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: 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 ($\Delta W_{\text{proj}} = \Delta W - (\Delta W V_{\text{base}}) V_{\text{base}}^T$)

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`)

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))

Downloads last month: -

Safetensors

Model size

0.5B params

Tensor type

BF16

Model tree for BRR1154/SLM-WebDev-v2

Base model

Qwen/Qwen2.5-0.5B

Finetuned

Qwen/Qwen2.5-0.5B-Instruct

Finetuned

BRR1154/SLM-WebDev-v1

Finetuned

(1)

this model

Spaces using BRR1154/SLM-WebDev-v2 2

Evaluation results

Domain Execution / Solver Accuracy (After v2) on HTML5-AST-UI-12
self-reported

58.330
Domain Execution / Solver Accuracy (Before v1 Baseline) on HTML5-AST-UI-12
self-reported

25.000
ARC-Easy-25 Raw / Calibrated (%) on ARC-Easy-25 General Retention Guard
self-reported

76.000

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

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

Null-Space Weight Surgery Audit

🏆 November 2026 Frontier Benchmark Showdown

Quickstart (transformers)

Model tree for BRR1154/SLM-WebDev-v2

Spaces using BRR1154/SLM-WebDev-v2 2

Evaluation results

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

Quickstart (`transformers`)