Proper-scale LoRA SFT checkpoint — 10x the data of sft-v0 (2232 train examples, 40 personas, memory-aware conversational format), 2 epochs. Best SFT-only checkpoint in this project; the DPO and distillation checkpoints chain off this one.

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 from dpo-v1-scale, not this checkpoint).

Model Details

Property	Details
Model	`onebee-gf-sft-v1`
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, 2 epochs, batch 8 / grad-accum 4
License	Apache-2.0 (inherited from base model)

Intended Use

As a base for further post-training (DPO/distillation), or for studying the isolated effect of SFT before preference optimization is applied.

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 retrieved memories
Improved abstention calibration over sft-v0 after a documented bug-fix cycle

Quick Start

Installation

pip install transformers torch

Usage

python

from transformers import AutoModelForCausalLM, AutoProcessor

model = AutoModelForCausalLM.from_pretrained("arrochi112/onebee-gf-sft-v1")
processor = AutoProcessor.from_pretrained("arrochi112/onebee-gf-sft-v1")

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
SFT v1 + memory (pre-DPO)	—	— (see full writeup; DPO adds the measured gain)
+ DPO (dpo-v1-scale)	—	70.0%

Full methodology, all numbers, and honest limitations: docs/proper_scale_results.md.

Limitations

SFT alone, no preference optimization — use onebee-gf-dpo-v1-scale or onebee-gf-distill-v1 for the strongest results. Single seed/run.

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

bibtex

@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).