-gf-dpo-v0 | Week 2 DPO v0 (200 pairs) |
| onebee-gf-dpo-v1-4epoch | DPO overfitting experiment |
| onebee-gf-dpo-v1-scale | Proper-scale DPO, pre-distillation |
| onebee-gf-distill-v1 | SFT+DPO+distillation — current best overall |
| onebee-gf-dpo-v1-scale-gguf | GGUF quantizations |
Citation
@software{small_mind_companion,
title = {small-mind-companion: Post-training and cognitive architecture for a small multimodal companion LLM},
author = {arrogance231},
year = {2026},
url = {https://github.com/arrogance231/small-mind-companion}
}
License
Apache-2.0, inherited from the base model (google/gemma-4-E2B-it).