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Hugging Face
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Train AI models with Unsloth and Hugging Face Jobs for FREE
The post presents a workflow for fine-tuning the LiquidAI/LFM2.5-1.2B-Instruct small language model with Unsloth on Hugging Face Jobs, including optional use of Claude Code, Codex, or another coding agent. It explains that Unsloth is described as providing about 2x faster training and 60% lower VRAM use than standard methods, while the model runs under 1GB of memory and is optimized for on-device deployment. The tutorial covers claiming free Hugging Face Jobs credits through the Unsloth Jobs Explorers organization, installing the hf CLI or a model-training skill, and submitting a job with a dataset, GPU flavor, timeout, and output repository. An agent can generate a UV training script using 4-bit loading and LoRA, submit the managed cloud-GPU job, report Trackio monitoring details, and push the trained model to the Hugging Face Hub.
IBM and UC Berkeley Diagnose Why Enterprise Agents Fail Using IT-Bench and MAST
IBM Research and UC Berkeley analyze how agentic LLM systems fail in enterprise IT automation, including incident triage, logs and metrics queries, and Kubernetes actions across long-horizon tool loops. To move beyond benchmark success rates, the analysis applies MAST (Multi-Agent System Failure Taxonomy) to execution traces, converting failures into structured signatures across system design, inter-agent misalignment, and task verification. It covers 310 ITBench SRE traces from Gemini-3-Flash, Kimi-K2, and GPT-OSS-120B, whose mean recalls were 75.5%, 28.6%, and 12.4%, respectively. Gemini averaged 2.6 failure modes per failed trace, compared with 4.7 for Kimi and 5.3 for GPT-OSS-120B, while FM-3.3, Incorrect Verification, was identified as the strongest predictor across models; the analysis recommends external verification, explicit termination controls, and clarification branches.
One-Shot Any Web App with Gradio's gr.HTML
The post presents Gradio 6’s gr.HTML as a way to build interactive web components in a single Python file, combining custom HTML templates, scoped CSS, and JavaScript interactivity without a separate build step. It describes applications including a Pomodoro timer, contribution heatmap, Kanban board, spinning wheel, and machine-learning viewers for detection, 3D camera control, and speech transcription. Within the component API, ${value} injects Python state into the template, props.value updates that state from JavaScript, and trigger('change') synchronizes it back to Python; CSS keyframes and native HTML5 drag events supply richer behavior. For reuse, a gr.HTML subclass packages the templates and script so the result can be used like built-in Gradio components, while reload mode and Spaces deployment support rapid iteration from generated code to a live app.
Custom Kernels for All from Codex and Claude
Writing CUDA kernels that integrate with diffusers and transformers involves GPU-specific optimization, library integration, PyTorch registration, and environment compatibility. It presents a cuda-kernels agent skill that packages architecture guides, integration patterns, kernel templates, troubleshooting material, benchmarks, and Kernel Hub examples for coding agents. Claude and Codex used it to generate RMSNorm, RoPE 3D, GEGLU, and AdaLN kernels for LTX-Video in diffusers, and an RMSNorm kernel for Qwen3-8B in transformers, including CUDA source, PyTorch bindings, build configuration, and benchmark scripts. On H100 80GB HBM3, the LTX-Video RMSNorm tests averaged 1.88x speedup and the optimized pipeline with torch.compile reached 1.43x the listed baseline, while Qwen3-8B RMSNorm averaged 1.94x and reached 2.47x at 8192 tokens. The skill supports packaging kernels for precompiled distribution through the Hub, where get_kernel selects a matching binary across Python, PyTorch, and CUDA variants.
OpenEnv in Practice: Evaluating Tool-Using Agents in Real-World Environments
OpenEnv is an open-source framework from Meta and Hugging Face for evaluating AI agents against real systems rather than simulations, with stateful environments that connect to tools and APIs. Turing's production-grade Calendar Gym applies this model to calendar operations, combining access control lists, partial visibility, temporal reasoning, and multi-step workflows in isolated sessions. Agents often handled individual actions but became less reliable on longer or ambiguous tasks: success was close to 90% with explicit calendar identifiers and roughly 40% when requests used natural-language descriptions. More than half of failures involved malformed arguments or incorrect action ordering despite selecting the correct tool, while structured validation, permission, and datetime errors provided concrete recovery signals.
