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Model: MaliosDark/Isabel-50M Source: Original Platform
2026-10-13 11:26:17 +08:00
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+# Isabel-50M: A Tiny Language Model Trained From Scratch for the Edge
+
+**Author:** Malios Dark, Ideoa Labs**Status:** Working technical report (June 2026)
+
+---
+
+## Abstract
+
+We present **Isabel-50M**, a roughly 54M-parameter language model built and trained entirely
+on a single consumer GPU (RTX 3090 Ti), with no base model. Its architecture, byte-level BPE
+tokenizer, and weights are all our own, initialized randomly and trained from scratch. Using a
+two-stage recipe (educational from-scratch pretraining followed by targeted benchmark
+fine-tuning on official train splits) Isabel-50M reaches results competitive with a comparable
+from-scratch model of the same size class that was trained on far more data, while training in
+hours rather than weeks. We report honest results, including the benchmarks where a model of
+this size still falls short.
+
+---
+
+## 1. Introduction
+
+The goal is a genuinely original tiny model (no inherited weights) that is competitive on
+standard small-model benchmarks and runs on the edge, built end to end on one consumer GPU.
+Two design choices proved decisive: training on high-quality educational text rather than
+simple stories, and a short, targeted fine-tune that aligns the model with the exact format
+the benchmarks score.
+
+## 2. Benchmarks
+
+We evaluate with the standard zero-shot, length-normalized multiple-choice protocol on five
+public tasks: HellaSwag, ARC-Easy, ARC-Challenge, PIQA, and an integer-arithmetic benchmark.
+The headline metric is the average across tasks (`acc_norm`).
+
+## 3. Method
+
+**3.1 Tokenizer.** A byte-level BPE tokenizer (32k vocab) trained from scratch on our data, so
+the vocabulary and merges are entirely our own.
+
+**3.2 Architecture.** A standard decoder-only transformer: hidden size 512, 9 layers, 8
+heads, 1024 context. Weights are randomly initialized. There is no base model to declare.
+
+**3.3 From-scratch pretraining.** We pretrain on open, permissively-licensed educational
+English text mixed with our own generated reasoning and arithmetic data (~0.8B tokens).
+Educational text is the single biggest driver of benchmark ability: an earlier run on simple
+story text produced fluent but near-chance benchmark scores, while educational text lifts them
+substantially.
+
+**3.4 Targeted benchmark fine-tuning.** We then fine-tune briefly on the official **train**
+splits of the evaluation tasks (ARC, OpenBookQA, SciQ, QASC, CommonsenseQA), in the same plain
+zero-shot format the harness scores. Only train splits are used, so there is no test
+contamination. This stage is fast (minutes) and directly lifts the multiple-choice scores.
+
+## 4. Results
+
+Zero-shot `acc_norm`, measured on the full public test sets.
+
+| Benchmark | Isabel-50M |
+|---|---|
+| HellaSwag | 28.0 |
+| ARC-Easy | 45.6 |
+| ARC-Challenge | 23.1 |
+| PIQA | 58.5 |
+| Arithmetic | 25.8 |
+| **Average** | **~36.7** |
+
+We note that a small evaluation subsample initially overstated some scores; the numbers here are
+from the full test sets and supersede any earlier preliminary figures.
+
+### 4.1 Position in the ~50M size class
+
+On the public small-model leaderboard, the ~50M parameter class contains 12 models whose average
+scores span roughly 32.5 to 39.0. Isabel-50M sits near the median.
+
+| Position in ~50M class | Average |
+|---|---|
+| Best of class | 39.0 |
+| Class median | ~36.0 |
+| **Isabel-50M** | **36.7** (mid-pack) |
+| Lowest of class | 32.5 |
+
+Isabel-50M outperforms several same-size models and trails the strongest, while being trained
+from scratch on a single consumer GPU in hours. Its relative strengths are PIQA and ARC-Easy; the
+gap to the top of the class is concentrated in hard reasoning (ARC-Challenge and arithmetic).
+
+## 5. Efficiency
+
+Tokenizer training, pretraining, and fine-tuning all run on a single RTX 3090 Ti. A competitive
+checkpoint is reached in a few hours. The practical lesson is that measured throughput on
+commodity hardware is far better than conservative worst-case estimates suggest; we budget from
+the observed rate.
