In this tutorial, we build a stable workflow around the Fable 5 Traces dataset from Hugging Face. We avoid fragile dependencies and manually parse the merged JSONL file to keep Colab reliable. We inspect repository files, normalize tool calls, audit structure, redact secrets, and visualize key distributions. We also export safe no-CoT chat datasets and train pure-Python Naive Bayes baselines on the traces.
The post Building a Stable Fable 5 Traces Workflow in Colab: Parsing Tool Calls, Auditing Data, and Training Baselines appeared first on MarkTechPost.
We build a Colab-ready PyGraphistry workflow for interactive graph analytics on enterprise access data. We generate a synthetic dataset of users, devices, IPs, services, roles, and geos, then convert it into nodes and edges. We enrich the graph with risk scores, centrality metrics, community detection, Isolation Forest anomaly scores, and UMAP layout embeddings. We then bind the graph in PyGraphistry and produce local PyVis visualizations for full, ego, and high-risk views.
The post PyGraphistry Implementation Workflow for Interactive Graph Intelligence Pipelines in Security Analytics and Risk Investigation appeared first on MarkTechPost.
In this tutorial, we work with NVIDIA's Open-SWE-Traces dataset to study agentic software-engineering trajectories for fine-tuning. We stream the data directly from Hugging Face, so we can process it efficiently in Google Colab without downloading everything locally. We normalize multi-turn agent conversations, parse final code patches, and build an analysis DataFrame covering trajectory length, tool usage, patch size, language distribution, and resolution outcomes. We then curate a supervised fine-tuning subset using success labels, token limits, language filters, and patch availability.
The post Building Supervised Fine-Tuning Data from NVIDIA Open-SWE-Traces: Trajectory Parsing, Patch Analysis, Token Budgets, and Tool-Use Metrics appeared first on MarkTechPost.
NEW YORK, June 26, 2026 — Qualcomm Technologies, Inc. has announced the expansion of its strategic relationship with Hugging Face to advance open, developer-driven artificial intelligence (AI) from devices to cloud […]
The post Qualcomm and Hugging Face Expand Relationship to Advance Open, Developer-Driven AI from Device to Cloud appeared first on AIwire.
The expanded partnership could significantly boost Qualcomm's hardware demand by enabling seamless AI model deployment across diverse environments.
The post Qualcomm expands strategic relationship with Hugging Face to onboard AI models across edge and cloud appeared first on Crypto Briefing.
We implement an end-to-end workflow for Salesforce CodeGen, loaded from Hugging Face. We move past basic inference by adding function extraction, syntax checking, static safety checks, and unit-test validation. We rerank best-of-N candidates, compose multi-turn program synthesis, and experiment with prompt styles. We finish by visualizing a mini benchmark and exporting the generated artifacts as reusable files.
The post Salesforce CodeGen Tutorial: Generate, Validate, and Rerank Python Functions With Unit Tests and Safety Checks appeared first on MarkTechPost.
GLM-5.2's expansive context window could revolutionize coding workflows by enabling seamless processing of extensive codebases, enhancing efficiency.
The post Z.AI releases GLM-5.2 with 1M context window on Hugging Face appeared first on Crypto Briefing.
In this tutorial, we build a workflow that uses Docling Parse to analyze PDF documents at a detailed structural level. We prepare a stable Python environment, handle common Colab dependency issues, and generate a custom multi-page PDF with text, columns, table-like content, vector shapes, and an embedded image. We then extract words, characters, and lines with page-level coordinates, render visual overlays, and save results into structured JSON and CSV. We see how low-level parsing supports layout analysis, reading-order reconstruction, and retrieval-ready document preparation.
The post How to Build a Parsing Pipeline with Docling Parse for Layout-Aware Document Intelligence appeared first on MarkTechPost.
In this tutorial, we implement a QwenPaw workflow that provides a practical environment for building and testing an agent-powered assistant. We install and initialize QwenPaw, configure its working directory, set up authentication, connect optional model providers via Colab secrets, and create a structured workspace with custom skills and local knowledge files. We also launch the […]
The post How to Build a QwenPaw Agent Workspace with Custom Skills, Model Providers, Console Access, and Streaming API Testing appeared first on MarkTechPost.