Transformers.js v4: Now Available on NPM!
Transformers.js v4 is now available on NPM, marking a major release after a year of development. It introduces a C++-rewritten WebGPU runtime tested across about 200 supported model architectures, enabling the same JavaScript code to run WebGPU-accelerated models in browsers, Node, Bun, Deno, and desktop applications. Specialized ONNX Runtime Contrib Operators support new export strategies for large language models, and the post reports a roughly fourfold speedup for BERT-based embedding models using MultiHeadAttention. The release also restructures the repository as a pnpm-workspaces monorepo, modularizes model definitions, moves examples to a separate repository, and switches from Webpack to esbuild, reducing build times from two seconds to 200 milliseconds and shrinking transformers.web.js by 53%. New APIs cover model asset inspection, caching, environment fetch controls, logging, and standalone tokenization, while added architectures include Mamba, MLA, and MoE models compatible with WebGPU.
Introducing SyGra Studio
SyGra 2.0.0 introduces Studio, an interactive environment for building, previewing, executing, and debugging synthetic data generation flows on a visual canvas. Users can configure connectors and models, preview source rows, reference automatically created state variables in prompts, define structured outputs, and inspect the generated YAML or JSON graph configuration. Studio streams node-level execution status, token usage, latency, cost, logs, and guardrail outcomes, while storing run history in .executions/runs/*.json and autosaving drafts. It also runs existing workflows, including a Glaive Code Assistant example with conditional revision loops, tunable dataset and batching inputs, and observable intermediate critiques; generated outputs can support training, evaluation, or annotation workflows.
Community Evals: Because we're done trusting black-box leaderboards over the community
Hugging Face is introducing Community Evals, a system for decentralized and transparent reporting of model evaluation results on the Hub. It responds to saturated benchmarks and inconsistent scores across model cards, papers, and evaluation platforms, while acknowledging that benchmark performance does not necessarily reflect real-world capabilities. Benchmark dataset repositories can register as benchmarks, define reproducible eval specifications in eval.yaml, aggregate scores from model repositories and community submissions, and display leaderboards in dataset cards. Model results are stored in .eval_results/*.yaml, while users can submit sourced scores through pull requests that remain visible as community results, with Git history and APIs supporting tracking and further aggregation. The beta feature aims to expose what was evaluated, how, when, and by whom, but it does not claim to solve benchmark saturation, test-set training, or the benchmark-reality gap.
The Future of the Global Open-Source AI Ecosystem: From DeepSeek to AI+
This third and final article in a series examines how Chinese AI organizations are shaping an open-source ecosystem after DeepSeek R1's January 2025 release. It argues that open sharing of models, papers, deployment infrastructure, and techniques has become the dominant near-term approach, with organizations ranging from Alibaba, Tencent, ByteDance, and Baidu to startups and application companies contributing at different layers. Qwen illustrates the ecosystem strategy: by mid-2025, more than 113,000 Hugging Face models used Qwen as a base, while organizations also connected models with chips, cloud platforms, toolchains, evaluation, and deployment systems. The article places this shift on infrastructure built since 2017, including the East Data, West Compute plan, and says R1 catalyzed wider use of existing compute, energy, and data capacity. It concludes that open-source models are becoming reusable, composable components in industrial workflows and localized AI systems as China moves from DeepSeek toward AI+.
Training Design for Text-to-Image Models: Lessons from Ablations
The second part of a series on training efficient text-to-image models from scratch documents training-design ablations for the PRX model. The authors establish a Flow Matching baseline (PRX-1.2B, 100K steps, 256×256, AdamW, GemmaT5, RoPE, 1M MidJourneyV6 synthetic images) and benchmark interventions against FID, CMMD, DINO-MMD, and throughput across four buckets: Representation Alignment, Training Objectives, Token Routing and Sparsification, and Data. REPA representation alignment boosts early convergence when used as a burn-in, while the Muon optimizer lowered FID from 18.20 to 15.55 and improved CMMD and DINO-MMD. Storing denoiser weights in BF16 instead of using BF16 autocast raised FID to 21.87, and token routing methods such as TREAD/SPRINT were minor at 256² but became major wins at higher resolutions. Long captions proved critical, synthetic versus real data shifted texture versus structure, and upcoming work releases the full training code alongside a 24-hour end-to-end speedrun.
