<?xml version="1.0" encoding="UTF-8"?><rss version="2.0"><channel><title>Mike Bommarito - AI Models Bookmarks</title><description>All bookmarks in the AI Models category</description><link>https://michaelbommarito.com</link><language>en-us</language><copyright>Copyright 2026 Michael Bommarito</copyright><webMaster>michael.bommarito@gmail.com (Michael Bommarito)</webMaster><managingEditor>michael.bommarito@gmail.com (Michael Bommarito)</managingEditor><ttl>1440</ttl><generator>Astro</generator><docs>http://www.rssboard.org/rss-specification</docs><item><title>avataRL - Reinforcement Learning from Zero Pretrain</title><link>https://tokenbender.com/post.html?id=avatarl</link><guid isPermaLink="false">ai-models/avatarl-rl-zero-pretrain</guid><description>Experimental project by TokenBender exploring whether reinforcement learning can be successfully implemented from zero pretraining - an interesting research direction in AI/ML | Notes: TokenBender (self-described &apos;ml hermit&apos;) is exploring whether RL from zero pretrain is possible. Also maintains other projects including LLaMA fine-tunes and tools for local LLM hosting. GitHub repo has 49 stars showing community interest. | Tags: reinforcement-learning, rl, experimental, pretrain, ai-research, open-source, github, llm, local-models | Author: TokenBender | Source: tokenbender.com</description><pubDate>Sat, 09 Aug 2025 00:00:00 GMT</pubDate><category>AI Models</category><category>reinforcement-learning</category><category>rl</category><category>experimental</category><category>pretrain</category><category>ai-research</category><category>open-source</category><category>github</category><category>llm</category><category>local-models</category><author>TokenBender</author></item><item><title>Intel GPT-OSS 20B INT4 Quantized Model</title><link>https://huggingface.co/Intel/gpt-oss-20b-int4-rtn-AutoRound</link><guid isPermaLink="false">gpt-oss/gpt-oss-20b-intel-int4</guid><description>Intel&apos;s INT4 quantized version of GPT-OSS 20B using AutoRound technique with symmetric quantization, optimized for efficient inference on CPU/Intel GPU/CUDA hardware. | Notes: 1.8B parameter quantized model with group size 128, using RTN (no algorithm tuning). Mixed int4 model with non-expert layers at 16-bit precision for better performance. | Tags: gpt-oss, llm, quantization, INT4, Intel, AutoRound, model-compression, inference-optimization | Source: HuggingFace</description><pubDate>Sat, 09 Aug 2025 00:00:00 GMT</pubDate><category>AI Models</category><category>gpt-oss</category><category>llm</category><category>quantization</category><category>INT4</category><category>Intel</category><category>AutoRound</category><category>model-compression</category><category>inference-optimization</category></item><item><title>GPT-OSS 120B on HuggingFace</title><link>https://huggingface.co/openai/gpt-oss-120b</link><guid isPermaLink="false">gpt-oss/gpt-oss-120b-huggingface</guid><description>117B parameter model with extreme sparsity for production use | Notes: Fits on single H100 GPU with MXFP4 quantization | Tags: gpt-oss, llm, apache-2.0 | Source: HuggingFace</description><pubDate>Tue, 05 Aug 2025 00:00:00 GMT</pubDate><category>AI Models</category><category>gpt-oss</category><category>llm</category><category>apache-2.0</category></item><item><title>GPT-OSS 20B on HuggingFace</title><link>https://huggingface.co/openai/gpt-oss-20b</link><guid isPermaLink="false">gpt-oss/gpt-oss-20b-huggingface</guid><description>21B parameter model optimized for local deployment | Notes: Runs in ~16GB VRAM, ideal for consumer hardware | Tags: gpt-oss, llm, apache-2.0, local | Source: HuggingFace</description><pubDate>Tue, 05 Aug 2025 00:00:00 GMT</pubDate><category>AI Models</category><category>gpt-oss</category><category>llm</category><category>apache-2.0</category><category>local</category></item><item><title>Introducing GPT-OSS</title><link>https://openai.com/index/introducing-gpt-oss/</link><guid isPermaLink="false">gpt-oss/gpt-oss-announcement</guid><description>Detailed announcement of GPT-OSS models with benchmarks and examples | Tags: gpt-oss, blog, announcement | Author: OpenAI | Source: OpenAI</description><pubDate>Tue, 05 Aug 2025 00:00:00 GMT</pubDate><category>AI Models</category><category>gpt-oss</category><category>blog</category><category>announcement</category><author>OpenAI</author></item><item><title>GPT-OSS GitHub Repository</title><link>https://github.com/openai/gpt-oss</link><guid isPermaLink="false">gpt-oss/gpt-oss-github</guid><description>Reference PyTorch implementation and tools for GPT-OSS models | Tags: gpt-oss, pytorch, implementation | Author: OpenAI | Source: GitHub</description><pubDate>Tue, 05 Aug 2025 00:00:00 GMT</pubDate><category>AI