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AI tools that help ML engineers find research papers, scan model dependencies for CVEs, benchmark architectures, and document ML pipelines.
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MethodPaperVram
QLoRADettmers et al. 202312 GB
LoRAHu et al. 202216 GB
DoRALiu et al. 202414 GB
AdaLoRAZhang et al. 202314 GB
4 of 11 results · sorted by VRAM efficiency
ML research paper discovery
Search millions of ML papers by method, architecture, benchmark, or dataset name. Find the current state-of-the-art on any task — image classification, NLP, recommendation — and compare benchmark scores before choosing which approach to implement.
Find 2024–2025 papers on vision-language models for medical image analysis. I need methods that work on X-ray classification with limited labeled data (<500 examples).
Found 11 papers. Top methods: (1) LLaVA-Med — visual instruction tuning on medical image-text pairs, 89.4% accuracy on CheXpert with 400 labels. (2) BioViL-T — temporal contrastive learning, achieves 87.1% with 300 chest X-rays. (3) MedCLIP — CLIP fine-tuning on MIMIC-CXR. All three include open-source code. LLaVA-Med best for < 500 labeled examples.
ToolRouter search_papers
MethodAccuracyLabels used
LLaVA-Med89.4% (CheXpert)400
BioViL-T87.1% (chest X-ray)300
MedCLIP85.2% (MIMIC-CXR)500
3 of 11 papers · best for <500 labeled examples
ML library CVE scanning
Scan your ML stack — PyTorch, Transformers, ONNX Runtime, scikit-learn, NumPy — for CVEs before deploying inference servers. ML libraries are frequent targets for deserialization and pickle loading vulnerabilities.
Check these ML packages for CVEs: torch@2.2.1, transformers@4.40.0, onnxruntime@1.17.3, numpy@1.26.4, scikit-learn@1.4.2.
numpy@1.26.4: CVE-2023-52425 (CVSS 7.5) — integer overflow in array indexing. Upgrade to 1.26.5. Others: clean. torch, transformers, onnxruntime, scikit-learn all pass. numpy is commonly in transitive deps — check all packages that depend on it.
ToolRouter search_cves
torch@2.2.1
Clean — no CVEs
transformers@4.40.0
Clean — no CVEs
onnxruntime@1.17.3
Clean — no CVEs
numpy@1.26.4
CVE-2023-52425 · CVSS 7.5 — upgrade to 1.26.5
scikit-learn@1.4.2
Clean — no CVEs
Framework and model documentation lookup
Fetch version-specific docs for PyTorch, HuggingFace Transformers, JAX, and ONNX. Get exact API signatures for training loops, optimizers, and inference without hunting through outdated tutorials.
Fetch PyTorch 2.2 docs for torch.compile: what backends are available, how to handle graph breaks with fullgraph mode, and how to profile with torch.profiler to find the slowest kernels.
torch.compile backends: "inductor" (default, CUDA/CPU), "cudagraphs" (static shapes), "onnxrt" (ONNX export). Fullgraph=True raises error on graph breaks instead of falling back. Profiling: use torch.profiler.profile(activities=[...]) as context manager, then export Chrome trace. Key metrics: CUDA kernel time vs CPU overhead.
ToolRouter fetch_docs
Backends
inductor (default, CUDA/CPU), cudagraphs (static shapes), onnxrt
fullgraph=True
Raises error on graph breaks — no silent fallback
Profiling Setup
torch.profiler.profile(activities=[...]) → export Chrome trace
Key Metrics
CUDA kernel time vs CPU overhead via profiler
ML pipeline architecture diagramming
Generate training pipeline, model serving, and MLOps architecture diagrams for design docs and onboarding. Describe the system and get Mermaid output ready to embed in GitHub or Confluence.
Create an ML pipeline diagram: raw data in S3 → feature engineering (PySpark) → feature store (Feast) → model training (PyTorch on SageMaker) → MLflow experiment tracking → model registry → FastAPI inference service → monitoring with Evidently.
Generated Mermaid flowchart with 8 stages. Data lineage arrows show S3 → PySpark → Feast Feature Store branching to both training and inference paths. MLflow experiment tracking shown as a parallel audit path from training. Evidently monitoring connected to inference endpoint with drift detection annotation.
Model benchmark and comparison charts
Generate accuracy vs latency, training loss curve, and confusion matrix-style charts from model evaluation results. Turn raw benchmark numbers into presentation-ready charts for model review meetings.
Create a scatter chart: model accuracy (y-axis) vs p50 inference latency in ms (x-axis) for: GPT-3.5 (88.2% acc, 320ms), Llama-3-8B (82.1%, 45ms), Mistral-7B (80.4%, 38ms), Phi-3-mini (76.3%, 22ms). Label each point.
Generated scatter chart with labeled data points. Clear Pareto frontier: Mistral-7B and Llama-3-8B offer best accuracy-per-latency tradeoff. Phi-3-mini best for real-time latency requirements. GPT-3.5 in upper-right quadrant (high accuracy, high latency) — suitable only where latency is not a constraint.