Introducing Daggr: Chain apps programmatically, inspect visually
Building AI applications that connect multiple models and APIs often results in fragile scripts or requires complex production orchestration tools. To address rapid experimentation needs, Daggr introduces an open-source Python library created by the Gradio team for chaining AI workflows programmatically. Developers define workflows in Python code using components like GradioNode, FnNode, and InferenceNode, which automatically generate an interactive visual canvas served on port 7860. The interface allows users to inspect intermediate outputs, modify inputs, rerun individual pipeline steps without complete re-execution, and maintain persistent state across workspaces called sheets. Currently in beta, Daggr supports local execution with automatic fallback to remote APIs and integrates directly with Hugging Face Spaces.
We Got Claude to Build CUDA Kernels and teach open models!
Domain-specific coding tasks like writing optimized CUDA kernels often require capable, expensive frontier models, leading to high operational costs. The upskill tool addresses this challenge by capturing specialized workflows from teacher models such as Claude Opus 4.5 into portable skill files that can be consumed by smaller or locally hosted models. The framework automatically generates input-output test cases from execution traces to benchmark student model accuracy and token efficiency with and without the skill. In an evaluation targeting HuggingFace kernel-builder development, a local GLM-4.7 Flash model improved its pass rate from 40% to 85% when supplied with the generated skill. Measuring token usage across different models remains essential, as skills can lower token consumption for some architectures while introducing unnecessary overhead for others.
Architectural Choices in China's Open-Source AI Ecosystem: Building Beyond DeepSeek
Chinese open-source artificial intelligence development has shifted focus toward architectural choices, ecosystem toolchains, and domestic hardware integration following the January 2025 DeepSeek release. Leading groups have adopted Mixture-of-Experts architectures like Kimi K2 and Qwen3 to balance capability, dynamic compute allocation, and operating costs. Development expanded beyond text into multimodal domains including speech, video, and 3D systems while prioritizing smaller 0.5B to 30B distilled models under permissive Apache 2.0 licensing. Furthermore, model releases increasingly provide day-zero alignment with domestic inference and training infrastructure, including Huawei Ascend, Cambricon, and Baidu Kunlun chips. Production serving engines such as Moonshot AI's Mooncake and Baidu's FastDeploy 2.0 reflect a transition from standalone models to comprehensive system-level design.
Alyah ⭐️: Toward Robust Evaluation of Emirati Dialect Capabilities in Arabic LLMs
Standard Arabic language model evaluations predominantly assess Modern Standard Arabic while overlooking regional spoken dialects that feature distinct syntax, vocabulary, and cultural grounding. To address this evaluation gap, researchers created Alyah, a benchmark comprising 1,173 manually curated multiple-choice questions collected from native Emirati speakers across seven cultural and linguistic categories. Distractor answer choices were synthetically generated using language models and manually reviewed for semantic plausibility. Evaluating base and instruction-tuned Arabic-native, adapted, and multilingual models revealed that instruction-tuned systems consistently outperformed base variants, with falcon-h1-arabic-7b-instruct achieving the top instruction accuracy at 82.18%. However, questions involving implicit meanings, rare expressions, and specific dialect phenomena remained difficult across all tested model families and sizes.
Unlocking Agentic RL Training for GPT-OSS: A Practical Retrospective
Engineers evaluated the GPT-OSS model for agentic reinforcement learning across multi-step tasks such as ReTool and GSM8K using the verl framework. Initial training runs suffered from exploding gradient norms, increasing KL divergence, and stagnating rewards. Investigation revealed that non-deterministic expert routing in the Mixture of Experts architecture caused log-probability mismatches between forward passes, which falsely triggered Proximal Policy Optimization clipping. The team resolved this by substituting detached current log-probabilities for old log-probabilities during on-policy updates. To address further training instabilities and memory bottlenecks, the implementation integrated an attention-sink-aware backward pass into FlashAttention v3 and applied sequence parallelism using all-to-all communication around attention layers.
