[AINews] LLMs can read & write neural network weights: "Weight Space Programming" cuts Autoresearch out of the RSI loop
Editing Qwen’s weights to reduce reasoning loops, DeepSeek’s super-agent prototype, and the GDPx benchmark.
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Alibaba's Qwen Code repo just leaked qwen4-max, qwen4-coder-plus, and qwen4-omni-120b
PRs: https://github.com/QwenLM/qwen-code/pull/15762: "Measured on main at 79a9b753c60 , before this change: qwen4 , qwen4-max , qwen4-plus , qwen4.5-max , qwen4-coder-plus , qwen4-max-2026-10-01 , openai/qwen4-max and qwen4-omni-120b-think all resolved to 262,144. The last of those is already in use as the session model in the omni fixtures, so these ids are not hypothetical."…
llm 0.37
Release: llm 0.37 There's a lot of stuff in this new release of my LLM CLI tool and Python library, representing around two months of accumulated features and fixes. I added gpt-6.1-sol and gpt-6.1-terra , plus a new llm openai endpoint add command to make it easier to configure additional models that use OpenAI-compatible endpoints. I switched the default model (if you have not set your own…
New York Times and other outlets sue web browser Brave over alleged content scraping
The New York Times is suing privacy-focused web browser company Brave for allegedly scraping the outlet’s copyrighted content and unlawfully distributing it to its own customers, like Anthropic and Perplexity.
The New York Times sues Brave web browser for AI scraping and bypassing paywalls
The New York Times and a group of other publications have filed lawsuits against the company that makes the Brave web browser over alleged unlawful scraping to train AI and also letting users bypass their paywalls, as reported by Reuters. According to a complaint filed by the New York Daily News, the Chicago Tribune, and […]
Rtx 8000 48gb prices have decreased over the last two years while 3090 doubled.
In 2024 i purchased an RTX 8000 48gb for $2400. As the market has changed I’ve kept an eye on prices. Two years later, they are available around $2100. Its a little slower than 3090 at inference (672gb/s vs 936gb/s) but it has double the memory so it can run larger models / contexts / higher quants. Rtx 8000 also wins on power, 265w vs 700w (2x 3090 to match 48gb) In 2024 two 3090s would cost…
I found another niche use case for local LLMs, but need some advice
Kind of a random thing, but I've gotten to the age where I'm starting to care about what the city council is doing. So I downloaded all the available council meeting notes from the last 16 years and pointed Qwen 3.8 27B Q6 at them and started having it dig through notes. Namely I wanted to see who approved the purchase of Flock cameras for our town. Turns out the police chief got a $9,000 grant…
LFM3-1.7B quantization report: 3.6 GB VRAM lossless; 2.2 GB with modest degradation
This model looks quite spectacular. According to LiquidAI's own benchmarks, it scores higher than MiniCPM5-2B across the board, while offering 2x the context and taking up half the space in VRAM (3.6 GiB vs. 7.1 GiB for a quasi-lossless quant). Read the report: https://github.com/crusaderky/pixi-llm-recipes/blob/main/perplexity/LFM3-1.7B/README.md Raw plots as interactive HTML…
Qwen 3.8 27b is sometimes so smart it scares me...
For fun, I gave Qwen 3.8 27b a physics problem involving some heavy math. A little bit into its reasoning, Qwen took a look at its tools, found that it had access to Blender via blender mcp - and that it could write and execute arbitrary python code through that. So it engaged Blender's python support to write itself bits of python code to do its math faster. You really have to tell this model…
Is there still a point to fine-tuning local LLMs?
I've been getting more into running LLMs locally, and I'm starting to wonder if fine-tuning is even worth the effort anymore. Models are getting really good, context windows are huge, and with things like RAG, subagents, and reasoning models, it seems like you can get pretty far without touching the model weights. For most use cases, wouldn't you be better off just using a good base model and…
GPT-6 Astra Edits Qwen Weights: MATH-500 7.9% to 48.1%
submitted by /u/1-800-methdyke [link] [comments]
Inconsistencies in Unsloth's LiveCodeBench Results
I compared two published benchmark charts for DeepSeek-V4.1-Flash-1017 and noticed something rather unusual. All four Unsloth quantizations AND the Q8 baseline simultaneously improved by approximately 2–3 percentage points on LiveCodeBench v6. Meanwhile, the GSQ-RCO results changed much less consistently. 1. The numbers Model First chart Updated chart Change Unsloth IQ1_S 88.95% 91.0% +2.05…
Kind of enjoying the ISTA (GSQ-RCO) vs Unsloth beef
I'm not on anyone's side, I just love the competition. Both teams pushing each other means better quants for all of us. What I don't understand is why ISTA doesn't publish KLD for their quants. It doesn't depend on how long the model thinks, so it would make this a lot easier to compare. The only KLD I've seen for GSQ-RCO is in Unsloth's charts. Maybe there's a reason I'm missing. (English isn't…
Sparks of Weight-Space Programming: LLMs can natively read & write neural network weights
submitted by /u/teakhop [link] [comments]
Do we have any benchmarks on quality degradation between Basalt, Infernix, Strata(-nvfp4)? Not "engine go more brr" benchmarks, exploring wrong avenues fast is worthless.
While fast inference is very welcome, I would like to know if it's worth increasing the speed over strata-nvfp4 or am I paying some unacceptable penalties. One thing I've noticed with Basalt is that it doesn't have dynamic rope scaling, so it goes off with the yarn scaling statically (2 for 524K context) from the get-go. Basalt also lacks a censorship switch, but the machinery exists so it's…
Yet another reason to self host: Anthropic has (again) cut back on reverse engineering tools
https://www.tomshardware.com/tech-industry/artificial-intelligence/anthropic-confirms-new-claude-code-limits-to-prevent-game-cloning-users-report-opus-5-5-is-also-actively-blocking-reverse-engineering-tasks Besides privacy and being in control of your data. Besides being able to control the quality of the inference compute. Besides being able to discuss things with the model and not have the cops…
Fine tuned open model ftw
another win for GLM. Chenru Duan on X: "We’re introducing Hydrogen-1.0, our first open-weight model for scientific research. We post-trained GLM-5.3 (@Zai_org) on our internal scientific data, lab records, and discovery trajectories. It scores 38.3% on FrontierScience-Research vs GPT-6-Astra’s 33.3% submitted by /u/Choice_Celery9481 [link] [comments]
Official Statement: Qwen Models Will Stick to Open Source to the End!
There were some rumours the last few days that Qwen4 will be the last open-weight series of Qwen, and future ones would be API only. The following statement contradicts the rumours and clearly states the stance that Qwen is taking regarding their future releases. Qwen Head Liu Dayi Heng Clearly States : Future Qwen 4.0 and Qwen 5.0 Will Continue to Be Open Source! He believes that the value of AI…
llama + CUDA: flash_attn_ext_rows (fix unified kv for multi-seq) by am17an · Pull Request #29510 · ggml-org/llama.cpp
Do you run multiple agents locally like I do? If so, this is a really nice speedup for the unified KV cache submitted by /u/jacek2023 [link] [comments]