Models</category><category>gpt-oss</category><category>pytorch</category><category>implementation</category><author>OpenAI</author></item><item><title>OpenAI Harmony</title><link>https://github.com/openai/harmony</link><guid isPermaLink="false">gpt-oss/gpt-oss-harmony</guid><description>Structured response format specification for reasoning and tool use | Tags: harmony, gpt-oss, response-format | Author: OpenAI | Source: GitHub</description><pubDate>Tue, 05 Aug 2025 00:00:00 GMT</pubDate><category>AI Models</category><category>harmony</category><category>gpt-oss</category><category>response-format</category><author>OpenAI</author></item><item><title>GPT-OSS Model Card</title><link>https://cdn.openai.com/pdf/419b6906-9da6-406c-a19d-1bb078ac7637/oai_gpt-oss_model_card.pdf</link><guid isPermaLink="false">gpt-oss/gpt-oss-model-card</guid><description>Technical specifications and training details for GPT-OSS models | Tags: gpt-oss, model-card, specifications | Source: OpenAI</description><pubDate>Tue, 05 Aug 2025 00:00:00 GMT</pubDate><category>AI Models</category><category>gpt-oss</category><category>model-card</category><category>specifications</category></item><item><title>Run GPT-OSS with Ollama</title><link>https://cookbook.openai.com/articles/gpt-oss/run-locally-ollama</link><guid isPermaLink="false">gpt-oss/gpt-oss-ollama-guide</guid><description>Guide for running GPT-OSS models locally using Ollama | Tags: gpt-oss, ollama, local-deployment | Source: OpenAI Cookbook</description><pubDate>Tue, 05 Aug 2025 00:00:00 GMT</pubDate><category>AI Models</category><category>gpt-oss</category><category>ollama</category><category>local-deployment</category></item><item><title>OpenAI Open Models</title><link>https://openai.com/open-models/</link><guid isPermaLink="false">gpt-oss/gpt-oss-open-models</guid><description>Official announcement page for GPT-OSS open-weight reasoning models | Tags: gpt-oss, openai, announcement | Author: OpenAI | Source: OpenAI</description><pubDate>Tue, 05 Aug 2025 00:00:00 GMT</pubDate><category>AI Models</category><category>gpt-oss</category><category>openai</category><category>announcement</category><author>OpenAI</author></item><item><title>GPT-OSS Playground</title><link>https://gpt-oss.com/</link><guid isPermaLink="false">gpt-oss/gpt-oss-playground</guid><description>Interactive demo to try GPT-OSS models directly in browser | Tags: gpt-oss, demo, playground | Source: OpenAI</description><pubDate>Tue, 05 Aug 2025 00:00:00 GMT</pubDate><category>AI Models</category><category>gpt-oss</category><category>demo</category><category>playground</category></item><item><title>GPT-OSS Red Teaming Challenge</title><link>https://www.kaggle.com/competitions/openai-gpt-oss-20b-red-teaming/</link><guid isPermaLink="false">gpt-oss/gpt-oss-red-teaming</guid><description>$500k Kaggle competition for community safety testing | Tags: gpt-oss, red-teaming, kaggle, competition | Source: Kaggle</description><pubDate>Tue, 05 Aug 2025 00:00:00 GMT</pubDate><category>AI Models</category><category>gpt-oss</category><category>red-teaming</category><category>kaggle</category><category>competition</category></item><item><title>GPT-OSS Model Safety Paper</title><link>https://cdn.openai.com/pdf/231bf018-659a-494d-976c-2efdfc72b652/oai_gpt-oss_Model_Safety.pdf</link><guid isPermaLink="false">gpt-oss/gpt-oss-safety-paper</guid><description>Comprehensive safety analysis including malicious fine-tuning testing | Tags: gpt-oss, safety, research | Source: OpenAI</description><pubDate>Tue, 05 Aug 2025 00:00:00 GMT</pubDate><category>AI Models</category><category>gpt-oss</category><category>safety</category><category>research</category></item><item><title>Run GPT-OSS with Transformers</title><link>https://cookbook.openai.com/articles/gpt-oss/run-transformers</link><guid isPermaLink="false">gpt-oss/gpt-oss-transformers-guide</guid><description>Development setup guide using HuggingFace Transformers library | Tags: gpt-oss, transformers, huggingface | Source: OpenAI Cookbook</description><pubDate>Tue, 05 Aug 2025 00:00:00 GMT</pubDate><category>AI Models</category><category>gpt-oss</category><category>transformers</category><category>huggingface</category></item><item><title>Run GPT-OSS with vLLM</title><link>https://cookbook.openai.com/articles/gpt-oss/run-vllm</link><guid isPermaLink="false">gpt-oss/gpt-oss-vllm-guide</guid><description>Production deployment guide using vLLM for high-performance inference | Notes: Best for production deployments, supports CUDA 12.1+ | Tags: gpt-oss, vllm, production | Source: OpenAI Cookbook</description><pubDate>Tue, 05 Aug 2025 00:00:00 GMT</pubDate><category>AI Models</category><category>gpt-oss</category><category>vllm</category><category>production</category></item></channel></rss>