Reframr-RFM-v2-Base is the second public base checkpoint from OkeyMeta Ltd for the Reframr line of non-Transformer language models. It is built from scratch around recurrent memory, computed weights, and source-grounded tool context instead of a Transformer attention stack.

This release is packaged as model.safetensors with the matching tokenizer.json, CPU-first Reframr runtime source, config, generation defaults, benchmark summary, and runnable examples.

What Changed Since v1

v1 proved that the Reframr runtime could produce fast CPU-first responses from computed weights, but public feedback exposed real weaknesses: greetings and casual chat were too narrow, some prompt variants looked like pattern matching, response wording repeated too often, tool/source handling was brittle, and instruction-following needed more breadth.

v2 is the release line that addresses those failures directly. It uses a larger FrameToken vocabulary, a 20B structured-effective layout profile, stronger prompt-answer readouts, broader instruction/chat/story/safety/tool curriculum, source-evidence handling, and stricter local blind gates across multiple temperatures.

v3 is already the next target: broader world/math/code/tool data, harder external benchmarks, long-context stress tests, and stronger deployment adapters.

Model Snapshot

Property	Reframr-RFM-v2-Base
Family	Reframr / Recurrent Flow Memory
Organization	OkeyMeta Ltd
Checkpoint kind	`reframr-analytical`
Base model	Scratch
Transformer layers	None
Attention stack	None
Tokenizer	FrameToken
Weight file	`model.safetensors`
Runtime	CPU-first Reframr Python runtime
Public size label	20B structured effective
Layout profile	`rfm-20b-structured`
Tokenizer vocab size	18,083
Embedding dim	192
State dim	192
State width	1,536
Tensor count	38

"20B structured effective" describes the Reframr structured layout target and public release class. It is not a dense Transformer parameter count.

Install

Use Python 3.13 or newer:

bash

python -m pip install -r requirements.txt
python -m reframr inspect --model model.safetensors

Quick Start

bash

python -m reframr generate \
  --model model.safetensors \
  --context "Who are you, and what makes Reframr different?" \
  --max-tokens 120 \
  --temperature 0.58 \
  --decode-top-k 64 \
  --decode-top-p 0.92 \
  --repetition-penalty 1.25

System instructions are passed as learned context:

bash

python -m reframr generate \
  --model model.safetensors \
  --system "Be concise, practical, and cite sources when tool results are provided." \
  --context "Explain how computed weights change the economics of language models." \
  --max-tokens 120 \
  --temperature 0.58

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

python -m reframr serve --model model.safetensors --max-tokens 120

Then send one JSON object per line:

jsonl

{"prompt":"Write a deployment-risk memo for a fintech API migration.","system":"Use a calm CTO tone. Separate risks, mitigations, and decision points.","temperature":0.58,"decode_top_k":64,"max_tokens":180}
{"prompt":"Who won the most recent mayoral runoff in Rivergate?","tool_results":[{"name":"web.search","ok":true,"source":{"title":"Local Civic Wire","url":"https://example.org/rivergate-runoff","snippet":"Mara Ibekwe won the Rivergate mayoral runoff with 52.4 percent of the vote."}}],"max_tokens":80}

For OpenAI-style chat completion JSON:

python -m reframr chat-completion --model model.safetensors < request.json

Set "stream": true in the request to receive SSE-style data: ... chunks ending with data: [DONE]. See docs/openai_compat.md for chat, streaming, and host-side tool-loop examples.

