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crumb-ai/
abl_2_1_interleaved

Name: abl_2_1_interleaved
Availability: InStock

bigcode/the-stack-dedup-v2

mit

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CRUMB `abl_2_1_interleaved` ⭐ Best in Phase-1 Ablation

Model Overview

abl_2_1_interleaved is the best-performing model in the CRUMB Phase-1 ablation. It is a hybrid decoder-only language model that interleaves Mamba-3 selective state-space layers with GQA (Grouped-Query Attention) layers at a 2:1 Mamba-majority ratio with interleaved placement, and was pre-trained exclusively on Python source code.

Across the eleven ablation variants, this configuration achieves the lowest evaluation perplexity (3.4182), confirming that a moderate Mamba majority (67 % of layers) with attention distributed throughout the stack is the optimal design choice at the 150M-parameter scale.

Architecture

Property	Value
Total parameters	146,075,776 (146.1M)
`d_model`	768
`n_layers`	12
`n_heads`	12
`n_kv_heads`	4 (GQA)
`d_head`	64
`d_ff`	3072
`vocab_size`	32768
`seq_len`	4096
Tie embeddings	yes
Pos. encoding	RoPE (base = 10000)
Mamba layer type	Mamba-3 (d_state=64, expand=2, headdim=64, ngroups=1, chunk=64)

Mamba : Attention ratio — 2 : 1

8 Mamba layers + 4 GQA attention layers (2 Mamba blocks per attention block).

Placement — Interleaved

Mamba and attention layers alternate throughout the network according to a 2:1 schedule. Layer order: M A M M A M M A M M A M

This placement distributes the attention layers as evenly as the 2:1 ratio allows while keeping every attention layer close to a Mamba layer that provides the broad sequential context.

Training

Property	Value
Training data	Python subset of `bigcode/the-stack-dedup-v2`
Tokens seen	5,367,398,400 (~5.37 B)
Steps	163,840
Context length	4096
Training time	47 h 24 m 43 s
Final learning rate	3.00e-05

Evaluation Method

Perplexity (primary metric)

Per-token cross-entropy loss with BF16 autocast, computed over the full held-out evaluation set.

Setting	Value
Eval sequences	20,063 batches
Eval tokens	328,631,940
Implementation	`src/evaluation/perplexity.py`

Generation-based metrics

Python syntax validity — 200 free-form completions generated per model from 49 diverse Python prompts at temperature=0.8, top_k=50, max_new_tokens=128; each completion checked with ast.parse(). Implementation: src/evaluation/syntax_validity.py.
Qualitative side-by-side completions — 10 fixed prompts at temperature=0.6, top_k=50, max_new_tokens=200, identical random seed per prompt. Implementation: src/evaluation/qualitative_comparison.py.

Evaluation Results

Metric	Value
Eval loss	1.2291 ⭐ (best)
Eval perplexity	3.4182 ⭐ (best)
Eval time	3,175.94 s (~53 min)
Syntax validity (n=200)	55 / 200 → 27.5 %
Inference gen. time (200×128 tok)	180.33 s

Rank Summary

Out of 11 ablation configurations evaluated at the same token budget:

Rank	Model	Perplexity
1	`abl_2_1_interleaved`	3.4182 ⭐
2	`abl_3_1_interleaved`	3.4359
3	`abl_3_1_backloaded`	3.4493
4	`abl_2_1_backloaded`	3.4683
5	`abl_1_1_backloaded`	3.4763
6	`abl_pure_mamba`	3.5237
7	`abl_1_1_interleaved`	3.5407
8	`abl_pure_attn`	3.5939
9	`abl_3_1_frontloaded`	3.6798
10	`abl_2_1_frontloaded`	3.7078
11	`abl_1_1_frontloaded`	3.7315

abl_2_1_interleaved is recommended as the base configuration for Phase 2 of the CRUMB project. The overall spread across all 11 configurations is 0.314 perplexity points (9.2 %); this model is 0.176 PPL (4.9 %) better than the worst model (abl_1_1_frontloaded) and 0.105 PPL (3.0 %) better than the best pure baseline (abl_pure_mamba).

Intended Use & Limitations

Domain: Python source-code language modelling.
Base model only: no instruction tuning, no chat alignment, no safety filtering. Outputs are unconstrained code completions.
Repetitive degeneration: all base CRUMB models tend to repeat function signatures / docstrings during free-form generation; this is expected behaviour for unaligned base models.

Citation / Context

This model is part of the CRUMB Phase-1 ablation study:

Efficient Architectural Hybrids for Small-Scale Language Models in Python Program Synthesis — Department of Computer Science and Engineering, Daffodil International University. Findings documented in documents/phase1_ablation_findings.md.

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