AccessEval: Benchmarking Disability Bias in Large Language Models
Paper • 2509.22703 • Published • 21
nomadicsynth PRO
nomadicsynth
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architecture research, knowledge discovery
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AccessEval: Benchmarking Disability Bias in Large Language Models published a model 13 days ago
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i hope good things come from this, but i have doubts.
reacted to Bc-AI's post with 👍 21 days ago
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2609
Smilyai News
Hello everyone! August has been a crazy month for us at Smilyai-Labs. We've been doing lots behind the scenes, so here's the latest 👇
1. MiniCoder
We are very close to releasing MiniCoder-1, our first-generation coding model designed for reasoning and coding. Our planned context window is 128K, but the earlier versions probably will not support that long! It's currently in the final stages of DPO so expect a release in early september.
Release: VERY SOON™🤣
2. Smilyai G1
So, the current plan is 20B parameter model total, with a MoE architecture, activating around 2B parameters per token. Its desgigned for maximum performance but keeping it runnable on consumer hardware. It's only a plan and i have no idea when me and the team can finish it. Expect a launch around the end of september to early october-ish. I have no guarantees so don't quote me on the launch date.
3. T1
Smilyai-T1 is another major model we are working on.
The goal for T1 is to take what we learnt from the countless architectural experiments and creating a powerful model designed for thinking. Think MiniCoder but reasons more and G1 but more capable. Its main goals are coding, math, reasoning and general capability.
4. Omni
We are also planning Omni, our first from scratch multimodal model. It will not launch this year as it will take a while. We are actively researching the best architecture for it and we will update progress as we go!
Thanks to our beta testers:
@guardamarcos
@ProCreations
@juiceb0xc0de
@Timmy6767
@Sbui503
@atom77777
@Fishtiks
@smartdigitalnetworks
@EmetTheGolum
@smilyai-large-team
@MUK-IS-GOAT
@Bc-AI
Thanks to my friends who work with me at lunchtimes (Smilyai-Labs team):
@MUK-IS-GOAT
@smilyai-large-team
August was wild. Let’s see what September brings. 🚀
— Bc-AI, on behalf of SmilyAI Labs
Hello everyone! August has been a crazy month for us at Smilyai-Labs. We've been doing lots behind the scenes, so here's the latest 👇
1. MiniCoder
We are very close to releasing MiniCoder-1, our first-generation coding model designed for reasoning and coding. Our planned context window is 128K, but the earlier versions probably will not support that long! It's currently in the final stages of DPO so expect a release in early september.
Release: VERY SOON™🤣
2. Smilyai G1
So, the current plan is 20B parameter model total, with a MoE architecture, activating around 2B parameters per token. Its desgigned for maximum performance but keeping it runnable on consumer hardware. It's only a plan and i have no idea when me and the team can finish it. Expect a launch around the end of september to early october-ish. I have no guarantees so don't quote me on the launch date.
3. T1
Smilyai-T1 is another major model we are working on.
The goal for T1 is to take what we learnt from the countless architectural experiments and creating a powerful model designed for thinking. Think MiniCoder but reasons more and G1 but more capable. Its main goals are coding, math, reasoning and general capability.
4. Omni
We are also planning Omni, our first from scratch multimodal model. It will not launch this year as it will take a while. We are actively researching the best architecture for it and we will update progress as we go!
Thanks to our beta testers:
@guardamarcos
@ProCreations
@juiceb0xc0de
@Timmy6767
@Sbui503
@atom77777
@Fishtiks
@smartdigitalnetworks
@EmetTheGolum
@smilyai-large-team
@MUK-IS-GOAT
@Bc-AI
Thanks to my friends who work with me at lunchtimes (Smilyai-Labs team):
@MUK-IS-GOAT
@smilyai-large-team
August was wild. Let’s see what September brings. 🚀
— Bc-AI, on behalf of SmilyAI Labs
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Shell-Code-Large
Dataset: ajibawa-2023/Shell-Code-Large
Shell-Code-Large is a large-scale corpus of Shell scripting source code comprising approximately 640,000 code samples stored in JSON Lines (.jsonl) format. The dataset is designed to support research in large language model (LLM) pretraining, code intelligence, DevOps automation, cloud infrastructure engineering, system administration, and software engineering automation.
By providing a high-volume, language-specific corpus focused exclusively on Shell scripting, Shell-Code-Large enables systematic experimentation in automation workflows, deployment pipelines, infrastructure management, and command-line tooling. These domains remain foundational to Linux systems, cloud-native platforms, CI/CD environments, and modern DevOps practices.
Shell-Code-Large addresses the need for a dedicated Shell-focused dataset at substantial scale, enabling targeted research into scripting patterns, command composition, workflow orchestration, infrastructure automation, and operational engineering practices
Dataset: ajibawa-2023/Shell-Code-Large
Shell-Code-Large is a large-scale corpus of Shell scripting source code comprising approximately 640,000 code samples stored in JSON Lines (.jsonl) format. The dataset is designed to support research in large language model (LLM) pretraining, code intelligence, DevOps automation, cloud infrastructure engineering, system administration, and software engineering automation.
By providing a high-volume, language-specific corpus focused exclusively on Shell scripting, Shell-Code-Large enables systematic experimentation in automation workflows, deployment pipelines, infrastructure management, and command-line tooling. These domains remain foundational to Linux systems, cloud-native platforms, CI/CD environments, and modern DevOps practices.
Shell-Code-Large addresses the need for a dedicated Shell-focused dataset at substantial scale, enabling targeted research into scripting patterns, command composition, workflow orchestration, infrastructure automation, and operational engineering practices
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