[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"summary-gemini-file-search-2-0-cuts-multimodal-rag-to-4-ap-summary":3,"summaries-facets-categories":101,"summary-related-gemini-file-search-2-0-cuts-multimodal-rag-to-4-ap-summary":4398},{"id":4,"title":5,"ai":6,"body":13,"categories":52,"created_at":54,"date_modified":54,"description":46,"extension":55,"faq":54,"featured":56,"kicker_label":54,"meta":57,"navigation":83,"path":84,"published_at":85,"question":54,"scraped_at":86,"seo":87,"sitemap":88,"source_id":89,"source_name":90,"source_type":91,"source_url":92,"stem":93,"tags":94,"thumbnail_url":54,"tldr":98,"unknown_tags":99,"__hash__":100},"summaries\u002Fsummaries\u002Fgemini-file-search-2-0-cuts-multimodal-rag-to-4-ap-summary.md","Gemini File Search 2.0 Cuts Multimodal RAG to 4 API Calls",{"provider":7,"model":8,"input_tokens":9,"output_tokens":10,"processing_time_ms":11,"cost_usd":12},"openrouter","x-ai\u002Fgrok-4.1-fast",5186,1609,16896,0.00133485,{"type":14,"value":15,"toc":45},"minimark",[16,21,25,28,32,35,38,42],[17,18,20],"h2",{"id":19},"build-multimodal-rag-in-minutes-with-file-search-store","Build Multimodal RAG in Minutes with File Search Store",[22,23,24],"p",{},"Upload documents to a Gemini File Search Store, and it automatically chunks text, embeds both text and images into a unified multimodal vector space using Embeddings 2.0, performs semantic clustering, and indexes for retrieval—all asynchronously without custom parsers or vector DBs. Query the store directly (e.g., \"Based on architecture diagram in Figure 1, what comes between multi-head attention and feed-forward in the encoder?\") to get precise answers combining visual and textual context, like \"add & norm,\" proven on the \"Attention Is All You Need\" paper. This end-to-end process uses just 4 API calls: create store, upload file, embed\u002Findex, and query—replacing manual stitching of ingestion, parsing, chunking, embedding APIs, vector storage, and retrievers.",[22,26,27],{},"The store acts as a single managed resource for ingestion once, then real-time API-driven retrieval and generation, enabling production multimodal search without infrastructure overhead.",[17,29,31],{"id":30},"traditional-rags-heavy-lift-vs-file-search-simplicity","Traditional RAG's Heavy Lift vs File Search Simplicity",[22,33,34],{},"Traditional multimodal RAG demands separate steps: parse complex formats (tables, lists, images), chunk without overlap, embed chunks via API, store in a costly vector DB, then build retriever + LLM pipeline—a 6-month engineering effort requiring specialized maintenance. File Search collapses this stack: no custom parsing\u002Fchunking logic, no separate embeddings API or DB management, no citation plumbing. Embeddings 2.0 unifies text\u002Fimages in one vector space, making multimodality native rather than bolted-on.",[22,36,37],{},"Result: Developers who spent a year on pipelines can now prototype and ship multimodal RAG apps instantly, focusing on app logic over infra.",[17,39,41],{"id":40},"trade-offs-sledgehammer-for-most-cases-not-universal","Trade-offs: Sledgehammer for Most Cases, Not Universal",[22,43,44],{},"File Search excels for file-based multimodal queries, killing custom RAG for docs with diagrams (e.g., papers, reports) by automating 90% of the stack. It won't fully replace RAG for non-file data, custom retrieval logic, or massive scale needing fine-tuned control. Still rough edges in async indexing waits and store management, but for 80% of use cases, it's a massive unlock—build faster, iterate on prompts\u002Fqueries instead of pipelines.",{"title":46,"searchDepth":47,"depth":47,"links":48},"",2,[49,50,51],{"id":19,"depth":47,"text":20},{"id":30,"depth":47,"text":31},{"id":40,"depth":47,"text":41},[53],"AI & LLMs",null,"md",false,{"content_references":58,"triage":78},[59,63,68,71,74],{"type":60,"title":61,"context":62},"paper","Attention Is All You Need","mentioned",{"type":64,"title":65,"url":66,"context":67},"other","Gemini API File Search docs","https:\u002F\u002Fai.google.dev\u002Fgemini-api\u002Fdocs\u002Ffile-search","recommended",{"type":64,"title":69,"url":70,"context":62},"Gemini API File Search multimodal RAG announcement","https:\u002F\u002Fblog.google\u002Ftechnology\u002Fdevelopers\u002Fgemini-api-file-search-multimodal-rag\u002F",{"type":64,"title":72,"url":73,"context":67},"Multimodal RAG with the Gemini API File Search Tool: A Developer Guide","https:\u002F\u002Fdev.to\u002Fgoogleai\u002Fmultimodal-rag-with-the-gemini-api-file-search-tool-a-developer-guide-5878",{"type":75,"title":76,"url":77,"context":67},"tool","AI Studio sample app","https:\u002F\u002Fai.studio\u002Fapps\u002Facb0ca81-7130-43ae-a31f-bedd96d28294",{"relevance":79,"novelty":80,"quality":80,"actionability":79,"composite":81,"reasoning":82},5,4,4.55,"Category: AI & LLMs. The