ToolRouter create_chart
Accuracy % vs Latency ms
Research on model training techniques
Deep-dive research on specific training techniques — RLHF, DPO, LoRA, quantization — with citations from the latest papers. Get synthesized comparisons of competing methods before deciding which to implement.
Compare LoRA, QLoRA, and full fine-tuning for adapting Llama-3-8B on a domain-specific classification task with 10K training examples and an A100 GPU budget of 40GB VRAM.
QLoRA: 4-bit quantization + LoRA adapters, fits Llama-3-8B in 12GB VRAM, achieves ~95% of full fine-tune accuracy on classification tasks. LoRA without quantization: 16GB VRAM, marginal accuracy gain over QLoRA. Full fine-tune: 60GB+ VRAM — exceeds A100 40GB. Recommendation: QLoRA with rank=16, alpha=32, target_modules all attention layers.
ToolRouter research
| QLoRA | LoRA (fp16) | Full Fine-Tune |
|---|
Ready-to-use prompts
Find LoRA fine-tuning papers
Search for 2023–2025 papers on parameter-efficient fine-tuning methods: LoRA, QLoRA, DoRA, and AdaLoRA. Compare the parameter counts, VRAM requirements, and accuracy vs full fine-tuning benchmarks.
Scan ML stack for CVEs
Check for CVEs in: torch@2.2.1, transformers@4.40.0, onnxruntime@1.17.3, numpy@1.26.4, scikit-learn@1.4.2, accelerate@0.30.0. Flag anything CVSS 7+ immediately.
PyTorch compile docs
Fetch PyTorch 2.2 documentation on torch.compile: available backends (inductor, cudagraphs, onnxrt), how to handle graph breaks, and how to profile kernels with torch.profiler to find compilation bottlenecks.
ML pipeline architecture diagram
Generate a Mermaid diagram for an ML training pipeline: S3 raw data → feature engineering (PySpark) → feature store (Feast) → PyTorch training on SageMaker → MLflow tracking → model registry → FastAPI serving → Evidently drift monitoring.
Model latency vs accuracy chart
Create a scatter plot: inference latency (ms, x-axis) vs benchmark accuracy % (y-axis) for: GPT-4 (92%, 1200ms), GPT-3.5 (88%, 320ms), Claude Haiku (85%, 180ms), Llama-3-8B (82%, 45ms), Mistral-7B (80%, 38ms), Phi-3-mini (76%, 22ms).
QLoRA vs full fine-tuning
Compare QLoRA, LoRA, and full fine-tuning for adapting Llama-3-8B on a domain-specific NER task with 5K training examples. Include VRAM requirements, training time on A100, and expected accuracy trade-offs.
HuggingFace Trainer API docs
Fetch HuggingFace Transformers v4.40 docs for the Trainer API: how to configure TrainingArguments for gradient checkpointing, mixed precision (fp16/bf16), and gradient accumulation for large batch training on limited VRAM.
Vision transformer papers
Find papers from 2023–2025 comparing Vision Transformers (ViT) vs CNNs for industrial defect detection. I need methods that work on small datasets (<1000 labeled images) with high recall requirements (>95%).
Tools to power your best work
Academic ResearchSearch papers, authors, citations
Generate ChartTurn data into chart images fast
Deep ResearchAI research reports with citations
Library DocsUp-to-date docs and code examples for any library
Diagram GeneratorRender Mermaid, PlantUML & more
Economic DataCensus, FRED, and labor stats
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Architecture selection and research spike
Before implementing a new model architecture, find the relevant papers, compare approaches, and validate the dependency stack.
Model deployment preparation
Before deploying a model to production, audit the inference stack, generate architecture docs, and verify performance benchmarks.
Training pipeline documentation
Document a training pipeline end-to-end with architecture diagrams and framework references for onboarding and reproducibility.
Frequently Asked Questions
How many ML papers does Academic Research cover?
Academic Research covers hundreds of millions of papers across Semantic Scholar, arXiv, and other databases. For ML research, arXiv is the primary source — it includes essentially all published deep learning and NLP papers. Results include citation counts, author lists, and links to full PDFs.
Can Vulnerability Database catch CVEs in PyTorch and CUDA libraries?
The Vulnerability Database searches the full CVE catalog by package name and version. It covers PyPI packages like torch, transformers, and onnxruntime. For CUDA system libraries, search by the NVIDIA package name and version.
Can Library Docs fetch documentation for specific HuggingFace model cards?
Library Docs fetches documentation from official sources — it covers the HuggingFace Transformers library documentation, Trainer API, and framework guides. For specific model cards, the web-search tool is more effective for finding model-specific fine-tuning instructions.
Does Deep Research provide concrete code examples for ML techniques like QLoRA?
Yes. Deep Research synthesizes official documentation, GitHub READMEs, and engineering blog posts into structured comparisons that include configuration examples and YAML snippets. For QLoRA specifically, you will get BitsAndBytesConfig parameters and LoraConfig settings.
Can Diagram Generator produce MLOps workflow diagrams with different arrow styles for data vs control flow?
Yes. In your prompt, specify which connections are data flows vs control flows and Diagram Generator will use dashed vs solid arrows (or different line styles) to distinguish them in Mermaid or PlantUML output.
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