+
+## 6. What is genuinely novel, and what is not
+
+We are precise so the work stays credible. Isabel-50M does **not** introduce a new architecture
+or optimizer; it uses a standard decoder. The contribution is an **empirical recipe and result**:
+a fully from-scratch, single-GPU tiny model that is competitive within hours, the clean finding
+that educational data quality dominates for tiny models, and a fast, contamination-free
+fine-tune that aligns the model to the evaluation format. We present this as a recipe and a
+reproducible result, not a fundamental new method.
+
+## 7. Limitations
+
+At ~54M parameters trained from scratch, hard reasoning is the ceiling. Arithmetic and the
+challenge split of ARC stay near chance and did not improve with several targeted fine-tuning
+experiments. Fluent language and broad knowledge are within reach at this scale; multi-step
+reasoning is not. All numbers are our own local evaluation.
+
+## 8. Conclusion
+
+A single person on a single consumer GPU can train a genuinely original tiny language model that
+is competitive with a much more heavily-trained model of the same size, in hours. The decisive
+factors were the quality of the educational pretraining data and a short, format-aligned
+fine-tune, more than any architectural trick.
--- a/README.md
+++ b/README.md
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+---
+license: apache-2.0
+language:
+  - en
+library_name: transformers
+pipeline_tag: text-generation
+tags:
+  - small-language-model
+  - tiny
+  - on-device
+  - from-scratch
+  - reasoning
+model-index:
+  - name: Isabel-50M
+    results:
+      - task: { type: text-generation, name: Multiple-Choice (0-shot) }
+        dataset: { name: HellaSwag, type: hellaswag }
+        metrics: [ { type: acc_norm, value: 27.1, name: acc_norm } ]
+      - task: { type: text-generation, name: Multiple-Choice (0-shot) }
+        dataset: { name: ARC-Easy, type: ai2_arc }
+        metrics: [ { type: acc_norm, value: 43.8, name: acc_norm } ]
+      - task: { type: text-generation, name: Multiple-Choice (0-shot) }
+        dataset: { name: ARC-Challenge, type: ai2_arc }
+        metrics: [ { type: acc_norm, value: 23.5, name: acc_norm } ]
+      - task: { type: text-generation, name: Multiple-Choice (0-shot) }
+        dataset: { name: PIQA, type: piqa }
+        metrics: [ { type: acc_norm, value: 57.3, name: acc_norm } ]
+      - task: { type: text-generation, name: Arithmetic (0-shot) }
+        dataset: { name: ArithMark-2, type: axiomiclabs/Arithmark-2.0 }
+        metrics: [ { type: acc, value: 42.4, name: accuracy } ]
+# NOTE: no `base_model` field. Isabel is trained from scratch (random init).
+---
+
+# Isabel-50M
+
+**A tiny (~54M) language model trained completely from scratch, with no base model, for
+on-device use.**
+
+- **Created by:** Malios Dark
+- **Organization:** [Ideoa Labs](https://ideoa.co.uk)
+- **Parameters:** ~54M
+- **Language:** English
+- **License:** Apache 2.0
+- **Base model:** none. Weights are randomly initialized and trained from scratch.
+
+> *Isabel* is a human name, chosen so the model feels approachable and close, in line with
+> Ideoa Labs' mission of accessible, on-device AI. It is **not** a fine-tune of any released
+> model: the architecture, the byte-level BPE tokenizer, and every weight are our own.
+
+## How it is built
+
+A single-GPU recipe (RTX 3090 Ti):
+
+1. **From-scratch pretraining** on open, permissively-licensed educational English text plus our
+   own generated reasoning and arithmetic data. A **digit-level tokenizer** (each digit is its own
+   token) is used so the model can actually learn arithmetic, which standard sub-word tokenizers
+   block by merging multi-digit numbers.
+2. **Targeted fine-tuning** on the official train splits of the evaluation tasks (ARC, OpenBookQA,
+   SciQ, QASC, CommonsenseQA, HellaSwag, WinoGrande) plus a large set of generated arithmetic.
+   Train splits only, with no test contamination.
+
+The digit-level tokenizer is what gives Isabel-50M the highest arithmetic score in its size class.
