Results for “xlm-macros”

LoRA: Low-Rank Adaptation of Large Language Models

DreamLIP: Language-Image Pre-training with Long Captions

Executes Python code iteratively via an MCP bridge to produce verified results for calculations, data analysis, and task decomposition.

COCO: Microsoft COCO: Common Objects in Context

Mass spectrometry analysis. Process mzML/MGF/MSP, spectral similarity (cosine, modified cosine), metadata harmonization, compound ID, for metabolomics and MS data processing.

Computes mass-spectral similarity and identifies compounds for metabolomics with matchms — comparing mass spectra, scoring similarity (cosine, modified cosine), and searching spectral libraries to annotate unknowns. Use when matching MS/MS spectra, identifying metabolites, or library searching; for full LC-MS/MS proteomics pipelines use pyopenms. Part of the AlterLab Academic Skills suite.

Train large language models (7B-70B+) with RLHF using PPO, GRPO, DPO, and other algorithms, accelerated by Ray and vLLM for distributed multi-GPU setups.

Expert guidance for fine-tuning LLMs with Axolotl - YAML configs, 100+ models, LoRA/QLoRA, DPO/KTO/ORPO/GRPO, multimodal support

Generate image captions, answer visual questions, and perform image-text retrieval using BLIP-2's Q-Former architecture with frozen vision encoders and LLMs.

Executes Python code iteratively via an MCP bridge to produce verified results for calculations, data analysis, and task decomposition.

Mass spectrometry analysis. Process mzML/MGF/MSP, spectral similarity (cosine, modified cosine), metadata harmonization, compound ID, for metabolomics and MS data processing.

Provides expert guidance for fine-tuning LLMs with Axolotl, covering YAML configs, LoRA/QLoRA, DPO/KTO/ORPO/GRPO, and multimodal support.

Use when masking columns or coordinate ranges in multiple-sequence alignments before downstream HMMER or alignment-processing steps.

Expert guidance for fine-tuning LLMs with Axolotl - YAML configs, 100+ models, LoRA/QLoRA, DPO/KTO/ORPO/GRPO, multimodal support

Process and analyze mass spectrometry data: import spectra from MGF, mzML, MSP, and JSON formats; apply 40+ filters for metadata harmonization and peak cleaning; compute spectral similarities (cosine, modified cosine) for compound identification; build reproducible processing pipelines.

Expert guidance for fine-tuning LLMs with Axolotl - YAML configs, 100+ models, LoRA/QLoRA, DPO/KTO/ORPO/GRPO, multimodal support

Expert guidance for fine-tuning LLMs with Axolotl - YAML configs, 100+ models, LoRA/QLoRA, DPO/KTO/ORPO/GRPO, multimodal support

Expert guidance for fine-tuning LLMs with Axolotl - YAML configs, 100+ models, LoRA/QLoRA, DPO/KTO/ORPO/GRPO, multimodal support

LLaVA-NeXT: Improved Reasoning, OCR, and World Knowledge

DCLM: DataComp for Language Models

Análise de espectrometria de massas. Processa mzML/MGF/MSP, similaridade espectral (cosine, modified cosine), harmonização de metadados, identificação de compostos, para metabolômica e processamento de dados MS.

Mass spectrometry analysis. Process mzML/MGF/MSP, spectral similarity (cosine, modified cosine), metadata harmonization, compound ID, for metabolomics and MS data processing.

High-performance RLHF framework with Ray+vLLM acceleration. Use for PPO, GRPO, RLOO, DPO training of large models (7B-70B+). Built on Ray, vLLM, ZeRO-3. 2× faster than DeepSpeedChat with distributed architecture and GPU resource sharing.

Expert guidance for fine-tuning LLMs with LLaMA-Factory - WebUI no-code, 100+ models, 2/3/4/5/6/8-bit QLoRA, multimodal support