README.md

metadata

language:
  - en
license: apache-2.0
library_name: transformers
pipeline_tag: text-generation
base_model:
  - Qwen/Qwen2.5-0.5B-Instruct
  - BRR1154/SLM-WebDev-v1
tags:
  - webdev
  - html5
  - accessibility
  - frontend
  - qwen2.5
  - null-space-lora
  - slm-v2
model-index:
  - name: SLM-WebDev-v2
    results:
      - task:
          type: text-generation
          name: Text Generation
        dataset:
          name: HTML5-AST-UI-12
          type: domain-eval
        metrics:
          - name: Domain Execution / Solver Accuracy (After v2)
            type: accuracy
            value: 58.33
          - name: Domain Execution / Solver Accuracy (Before v1 Baseline)
            type: accuracy
            value: 25
      - task:
          type: multiple-choice
          name: Multiple Choice
        dataset:
          name: ARC-Easy-25 General Retention Guard
          type: arc-easy
        metrics:
          - name: ARC-Easy-25 Raw / Calibrated (%)
            type: accuracy
            value: 76

SLM-WebDev-v2: Accessible HTML5 AST UI Synthesis Specialist (v2)

Lead Architect: Bhavanam Rajendra Reddy (Runtime-Slayers | ORCID: 0009-0005-3349-1162)
Base v1 Checkpoint: BRR1154/SLM-WebDev-v1 (Qwen/Qwen2.5-0.5B-Instruct, 494M params, BF16)
Training Method: Real PyTorch LoRALinear Backpropagation + Wake-Error ABSTAIN Calibration + Empirical Activation-Covariance SVD Null-Space Consolidation ($\Delta W_{\text{proj}} = \Delta W - (\Delta W V_{\text{base}}) V_{\text{base}}^T$)

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Verified Before (v1) vs After (v2) Evaluation Scorecard

All metrics below are directly measured on disjoint held-out test suites (eval_swarm14.py) with real execution/solver verifiers (sqlite3, z3, lxml.html, HTMLParser, subprocess Python, Kahn DAG checker). Full weight telemetry is included in real_training_telemetry.json.

Metric	v1 Baseline (BRR1154/SLM-WebDev-v1)	v2 Checkpoint (BRR1154/SLM-WebDev-v2)	Delta
Domain (HTML5-AST-UI-12)	25.00%	58.33%	+33.33 pts
ARC-Easy-25 Retention (Raw / PMI-Cal)	64.0% / 72.0%	76.0% / 76.0%	+12.0 / +4.0 pts
AA-Omniscience-12 Calibration Index	-25.00	-25.00	+0.00 pts

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Null-Space Weight Surgery Audit

• Targeted Layer Band: 10-23 (q_proj, v_proj, o_proj, down_proj)
• Optimization Schedule: 40 gradient steps (LoRA rank=16, empirical SVD subspace k=24)
• Mutated Projections Verified: 56/56 modules (sha256 verified before vs after merge)
• Max Frobenius Shift Ratio: 1.627%
• Max Null-Space Orthogonality Error: 3.842e-09

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🏆 November 2026 Frontier Benchmark Showdown

Evaluated on official held-out November 2026 frontier benchmark datasets:

Benchmark	Scope	Raw Logit Argmax	Choice-Prior Debiased (PMI)
GPQA-Diamond	50 Stratified PhD Science Questions	20.0% (10/50)	18.0% (9/50)
Humanity's Last Exam (HLE)	51 Multiple-Choice Items	9.8% (5/51)	15.69% (8/51)
MATH-500 (Level 1–3)	Program-Aided Language (PAL Python)	3/8 (37.5%)	—
AIME 2025	Program-Aided Language (PAL Python)	0/5 (0.0%)	—

Quickstart (transformers)

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "BRR1154/SLM-WebDev-v2"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, dtype=torch.bfloat16, device_map="auto")

messages = [{"role": "user", "content": "Solve the specialist task concisely and deterministically."}]
inputs = tokenizer.apply_chat_template(messages, tokenize=True, add_generation_prompt=True, return_tensors="pt").to(model.device)
out = model.generate(inputs, max_new_tokens=160, do_sample=False)
print(tokenizer.decode(out[0][inputs.shape[1]:], skip_special_tokens=True))