| --- |
| license: apache-2.0 |
| language: |
| - en |
| - fr |
| library_name: transformers |
| pipeline_tag: text-generation |
| tags: |
| - reasoning |
| - chain-of-thought |
| - structured-generation |
| - function-calling |
| - agentic |
| - edge |
| - small-language-model |
| base_model: AMFORGE/samg |
| model-index: |
| - name: SAM-G-Reasoning |
| results: |
| - task: |
| type: multi-step-reasoning |
| name: Verified multi-step reasoning (12 families, held-out) |
| metrics: |
| - type: exact_match |
| value: 77.8 |
| name: Exact match, aggregate (%) |
| --- |
| |
| # SAM-G-Reasoning |
|
|
| **SAM-G-Reasoning** is a 30.3M-parameter model fine-tuned from |
| [SAM-G](https://huggingface.co/AMFORGE/samg) on 196k verified multi-step |
| reasoning traces and action plans. It emits explicit step-by-step traces |
| (`step 1: ... step 2: ... Answer: X`) for questions and ordered JSON plans for |
| multi-step instructions. Built by **AMEFORGE** for procedural reasoning on the |
| edge. |
|
|
| - **Parameters:** 30.3M · **Footprint:** 121 MB fp32 · **Base:** SAM-G |
| - **Fine-tuning:** prompt-masked SFT (loss on the reasoning span only), cosine |
| 8e-5, 8k steps |
| - **Aggregate exact-match:** 77.8% (held-out, disjoint seed) |
|
|
| ## What it is good at — and what it is not |
|
|
| The model was stress-tested on twelve verified families. The pattern is clear: |
| it excels at **procedural** reasoning (following steps, tracking state, chaining |
| actions) and is limited on **calculation-heavy** tasks, as expected at 30M |
| parameters. |
|
|
| | Family | Exact % | Type | |
| |---|---|---| |
| | logic (ponens/tollens/chains) | 100 | procedural | |
| | plan_chain (multi-step actions) | 100 | procedural | |
| | conversion (unit chains) | 100 | procedural | |
| | sequence (next term) | 100 | procedural | |
| | date_time (clock/calendar) | 92 | procedural | |
| | compare (max/min) | 92 | procedural | |
| | **state_track (device toggles)** | **83** | **working memory** | |
| | parity_digits | 58 | mixed | |
| | count_filter | 67 | calculation | |
| | sort_list | 50 | calculation | |
| | word_problem | 50 | calculation | |
| | arith_chain | 42 | calculation | |
| |
| State-tracking at 83% is notable for this scale — it requires maintaining a |
| mutable state across several operations. Arithmetic-chain and sorting plateau |
| because exact multi-digit calculation is not reliably learnable at 30M; for |
| those, delegate to a tool rather than the model. |
| |
| ## Intended use |
| |
| Agentic control loops: decompose an instruction into ordered steps, track |
| execution state, and emit structured action plans — entirely offline. Best used |
| as the **planning and state-tracking layer** of an agent, with arithmetic and |
| data lookups delegated to deterministic tools. |
|
|
| ## Usage |
|
|
| ```python |
| import sentencepiece as spm, torch |
| sp = spm.SentencePieceProcessor(); sp.Load("samg_tokenizer.model") |
| prompt = "states: lamp=off, fan=on. ops: toggle lamp, turn off fan, toggle lamp. final state of lamp? [CHAT]" |
| ids = torch.tensor([sp.EncodeAsIds(prompt)]) |
| # greedy-decode -> "step 1: ... step 2: ... step 3: ... Answer: off" |
| ``` |
|
|
| ## Limitations |
|
|
| - Calculation-heavy families (arithmetic, sorting, word problems) plateau at |
| 42–50%; do not use for exact math — delegate to tools. |
| - Reasoning traces are synthetic, drawn from the training distribution family |
| with a disjoint evaluation seed. |
| - Not a general assistant; inherits the base model's knowledge limits. |
|
|
| ## Citation |
|
|
| ```bibtex |
| @misc{samgreasoning2026, |
| title = {SAM-G-Reasoning: Procedural Multi-Step Reasoning at 30M Parameters}, |
| author = {AMEFORGE Lab}, |
| year = {2026} |
| } |
| ``` |
