SonaMath-0.5B

SonaMath-0.5B

Important (please read)

The weights in this repository are from the SFT stage only.

• They are not trained with our experimental RL method.
• They are not a drop-in replacement for GRPO / group-based optimizers.
• GSM8K numbers below measure this SFT checkpoint, not an RL algorithm.

We also research a new RL formulation (intended as an alternative to GRPO-style group optimization) and a custom reasoning architecture for small LMs. That work is active / unfinished and will be reported separately when we have matched-compute experiments and RL checkpoints.

This page is a transparent research preview under limited compute — not a claim that “new RL already beats GRPO.”

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SonaMath is a research preview of a custom architecture for math reasoning in the small-language-model (SLM) regime (approx. 0.5B parameters), trained under a strict compute budget (approx. 3B pretrain tokens + light SFT).

Formerly previewed as JunMath; the project brand is now SonaMath.

What this release is / is not

How to use (inference)

Important: SonaMath uses a custom architecture.
It does not load with AutoModelForCausalLM.from_pretrained(...) / standard Hugging Face pipeline yet.
Public files are weights + tokenizer; the research runtime loader is required to run generation.

1) Download files from this repo

# Option A: huggingface-cli
huggingface-cli download HaniAI/SonaMath-0.5B --local-dir ./SonaMath-0.5B

# Option B: Python
pip install -U "huggingface_hub>=0.23"

from huggingface_hub import snapshot_download

path = snapshot_download("HaniAI/SonaMath-0.5B", local_dir="SonaMath-0.5B")
print("downloaded to", path)
# weights: SonaMath-0.5B/sonamath.pt
# tokenizer: SonaMath-0.5B/tokenizer.json

Downloading the repo does count as usage and is the supported way to fetch artifacts while the full open runtime is still under development.

2) Prompt format (required)

The model is trained with explicit problem / thinking tags. Build prompts like:

<problem>
{your grade-school math word problem here}
</problem>
<think>

The model continues with reasoning and typically closes with:

... reasoning ...
<answer>
{final number}
</answer>

3) Recommended decoding (matches public GSM8K number)

4) Pseudocode (custom research runtime)

# Pseudocode — requires the SonaMath research runtime (not a stock transformers model).
# Full open loader will be linked here when released.

from sonamath_runtime import load_sonamath, generate  # research package (not on PyPI yet)

model, tokenizer = load_sonamath(
    hub_id="HaniAI/SonaMath-0.5B",   # or local folder from snapshot_download
    device="cuda",
)

problem = (
    "Natalia sold clips to 48 of her friends in April, and then she sold "
    "half as many clips in May. How many clips did Natalia sell altogether "
    "in April and May?"
)

text = generate(
    model,
    tokenizer,
    problem=problem,
    max_new_tokens=1024,
    temperature=0.0,  # greedy — same as public eval
)
print(text)

5) What works today vs later

If you only need artifacts for inspection, fine-tuning research, or offline study:

from huggingface_hub import hf_hub_download

ckpt = hf_hub_download("HaniAI/SonaMath-0.5B", "sonamath.pt")
tok  = hf_hub_download("HaniAI/SonaMath-0.5B", "tokenizer.json")
print(ckpt, tok)

6) Not supported (yet)

# ❌ Will NOT work — not a standard transformers architecture export
from transformers import AutoModelForCausalLM, AutoTokenizer
model = AutoModelForCausalLM.from_pretrained("HaniAI/SonaMath-0.5B")  # no

Questions / runtime access requests: open a Discussion on this model page.

Two research tracks (keep them separate)

Track A — Architecture + data-efficient SFT (this repo)

• Custom architecture specialized for multi-step math reasoning in small LMs
• Pretrain approx. 3B tokens (math-focused, English-centric)
• Light SFT on math Q&A / solution-style data
• Public artifact: sonamath.pt (SFT-stage, weights only)

Track B — RL theory & methods (research in progress; not this checkpoint)

• Studying a new RL formulation as an alternative to group-based / GRPO-style post-training
• Status: theory + early experiments — no claim of superiority on this model card
• Will not use Track A’s GSM8K score as “proof” of Track B

SFT checkpoint for architecture research now; RL-vs-GRPO is future work with separate evals.

Evaluation (SFT checkpoint only)

Protocol

Results

Notes

• This is the authoritative public number for this release (full test set).
• Absolute accuracy is well below large or math-specialized models — expected under approx. 3B pretrain tokens.
• When Track B (RL) is ready, we will report SFT vs GRPO-style vs our RL under matched compute, on separate checkpoints.

Highlights

Files

Intended use

• Research discussion on small reasoning LMs and data-efficient math training
• Download weights/tokenizer for inspection and future runtime integration
• Planning compute for larger pretrain, cleaner SFT, and future RL ablations

Not intended for: production tutoring, grading, high-stakes decisions, or citing this repo as an RL result.

Limitations

• Low absolute GSM8K accuracy vs large / math-specialized models
• May hallucinate arithmetic and multi-step logic — verify answers
• Custom architecture: full inference stack not fully open as transformers yet
• No public RL checkpoint in this release

Compute context & GPU ask

Open a Discussion on this page if you can support A100/H100 hours for scale-up and matched-compute RL ablations.

Safety

Outputs can be confidently wrong. Do not use without human checking for education, finance, or safety-critical settings.

Citation

@misc{sonamath2026sft,
  title  = {SonaMath-0.5B: SFT-Stage Research Preview of a Small Math Reasoning Model},
  author = {HaniAI},
  year   = {2026},
  note   = {SFT weights only; GSM8K full test 13.8\\% (182/1319), greedy, max\\_new=1024; RL research is separate},
  url    = {https://huggingface.co/HaniAI/SonaMath-0.5B}
}

License

Apache-2.0 (weights and tokenizer files in this repository).