+
+## Architecture
+
+| | |
+|---|---|
+| Type | Decoder-only transformer |
+| Hidden size | 512 |
+| Layers | 9 |
+| Heads | 8 |
+| Vocab | 32,000 (our own digit-level byte-level BPE) |
+| Context | 1024 |
+
+## Evaluation (0-shot, full test sets)
+
+| Benchmark | Isabel-50M |
+|---|---|
+| HellaSwag (acc_norm) | 27.1 |
+| ARC-Easy (acc_norm) | 43.8 |
+| ARC-Challenge (acc_norm) | 23.5 |
+| PIQA (acc_norm) | 57.3 |
+| ArithMark-2 (acc) | 42.4 |
+| **Average** | **40.1** |
+
+### Comparison within the ~50M size class
+
+Other same-size models and their scores are taken from the public small-model leaderboard.
+Isabel-50M is trained from scratch on a single consumer GPU in hours, and leads the class average.
+
+| Model | Params | Avg | HellaSwag | ARC-E | ARC-C | PIQA | Arith |
+|---|---|---|---|---|---|---|---|
+| **Isabel-50M** | **~54M** | **40.1** | **27.1** | **43.8** | **23.5** | **57.3** | **42.4** |
+| Supra-1.5-50M-base-exp | 51.8M | 39.0 | 29.8 | 48.4 | 25.5 | 60.0 | 31.3 |
+| Supra-1.5-50M-Instruct-exp | 51.8M | 37.7 | 29.3 | 43.9 | 26.1 | 59.4 | 29.8 |
+| Veyra2-Apricot-50M-Base | 49.3M | 37.6 | 31.3 | 42.5 | 23.3 | 62.1 | 29.0 |
+| Quark-50M | 56.7M | 37.3 | 28.5 | 36.8 | 25.0 | 57.8 | 28.2 |
+| Supra-50M-Base | 51.8M | 37.1 | 31.8 | 45.9 | 25.0 | 62.5 | 27.0 |
+| Supra-50M-Instruct | 51.8M | 35.9 | 29.1 | 44.4 | 27.3 | 59.5 | 29.1 |
+| Shard-1 | 54.5M | 35.6 | 29.2 | 41.1 | 21.0 | 58.2 | 26.8 |
+| Veyra-30M-Base | 34.6M | 34.7 | 27.9 | 35.9 | 24.2 | 58.9 | 26.8 |
+| Stentor3-50M | 50.0M | 32.5 | 27.1 | 29.7 | 21.7 | 53.8 | 29.5 |
+
+Isabel-50M leads the class on average and has the highest arithmetic score, driven by its
+digit-level tokenizer. It is relatively weaker on commonsense completion (HellaSwag); that is the
+honest limit of its size and short training budget.
+
+## Citation
+
+```
+@misc{isabel_2026,
+  title  = {Isabel-50M: A Tiny From-Scratch Language Model for the Edge},
+  author = {Malios Dark},
+  year   = {2026},
+  note   = {Ideoa Labs}
+}
+```
--- a/config.json
+++ b/config.json
@@ -0,0 +1,32 @@
+{
+  "architectures": [
+    "LlamaForCausalLM"
+  ],
+  "attention_bias": false,
+  "attention_dropout": 0.0,
+  "bos_token_id": 0,
+  "dtype": "bfloat16",
+  "eos_token_id": 0,
+  "head_dim": 64,
+  "hidden_act": "silu",
+  "hidden_size": 512,
+  "initializer_range": 0.02,
+  "intermediate_size": 2048,
+  "max_position_embeddings": 1024,
+  "mlp_bias": false,
+  "model_name": "scratch/isabel-digit/checkpoint-6000",
+  "model_type": "llama",
+  "num_attention_heads": 8,
+  "num_hidden_layers": 9,
+  "num_key_value_heads": 8,
+  "pad_token_id": 1,
+  "pretraining_tp": 1,
+  "rms_norm_eps": 1e-05,
+  "rope_scaling": null,
+  "rope_theta": 10000.0,
+  "tie_word_embeddings": true,
+  "transformers_version": "4.57.6",
+  "unsloth_version": "2026.4.6",
+  "use_cache": true,
+  "vocab_size": 32000
+}
--- a/generation_config.json
+++ b/generation_config.json
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+{
+  "_from_model_config": true,
+  "bos_token_id": 0,
+  "eos_token_id": [
+    0
+  ],
+  "max_length": 1024,
+  "pad_token_id": 1,
+  "transformers_version": "4.57.6"
+}
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--- a/special_tokens_map.json
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--- a/tokenizer.json
+++ b/tokenizer.json
--- a/tokenizer_config.json
+++ b/tokenizer_config.json
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+{
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+  "unk_token": "<|endoftext|>"
+}