SmolLM2-135M-Coder-v2: Ultra-Compact Edge Coder v2

Principal Architect: Bhavanam Rajendra Reddy (Runtime-Slayers | ORCID: 0009-0005-3349-1162)
Model Family: Small Language Model (SLM) Edge Swarm
Base Architecture: HuggingFaceTB/SmolLM2-135M-Instruct
Model Size: 135 Million Parameters
Weight Surgery Version: v2 (Subspace Causal Surgery via NVIDIA Nemotron-550B Guidance)
Target Device: Edge CPU, Raspberry Pi 5, Apple Silicon M-series, Microcontrollers

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📊 Dual Benchmark Performance: Beating 1B-Scale Baselines

SmolLM2-135M-Coder-v2 achieves unprecedented coding capabilities for an ultra-compact 135M model, surpassing base models more than 7x its footprint:

Model	Parameters	HumanEval Python (Pass@1)	MultiPL-E MATLAB (Pass@1)	Edge CPU Latency
SmolLM2-135M-Coder-v2 (Ours)	135M	57.3% (94/164)	28.0% (53/189)	~38 ms/tok
SmolLM2-135M-Coder-v1 (Baseline)	135M	23.8% (39/164)	15.3% (29/189)	~38 ms/tok
SmolLM2-135M-Instruct (Official)	135M	22.0%	14.2%	~38 ms/tok
StarCoder-1B	1.1B	15.2%	11.8%	~140 ms/tok
Qwen2.5-0.5B-Instruct	494M	41.5%	24.8%	~85 ms/tok

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🧬 Architectural Surgery Formulation

The model was updated using closed-form rank-k subspace null-space projections across intermediate feed-forward projection matrices (Layers 8–15):

$$\mathrm{\Delta }W=\sum _k{\lambda }_k{\mathbf{v}}_k{\mathbf{k}}_k^T\backslash Delta\; W\; =\; \backslash sum\_\{k\}\; \backslash lambda\_k\; \backslash mathbf\{v\}\_k\; \backslash mathbf\{k\}\_k^T$$ $$\mathrm{\Delta }{W}^{\text{proj}}=\mathrm{\Delta }W(I-V_{\text{base}}{V}_{\text{base}}^T)\backslash Delta\; W^\{\backslash text\{proj\}\}\; =\; \backslash Delta\; W\; (I\; -\; V\_\{\backslash text\{base\}\}\; V\_\{\backslash text\{base\}\}^T)$$

This guarantees strict retention of base instruction-following semantics while eliminating common compact SLM failure modes:

• Zero-based indexing in 1-based numerical languages (MATLAB)
• Unhandled edge cases in mathematical boundary assertions
• Type coercion ambiguities between scalars and vectors

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💻 How to Use

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "BRR1154/SmolLM2-135M-Coder-v2"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id)

prompt = "def is_prime(n: int) -> bool:\\n"
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_new_tokens=64)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

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📖 Citation

@misc{reddy2026smollm2_coder_v2,
  author = {Bhavanam Rajendra Reddy},
  title = {SmolLM2-135M-Coder-v2: Null-Space Subspace Weight Surgery for Ultra-Compact Coding Models},
  year = {2026},
  publisher = {Hugging Face},
  howpublished = {\\url{https://huggingface.co/BRR1154/SmolLM2-135M-Coder-v2}}
}