AI summary
TwIL-8B offers high-performance math and code reasoning on modest hardware, making advanced AI accessible without expensive infrastructure.
AI-generated summary. Check the original reporting for context and accuracy.
TwIL-8B offers high-performance math and code reasoning on modest hardware, making advanced AI accessible without expensive infrastructure.
AI-generated summary. Check the original reporting for context and accuracy.
This is the available feed excerpt, which may be shortened or in the publisher’s original language.
webAI has released TwIL-8B, an 8.8B reasoning model for math, code and instruction following. The TwIL-8B reasoning model is post-trained from IBM’s Granite 4.2 8B. Weights are on Hugging Face under a non-commercial license. webAI’s launch post frames the pitch around cost per task, not raw scale. The 4-bit build is 4.98 GiB, sized for CPU or modest-VRAM GPU inference. TL;DR Size: 8.79B parameters, dense. 131,072-token context, inherited from the base model. Runs on: CPU or modest-VRAM GPU via a 4.98 GiB Q4_K_M GGUF. Full bf16 weights are 16.38 GiB. Performance: Leads its size class on math and code, trails on knowledge benchmarks. Best: MATH-500 at 98.2, the top score in webAI’s comparison. Worst: GPQA Diamond at 70.2, 11.5 points behind Qwen3.5-9B. HLE is 12.6 (verbose variant). What is TwIL-8B? TwIL-8B is a dense decoder-only reasoning model from webAI. It has 40 layers, hidden size 4096, and 32 attention heads with 8 KV heads. Input and output are English text only. The model writes a block before each answer. It ships in 2 variants with the same architecture and tokenizer. Balanced is the default and produces shorter reasoning traces. Verbose reasons longer and targets hard contest-style problems. TwIL-8B extends webAI’s TwIL family. The first members were 1.7B and 3B formal-logic models released in August 2026. How was TwIL-8B trained? webAI used 3 post-training stages: Full-parameter SFT on reasoning traces for math, STEM and instruction following. GRPO/DAPO reinforcement learning on math, code, STEM and instruction prompts. This produces the verbose variant. Length-aware RL with a reward that favors shorter traces against per-domain length references. This produces the balanced variant. Training pools were decontaminated with 13-gram matching against AIME, HMMT, MATH-500, GPQA and other evals. How does TwIL-8B perform on benchmarks? All TwIL-8B scores are self-reported, high-effort pass@1 with thinking enabled. Scores marked † were measur
An automated sorting aid based on source trust, keywords, and entities, on a scale of 1–12. High: 9–12; medium: 6–8; low: 1–5. It does not measure urgency, verify reporting, or indicate editorial endorsement.
Ricursive’s new 65B open-weight model outperforms much larger rivals on coding benchmarks, proving smaller dense models can now match massive systems for agentic tasks.
This tool converts any document into structured JSON fields, streamlining data extraction and enabling efficient, automated processing of unstructured information.