Falcon-OCR and Falcon-Perception
blogpost: https://huggingface.co/blog/tiiuae/falcon-perception HF collection: https://huggingface.co/collections/tiiuae/falcon-perception Ongoing llama.cpp support: https://github.com/ggml-org/llama.cpp/pull/21045 submitted by /u/Automatic_Truth_6666 [link] [comments]
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GPT-5.1 Codex, GPT-5.1-Codex-Max, and GPT-5.1-Codex-Mini deprecated
We have deprecated the following models across all GitHub Copilot experiences (including Copilot Chat, inline edits, ask and agent modes, and code completions) on April 1, 2026. Model Deprecation date The post GPT-5.1 Codex, GPT-5.1-Codex-Max, and GPT-5.1-Codex-Mini deprecated appeared first on The GitHub Blog .
v4.3.2
Changes Gemma 4 support with full tool-calling in the API and UI. 🆕 ik_llama.cpp support : Add ik_llama.cpp as a new backend through new textgen-portable-ik portable builds and a new --ik flag for full installs. ik_llama.cpp is a fork by the author of the imatrix quants, including support for new quant types, significantly more accurate KV cache quantization (via Hadamard KV cache rotation, enabled by default), and optimizations for MoE models and CPU inference. API: Add echo + logprobs for /v1/completions . The completions endpoint now supports the echo and logprobs parameters, returning token-level log probabilities for both prompt and generated tokens. Token IDs are also included in the output via a new top_logprobs_ids field. Further optimize my custom gradio fork, saving up to 50 ms
How to Run Local AI Agents on Consumer‑Grade Hardware: A Practical Guide
How to Run Local AI Agents on Consumer‑Grade Hardware: A Practical Guide Want to run powerful AI agents without the endless API bills of cloud services? The good news is you don’t need a data‑center‑grade workstation. A single modern consumer GPU is enough to host capable 9B‑parameter models like qwen3.5:9b, giving you private, low‑latency inference at a fraction of the cost. This article walks you through the exact hardware specs, VRAM needs, software installation steps, and budget‑friendly upgrade paths so you can get a local agent up and running today—no PhD required. Why a Consumer GPU Is Enough It’s a common myth that you must buy a professional‑grade card (think RTX A6000 or multiple GPUs linked via NVLink) to run LLMs locally. In reality, for 9B‑class models the sweet spot lies in t
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v4.3.2
Changes Gemma 4 support with full tool-calling in the API and UI. 🆕 ik_llama.cpp support : Add ik_llama.cpp as a new backend through new textgen-portable-ik portable builds and a new --ik flag for full installs. ik_llama.cpp is a fork by the author of the imatrix quants, including support for new quant types, significantly more accurate KV cache quantization (via Hadamard KV cache rotation, enabled by default), and optimizations for MoE models and CPU inference. API: Add echo + logprobs for /v1/completions . The completions endpoint now supports the echo and logprobs parameters, returning token-level log probabilities for both prompt and generated tokens. Token IDs are also included in the output via a new top_logprobs_ids field. Further optimize my custom gradio fork, saving up to 50 ms
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How to Run Local AI Agents on Consumer‑Grade Hardware: A Practical Guide Want to run powerful AI agents without the endless API bills of cloud services? The good news is you don’t need a data‑center‑grade workstation. A single modern consumer GPU is enough to host capable 9B‑parameter models like qwen3.5:9b, giving you private, low‑latency inference at a fraction of the cost. This article walks you through the exact hardware specs, VRAM needs, software installation steps, and budget‑friendly upgrade paths so you can get a local agent up and running today—no PhD required. Why a Consumer GPU Is Enough It’s a common myth that you must buy a professional‑grade card (think RTX A6000 or multiple GPUs linked via NVLink) to run LLMs locally. In reality, for 9B‑class models the sweet spot lies in t
Show HN: The Comments Owl for HN browser extension now hides obvious "AI" items
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