AssetOpsBench: Bridging the Gap Between AI Agent Benchmarks and Industrial Reality
AssetOpsBench is an evaluation framework designed to benchmark agentic AI systems within industrial Asset Lifecycle Management operations, such as chillers and air handling units. The benchmark incorporates 2.3 million sensor telemetry points, 4,200 work orders, 53 structured failure modes, and over 140 curated scenarios spanning four agent roles. Systems are assessed across six qualitative criteria, including task completion, retrieval accuracy, result verification, sequence correctness, clarity and justification, and hallucination rate. Evaluation traces are analyzed using a trajectory-level diagnostic pipeline called TrajFM, which extracts and clusters failure patterns. In community tests covering over 300 agents, no evaluated model family reached the 85-point readiness threshold.
One Year Since the “DeepSeek Moment”
The January 2025 release of DeepSeek's R1 model transformed China's artificial intelligence sector from a closed ecosystem into an organic open-source ecosystem. By releasing under the MIT license and sharing reasoning paths and post-training methods, DeepSeek turned complex reasoning into a reusable, downloadable engineering asset suitable for distillation in compute-constrained environments. Chinese technology organizations subsequently shifted from comparing standalone benchmark scores to developing ecosystem interfaces, application scenarios, and system-level infrastructure. Major players rapidly expanded their open footprints, driving downloads of newly created Chinese models on Hugging Face past those of any other nation. This momentum catalyzed global open-source adoption across international markets while prompting Western organizations to develop competing open-weight alternatives or build directly upon open Chinese foundations.
Differential Transformer V2
Differential Transformer V2 introduces an architectural refinement to differential attention that pairs and subtracts query heads within the same grouped-query attention group. The architecture doubles query heads while preserving key-value head counts, which avoids increasing key-value cache demands and removes the need for custom kernels. DIFF V2 replaces the per-head RMSNorm of its predecessor with a per-token, per-head projected lambda passed through a sigmoid function. This formulation expands the context root-mean-square range to between zero and root two, preventing massive gradient magnifications, mitigating attention sinks, and stabilizing large learning rate pretraining. Ablations confirm that sharing keys and values across subtracted heads and constraining lambda scaling are necessary to prevent training instability and elevated loss.
Introducing Waypoint-1: Real-time interactive video diffusion from Overworld
Overworld introduced Waypoint-1, a real-time interactive video diffusion model controllable through text prompts, mouse movements, and keyboard inputs. The architecture uses a frame-causal rectified flow transformer trained on compressed latent representations across 10,000 hours of video game footage paired with control signals. Pre-training uses diffusion forcing with causal attention, while post-training incorporates self forcing via DMD to mitigate autoregressive rollout error accumulation, enable one-pass classifier-free guidance, and support few-step denoising. To achieve low latency on consumer hardware, Overworld developed WorldEngine, a pure Python streaming inference library. Utilizing AdaLN feature caching, static rolling key-value caching with Flex Attention, fused matrix multiplications, and PyTorch compilation, the 2.3B parameter Waypoint-1-Small model reaches up to 60 frames per second on an RTX 5090 GPU.
Open Responses: What you need to know
Open Responses is a new open inference standard initiated by OpenAI, developed by the open source AI community, and backed by Hugging Face. While agentic workloads require multi-step planning and tool execution, the ecosystem has remained constrained by the legacy Chat Completion format and proprietary Responses implementations. Open Responses addresses this mismatch by standardizing semantic event streaming, provider-specific configuration options, and distinct roles for model providers and intermediary routers. The specification formalizes reasoning visibility across raw traces, protected encrypted content, and sanitized summaries while supporting provider-managed sub-agent execution loops. Developers can now orchestrate multi-step tool calls and stream reasoning deltas across heterogeneous providers through a single consistent API schema.
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