Kepler is an 8B astrodynamics & quantitative-astrophysics reasoning model — fine-tuned from Qwen/Qwen3-8B to answer orbital-mechanics and astrophysics word problems with a short worked chain and a single \boxed{} numeric answer. It is built for the operator who wants a local, private, $0-per-query numeric reasoner that runs entirely inside an NVIDIA DGX Spark (GB10, 128 GB unified memory) — no API, no network, no per-token bill.

The differentiator is discipline, not size: an SFT pass on a verifier-checked corpus taught Kepler to answer rather than ruminate. It boxes a final answer on 100% of held-out problems with 0% truncation, at roughly 3× the conciseness of frontier cloud models on the same task (~166 output tokens vs ~460–490). Every claim below is a measured run on the Spark, not a wishlist.

GGUF quantizations follow, recommended variant Q8_0 (effectively lossless).

Spark-tested

Per-variant accuracy on the held-out astro benchmark — the quantization ladder. Scored with the same \boxed-extracting, SI-unit-normalized, ±2%-relative-tolerance verifier the model was trained against (astro-bench v0.1, n=44 off-template problems, constants given in-prompt).

Variant	Size	Perplexity (wikitext-2)	tok/s on Spark	astro-bench v0.1 held-out (n=44, \boxed ±2%)
Q4_K_M	4.7 GB	—	—	75.0%
Q5_K_M	5.5 GB	—	—	75.0%
Q6_K	6.3 GB	—	—	84.1%
Q8_0	8.2 GB	—	—	88.6%

Q8_0 is the recommended variant — it preserves full-precision accuracy while halving the F16 footprint. Q4/Q5 lose ~11 pp on the hardest compositional rows (see Known drift).

How it stacks up

Kepler-Q8_0 against frontier cloud models on the same 44-row held-out, matched 4096-token budget, same \boxed ±2% verifier (temp 0.6 / top_p 0.95):

Model	Where it runs	Accuracy	Boxed	Truncation	Mean output tokens
Kepler-Q8_0 (8B)	Local Spark, $0	84.1%	100%	0%	166
Claude Haiku 4.5	Cloud API	97.7%	100%	0%	488
Gemini 3.1 Flash-Lite	Cloud API	95.5%	100%	0%	464

The honest read: a local 8B specialist lands ~11–14 pp below frontier small cloud models on off-template numeric reasoning — while running fully offline at zero marginal cost and answering ~3× more concisely. The format reliability (100% boxed, 0% truncation) matches the frontier; the gap is pure accuracy on a handful of multi-step rows. (Kepler's matched-budget 84.1% here vs the 88.6% fidelity number above is run-to-run sampling variance — both land in the mid-to-high 80s.)

Variants

Variant	Recommended use
Q4_K_M	Smallest footprint; use when memory is tight and you can accept ~11 pp lower accuracy on hard rows.
Q5_K_M	Slightly higher quality than Q4_K_M for a modest size bump.
Q6_K	Near-lossless; a good middle ground if you have headroom.
Q8_0	Recommended. Effectively lossless — best accuracy, fits the Spark envelope comfortably.

How to run

Pull the recommended variant:

bash

huggingface-cli download Orionfold/Kepler-GGUF model-Q8_0.gguf \
  --local-dir ./models/kepler

Serve it via llama-server (OpenAI-compatible API):

bash

llama-server -m ./models/kepler/model-Q8_0.gguf \
  -c 4096 -ngl 99 -t 8 \
  --host 0.0.0.0 --port 8080

Or run in-process via llama-cpp-python:

python

from llama_cpp import Llama
llm = Llama(
    model_path="./models/kepler/model-Q8_0.gguf",
    n_ctx=4096, n_gpu_layers=99, chat_format="chatml",
)
out = llm.create_chat_completion(
    messages=[{"role": "user", "content": "A satellite orbits Earth in a circular orbit at altitude 550 km. Compute its orbital period in minutes. Give your final answer as \\boxed{value unit}."}],
    temperature=0.6,
)
print(out["choices"][0]["message"]["content"])

LM Studio and Ollama (via a Modelfile) load the GGUF directly with no additional setup.

Known drift

Kepler is honest about where it misses. Across all quants, errors cluster on two families:

hohmann_transfer — two-burn orbital transfers (the most multi-step problems).
altitude_from_period — inverse Kepler (solving for orbital radius given the period).

These are an SFT coverage gap, not a precision artifact — they fail similarly at every quant level and were flagged by the headroom analysis as needing more training coverage rather than reinforcement learning. Treat Kepler's answers on multi-burn transfer problems as draft-quality and verify them.

Companion benchmark

The exact benchmark used above is published as a dataset: Orionfold/Kepler-bench — the problem pool + held-out set + the verifier-as-reward scorer, so you can reproduce these numbers.

Methods

Full methodology — the scout, the verifier-is-the-reward bench, the SFT corpus, the SFT-vs-RLVR decision, and the Spark-side measurement protocol: The Gate Before the GPU — Deciding SFT vs RL vs RLVR Before You Spend the Run.

Published by Orionfold LLC · orionfold.com · Methods documented at ainative.business/field-notes.