Instructions to use BRR1154/SLM-WebDev-v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use BRR1154/SLM-WebDev-v2 with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="BRR1154/SLM-WebDev-v2")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("BRR1154/SLM-WebDev-v2")
model = AutoModelForCausalLM.from_pretrained("BRR1154/SLM-WebDev-v2", device_map="auto")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=256)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use BRR1154/SLM-WebDev-v2 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "BRR1154/SLM-WebDev-v2"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "BRR1154/SLM-WebDev-v2",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/BRR1154/SLM-WebDev-v2

SGLang

How to use BRR1154/SLM-WebDev-v2 with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "BRR1154/SLM-WebDev-v2" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "BRR1154/SLM-WebDev-v2",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "BRR1154/SLM-WebDev-v2" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "BRR1154/SLM-WebDev-v2",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use BRR1154/SLM-WebDev-v2 with Docker Model Runner:
```
docker model run hf.co/BRR1154/SLM-WebDev-v2
```

SLM-WebDev-v2 / README.md

BRR1154

Add verified November 2026 Frontier Benchmark results (GPQA-Diamond, HLE, MATH-500 PAL)

5489f16 verified about 8 hours ago

preview code

Raw History Blame Contribute Delete

3.93 kB

---
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: 25.0
  - 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: 76.0
---
# SLM-WebDev-v2: Accessible HTML5 AST UI Synthesis Specialist (v2)

**Lead Architect:** Bhavanam Rajendra Reddy (*Runtime-Slayers* | ORCID: [0009-0005-3349-1162](https://orcid.org/0009-0005-3349-1162))  
**Base v1 Checkpoint:** [`BRR1154/SLM-WebDev-v1`](https://huggingface.co/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 ($\Delta W_{\text{proj}} = \Delta W - (\Delta W V_{\text{base}}) V_{\text{base}}^T$)
---
## 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 |

---

## 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`

---

---

## 🏆 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`)

```python
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))
```

	---
	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: 25.0
	- 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: 76.0
	---

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

	Lead Architect: Bhavanam Rajendra Reddy (Runtime-Slayers \| ORCID: [0009-0005-3349-1162](https://orcid.org/0009-0005-3349-1162))
	Base v1 Checkpoint: [`BRR1154/SLM-WebDev-v1`](https://huggingface.co/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 ($\Delta W_{\text{proj}} = \Delta W - (\Delta W V_{\text{base}}) V_{\text{base}}^T$)

	---

	## 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 \|

	---

	## 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`

	---

	---

	## 🏆 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`)

	```python
	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))
	```