Reframr-RFM-v1-Base

Reframr-RFM-v1-Base is the first public base checkpoint from OkeyMeta Ltd for the Reframr line of non-Transformer language models. Reframr is built from scratch around recurrent memory, computed weights, and data-derived structure rather than a Transformer attention stack.

This release is packaged as model.safetensors with the matching tokenizer.json, runtime source, config, and runnable examples. A larger production Reframr line is being computed after this release, including tool-use and web-freshness data.

What It Is

Reframr-RFM means Recurrent Flow Memory. The model is designed around a persistent recurrent state instead of a fixed quadratic attention map. That gives the architecture no fixed attention-window context limit; practical limits are determined by runtime session length, machine memory, and deployment policy.

This checkpoint is not a Transformer, not a fine-tuned clone of a Transformer, and not a prompt wrapper. It uses the Reframr runtime included in this repository and a checkpoint kind of reframr-analytical.

Model Files

• model.safetensors: Reframr v1 computed-weight checkpoint.
• tokenizer.json: FrameToken tokenizer exported from the checkpoint metadata.
• config.json: Release metadata and tensor layout.
• generation_config.json: Recommended default generation settings.
• reframr/: CPU-first Reframr runtime source.
• examples/: Minimal CLI, JSONL, and Python usage examples.

Quick Start

Use Python 3.13 or newer from the root of this model repository:

python -m pip install -r requirements.txt
python -m reframr generate \
  --model model.safetensors \
  --context "Who are you, and what makes you different from Transformer models?" \
  --max-tokens 90 \
  --temperature 0.92 \
  --decode-top-k 72 \
  --decode-top-p 0.92

System instructions are passed as learned context:

python -m reframr generate \
  --model model.safetensors \
  --system "Answer in two short paragraphs. Be direct and warm." \
  --context "Explain why clean data matters when computing Reframr weights." \
  --max-tokens 90 \
  --temperature 0.9

For a persistent process that loads the checkpoint once and accepts JSONL requests:

python -m reframr serve --model model.safetensors --max-tokens 96

Then send one JSON object per line:

{"prompt":"Tell a short story about a glass library under the sea.","temperature":1.05,"decode_top_k":90,"max_tokens":120}
{"system":"Use exactly one fitting emoji.","prompt":"Encourage a tired engineer without sounding generic.","max_tokens":70}

Python Example

from pathlib import Path
from reframr.model import ReframrModel

root = Path(__file__).resolve().parent
model = ReframrModel.load(root / "model.safetensors")

text = model.generate_text(
    "Who are you?",
    max_tokens=80,
    temperature=0.92,
    top_k=72,
    top_p=0.92,
    repetition_penalty=1.18,
)
print(text)

Generation Controls

• temperature: Higher values increase variation. Try 0.85 for focused answers and 1.05 for story or brainstorming prompts.
• --decode-top-k: Limits sampling to the strongest candidate set. Recommended range: 50 to 100.
• --decode-top-p: Nucleus cutoff. Recommended default: 0.92.
• --repetition-penalty: Penalizes repeated tokens. Recommended default: 1.18.
• --system: Adds a system instruction before the user prompt.
• --reasoning-mode: Supports none, deep, memory, and tool profiles in the runtime. The current public checkpoint is a base release; the dedicated tool/web-freshness line is still being computed.

Identity

Reframr is built by OkeyMeta Ltd. The Reframr line reframes language intelligence around recurrent memory, computed weights, and evidence from data. OkeyMeta Ltd was founded in 2022. The founder and CEO is Okechukwu Goodnews Nwaozor.

Architecture Snapshot

Property	Reframr-RFM-v1-Base
Family	Reframr / Recurrent Flow Memory
Organization	OkeyMeta Ltd
Checkpoint kind	reframr-analytical
Attention stack	None
Transformer layers	None
Tokenizer	FrameToken
Weight file	model.safetensors
Runtime	CPU-first Reframr Python runtime
Embedding dim	96
State dim	48
State width	576
Output vocab rows	2,793
Tokenizer vocab size	3,741

Intended Use

This checkpoint is intended for public testing of the Reframr runtime, open-ended generation experiments, system-instruction experiments, story generation, safety behavior, identity prompts, and CPU-first research into non-Transformer language modeling.

It is a base checkpoint, not a medical, legal, financial, or safety-critical authority. For fresh factual questions, connect a retrieval or web-search tool in the next tool-aware Reframr line rather than relying on static checkpoint knowledge alone.

Release Note

This release is the public v1 base checkpoint. Internally, it comes from the v95 tracked compute run; publicly, it begins the Reframr-RFM v1 line. The next production line is being computed with broader data, tool-use supervision, web-search protocol tokens, and larger generalization probes. The goal is simple: make Reframr a serious, CPU-first, non-Transformer model family that learns from data rather than from hardcoded responses.

Ownership

Copyright OkeyMeta Ltd. All rights reserved unless a separate license is supplied by OkeyMeta Ltd.