README.md

---
language:
- en
tags:
- reframr
- okeymeta
- non-transformer
- recurrent-memory
- computed-weights
- cpu-inference
- safetensors
library_name: reframr
pipeline_tag: text-generation
license: other
base_model: scratch
---

# 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:

```bash
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:

```bash
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:

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

Then send one JSON object per line:

```jsonl
{"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

```python
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.