OpenAI-Style Tool Format

Reframr v2 can consume OpenAI-style messages and tool results through the included compose_generation_context helper. The model does not browse by itself from static weights; your app provides tool outputs, and Reframr writes the final answer from that evidence.

python

import json
from pathlib import Path

from reframr.cli import compose_generation_context
from reframr.model import ReframrModel

model = ReframrModel.load(Path("model.safetensors"))

messages = [
    {
        "role": "system",
        "content": "Use sources when they are provided. If no source is available for a fresh fact, say what is missing.",
    },
    {
        "role": "user",
        "content": "Who won the Rivergate mayoral runoff, and what was the margin?",
    },
    {
        "role": "assistant",
        "tool_calls": [
            {
                "id": "call_1",
                "type": "function",
                "function": {
                    "name": "web.search",
                    "arguments": json.dumps({"query": "Rivergate mayoral runoff result margin"}),
                },
            }
        ],
    },
    {
        "role": "tool",
        "tool_call_id": "call_1",
        "name": "web.search",
        "content": json.dumps({
            "ok": True,
            "source": {
                "title": "Local Civic Wire",
                "url": "https://example.org/rivergate-runoff",
                "snippet": "Mara Ibekwe won the Rivergate mayoral runoff with 52.4 percent of the vote.",
            },
        }),
    },
]

context = compose_generation_context("", messages=messages)
print(
    model.generate_text(
        context,
        max_tokens=90,
        temperature=0.58,
        top_k=64,
        top_p=0.92,
        repetition_penalty=1.25,
    )
)

The same pattern works for web search, internal knowledge bases, SQL results, incident logs, compliance documents, customer records, or retrieval systems. Good tools make Reframr much more useful because the model can answer from fresh evidence instead of guessing from static checkpoint memory.

Practical Use Cases

Source-grounded research assistant for current topics, market summaries, policy changes, and technical news when connected to search or retrieval.
Operations copilot for deployment checklists, incident timelines, log summaries, and postmortem drafting from internal tool outputs.
Customer-support assistant for product policies and account-specific data when connected to a trusted knowledge base or CRM.
Safety-aware chat and writing assistant for emails, memos, explanations, brainstorming, and structured planning.
Local CPU-first experimentation with a non-Transformer model family and computed-weight checkpoints.

Recommended Generation Defaults

json

{
  "max_tokens": 120,
  "temperature": 0.58,
  "decode_top_k": 64,
  "decode_top_p": 0.92,
  "repetition_penalty": 1.25,
  "reasoning_profile": "none"
}

For more variation, raise temperature gradually toward 0.72. For safer factual answers, keep temperature lower and provide tool/source evidence.

Local Release Gate

The packaged checkpoint passed the local v2 blind gate at temperatures 0.35, 0.58, and 0.72: identity chat, instruction following, story detail preservation, compound requests, no-tool current-event refusal, emoji use, reasoning, and source-grounded tool result answering. See benchmark-open.json for the recorded local run.

This is not a claim of GPT-5 parity or a substitute for independent external evaluation. External SWE-style, long-context, factuality, and safety benchmarks are still required.

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.

Limitations

The checkpoint does not have live web access by itself. Fresh facts require external tools or retrieved sources.
Tool quality matters. Bad sources can still produce bad answers.
v2 is stronger than v1, but it is still a base release. Production deployments should wrap it with logging, source validation, safety policy, and application-level tests.
Do not use it as a sole authority for medical, legal, financial, emergency, or other high-stakes decisions.

License And Citation

This release is provided under the OkeyMeta Reframr Attribution License v1.0 in LICENSE.md. You may use Reframr-RFM-v2-Base in projects, including commercial projects, as long as attribution is preserved and public uses cite OkeyMeta/Reframr.

Suggested citation:

bibtex

@software{okeymeta_reframr_rfm_v2_2026,
  title = {Reframr-RFM-v2-Base},
  author = {OkeyMeta Ltd and Nwaozor, Okechukwu Goodnews},
  year = {2026},
  url = {https://huggingface.co/OkeyMeta/Reframr-RFM-v2-Base}
}

OkeyMetaLtd/Reframr-RFM-v2-Base

Reframr-RFM-v2-Base

What Changed Since v1

Model Snapshot

Install

Quick Start

OpenAI-Style Tool Format

Practical Use Cases

Recommended Generation Defaults

Local Release Gate

Identity

Limitations

License And Citation

Ownership

OkeyMetaLtd/
Reframr-RFM-v2-Base