Articles tagged "Apache 2.0"

Whistle is Cactus's 16.9MB open speech-to-text model for CPU, Apache 2.0. It supports 7 languages but not Japanese. Speed vs Whisper base, setup, best uses.

Reflection Beam (501B MoE, 23B active) needs an estimated ~501GB at FP8, ~250-290GB at 4-bit. GPU, Mac and CPU-offload fits as of October 2026; weights not out.

Kolibri-1 needs about 78GB in FP8, running on 1x H200 or 2x H100. Aleph Alpha's 78B/3.46B-active Apache 2.0 MoE: VRAM by precision, vLLM launch, German focus. Oct 2026.

Index-Translate needs about 2GB VRAM (2B), 6GB (9B) or 21GB (35B-A3B) at 4-bit. bilibili's 150-language models: GPU, Mac, CPU specs and setup. Oct 2026.

Cloudflare Clef and Clef-flash: open-weight decision models with a Jev-compatible API. Apache 2.0, 41GB minimum VRAM, $0.24/M. Compared with AWS Strands Decider 2B and Jev.

Claude for Financial Services is Anthropic's official, free reference repository of 10 named workflow agents, 9 vertical skill plugins, and 12 MCP data connectors for investment banking, equity research, private equity, and wealth management. Updated Sep 2026.

CLI-Anything is an Apache 2.0 open-source framework that automatically converts any software — GIMP, Blender, LibreOffice, and more — into an agent-native CLI through a 7-phase pipeline: analyze, design, implement, plan tests, write tests, document, publish. Updated Sep 2026.

Reladraw is a diagram language that places nodes using relative words like right of and below, between Mermaid automatic layout and draw.io fixed coordinates.

Ollaya is an open-source local runtime for decision models: state and typed questions in, calibrated probabilities out in ms, via Ollama-style pull/run/serve.

Ternary Bonsai 2 27B (PrismML, Sep 2026): Qwen3.8-27B at 1.76-bit weights, 5.9GB, 16GB RAM or a 24GB GPU, 98.2% of FP16 score. Apache 2.0, updated Sep 2026.

Open Code Review is Alibaba's open-source AI code review CLI, combining a deterministic pipeline with an LLM agent after two years of internal use. This guide covers installation, configuration, CI integration, and how it compares to CodeRabbit and other tools.

Bend 2 unifies dependent types, checked proofs, affine ownership, and GPU execution in one Apache 2.0 language, with install steps, code, and Rust comparison.

MCPJam Inspector is an Apache 2.0 OSS platform to test, debug and eval MCP servers, via hosted app, npx, desktop, or Docker, vs. the official MCP Inspector.

Hy4 preview needs about 385GB VRAM at 4-bit and 1.54TB at BF16. Tencent open-weighted this 770B MoE with 1M context in Aug 2026. VRAM tables, GPUs, API cost.

Needle 2 is Cactus Compute's 45M-parameter on-device agent LLM, shipped as a single 14MB binary for tool calling, device use, and structured extraction. It runs from Raspberry Pi 5 down to budget phones. Here's how it works and how to install it (Updated August 2026).

A primary-source deep dive on **DiffusionGemma** (`google/diffusiongemma-26B-A4B-it`, 25.2B total / 3.8B active MoE), released June 10, 2026 by Google DeepMind in coordination with NVIDIA. Grounded in the [official Google blog](https://blog.google/innovation-and-ai/technology/developers-tools/diffusion-gemma-faster-text-generation/), [ai.google.dev model card](https://ai.google.dev/gemma/docs/diffusiongemma/model_card), [Hugging Face card](https://huggingface.co/google/diffusiongemma-26B-A4B-it), and [NVIDIA's blog](https://blogs.nvidia.com/blog/rtx-ai-garage-local-gemma-diffusion/). Where autoregressive (AR) models generate one token at a time left-to-right, diffusion language models (DLMs) **denoise a 256-token canvas in parallel into final text**. 15-20 tokens commit per forward pass, up to 48 denoising steps, 1,000+ tok/sec on H100, 700+ on RTX 5090, ~3.5–4× the throughput of the AR Gemma 4 counterpart. Crucially, Google **openly states that quality lags AR**: MMLU Pro 77.6 vs 82.6, GPQA 73.2 vs 82.3, MMMU Pro 54.3 vs 73.8. Apache 2.0, distributed via Hugging Face / Vertex AI / NVIDIA NIM — the first large-scale open-weight diffusion LLM in the industry. The column covers practical implications for Japanese enterprises (on-prem internal agents, code editing, low-latency workflows) and positioning against Mercury (Inception Labs), LLaDA, and Gemini Diffusion.

A deep dive into Gemma 4 12B, released by Google DeepMind on June 3, 2026, grounded in the [official announcement](https://blog.google/innovation-and-ai/technology/developers-tools/introducing-gemma-4-12b/) and [Developer Guide](https://developers.googleblog.com/gemma-4-12b-the-developer-guide/). The standout property is **encoder-free multimodal architecture** — replacing the prior vision encoder (~550M parameters) with a 35M-parameter lightweight embedder plus a single matrix multiplication, and removing the 12-layer Conformer audio encoder entirely by projecting raw audio straight into the LLM's embedding space. Runs on a 16GB VRAM laptop (Copilot+ PC or Apple Silicon Mac), shipped under Apache 2.0, available through Hugging Face / Ollama / LM Studio / MLX / Vertex AI on day one. Covers the architectural rationale, the "approaches 26B MoE at less than half the memory" benchmark claim, positioning within the Gemma 4 family (E2B / E4B / 26B / 31B), competitive comparison against Llama 4 / Qwen 3.5 / Phi-5, and the fit with Japanese enterprise on-prem AI, voice workflows, and data-sovereignty requirements.

Google I/O 2026 put a fresh spotlight on Gemma 4 (2B–31B, 256K context, 140 languages, Apache 2.0) and a major Google AI Studio overhaul featuring Kotlin vibe coding, one-click Cloud Run deployment, and the Managed Agents API. This column covers the full picture — hardware requirements, competitive positioning against Llama 4 and Qwen, and practical adoption guidance for Japanese enterprises.

Qwen 3.6-27B Dense from Alibaba's Qwen Team, released April 22, 2026: 77.2 on SWE-bench Verified, 59.3 on Terminal-Bench 2.0 (matching Claude 4.5 Opus), 262K-to-1M context, Apache 2.0 license, and 40 tok/s on an RTX 3090 with Q4_K_M — summarized from official sources.

Mistral Small 4, released March 2026, unifies reasoning, multimodal vision, and agentic coding in a 119B MoE model under Apache 2.0. Supports 11 languages including Japanese. Full specs, setup guide, and model comparisons.

A comprehensive analysis of Rakuten AI 3.0's Mixture of Experts architecture with 700B parameters. Explore the 8-expert configuration, 40B active parameter efficiency, and technical background behind achieving 8.88 on Japanese MT-Bench.