article provides a detailed overview of how Gemini File Search 2.0 simplifies the process of building multimodal retrieval-augmented generation (RAG) applications, addressing a specific pain point for developers overwhelmed by complex setups. It offers actionable steps with just 4 API calls, making it immediately applicable for product builders looking to streamline their workflows.",true,"\u002Fsummaries\u002Fgemini-file-search-2-0-cuts-multimodal-rag-to-4-ap-summary","2026-05-07 14:00:00","2026-05-07 16:31:32",{"title":5,"description":46},{"loc":84},"e7802614eaf8f398","AI with Surya","article","https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=4n9Z-9YEtyY","summaries\u002Fgemini-file-search-2-0-cuts-multimodal-rag-to-4-ap-summary",[95,96,97],"llm","ai-tools","ai-llms","Gemini File Search 2.0 handles multimodal RAG—chunking, text\u002Fimage embeddings, storage, retrieval—in one managed store via 4 API calls, slashing a 6-month engineering project to 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4 31B-IT: Multimodal Open Model with 256K Context",{"provider":7,"model":8,"input_tokens":4403,"output_tokens":4404,"processing_time_ms":4405,"cost_usd":4406},7962,2013,15128,0.00230805,{"type":14,"value":4408,"toc":4460},[4409,4413,4416,4419,4423,4426,4430,4454,4457],[17,4410,4412],{"id":4411},"architectural-designs-for-scalable-multimodal-deployment","Architectural Designs for Scalable Multimodal Deployment",[22,4414,4415],{},"Gemma 4 family includes dense models (E2B: 2.3B effective params\u002F5.1B total, 35 layers, 128K context; E4B: 4.5B\u002F8B, 42 layers, 128K; 31B: 30.7B params, 60 layers, 256K) and MoE (26B A4B: 25.2B total\u002F3.8B active, 30 layers, 8\u002F128 experts, 256K). All use 262K vocab, hybrid attention (sliding window 512-1024 tokens + global layers with unified KV and p-RoPE for memory efficiency). Smaller E2B\u002FE4B employ Per-Layer Embeddings (PLE: ~150M vision\u002F~300M audio encoders) for on-device efficiency; larger have ~550M vision. Supports text\u002Fimage all sizes, audio\u002Fvideo on small (audio max 30s, video 60s at 1fps). Native system prompts, function-calling, configurable thinking modes (\u003C|think|>, \u003C|channel|thought\n\u003Cchannel|>) boost reasoning, coding, agents.",[22,4417,4418],{},"MoE activates only 4B params for 26B A4B, matching E4B speed but with larger capacity; dense 31B suits workstations. Variable image resolution via token budget trades detail for speed.",[17,4420,4422],{"id":4421},"superior-benchmarks-in-reasoning-coding-multimodality","Superior Benchmarks in Reasoning, Coding, Multimodality",[22,4424,4425],{},"Instruction-tuned models excel: 31B leads with 85.2% MMLU Pro, 89.2% AIME 2026 (no tools), 80.0% LiveCodeBench v6, 2150 Codeforces ELO, 84.3% GPQA Diamond, 76.9% Tau2, 19.5% HLE (no tools)\u002F26.5% (with search), 74.4% BigBench Hard. 26B A4B close: 82.6% MMLU Pro, 88.3% AIME, 77.1% LiveCodeBench, 1718 ELO. Small: E4B 69.4% MMLU Pro, E2B 60.0%. Multimodal: 31B 88.4% MMMLU, 76.9% MMMU Pro, 0.131 OmniDocBench edit distance, 85.6% MATH-Vision; audio E4B 35.54% CoVoST, 0.08 FLEURS. Long-context: 31B 66.4% MRCR v2 128K. Outperforms Gemma 3 27B across board (e.g., 67.6% MMLU Pro vs 85.2%).",[17,4427,4429],{"id":4428},"integration-code-and-best-practices-for-production","Integration Code and Best Practices for Production",[22,4431,4432,4433,4437,4438,4441,4442,4445,4446,4449,4450,4453],{},"Load via Transformers: ",[4434,4435,4436],"code",{},"pip install -U transformers torch accelerate","; use ",[4434,4439,4440],{},"AutoProcessor\u002FAutoModelForCausalLM"," or ",[4434,4443,4444],{},"AutoModelForMultimodalLM"," (add ",[4434,4447,4448],{},"torchvision librosa torchcodec"," for vision\u002Faudio\u002Fvideo). Chat template supports system\u002Fuser roles, ",[4434,4451,4452],{},"enable_thinking=True"," for reasoning parsing. Multimodal prompts embed {\"type\":\"image\u002Faudio\u002Fvideo\",\"url\":URL} before text.",[22,4455,4456],{},"Sampling: temperature=1.0, top_p=0.95, top_k=64. Audio prompts: transcribe numbers as digits, no newlines; translate formats source then '{TARGET}: translation'. Multi-turn via standard roles. Safety: rigorous evals match Gemini, low violations without filters, outperforms prior Gemma.",[22,4458,4459],{},"Pretraining on web\u002Fcode\u002Fimages\u002Faudio (cutoff Jan 2025), cleaned via dedup, PII filtering. Limits: no fine-grained video\u002Faudio beyond specs, potential biases\u002Fhallucinations; intended for reasoning\u002Fcoding\u002Fagents, not exhaustive list.",{"title":46,"searchDepth":47,"depth":47,"links":4461},[4462,4463,4464],{"id":4411,"depth":47,"text":4412},{"id":4421,"depth":47,"text":4422},{"id":4428,"depth":47,"text":4429},[53],{"content_references":4467,"triage":4484},[4468,4471,4474,4477,4480],{"type":64,"title":4469,"url":4470,"context":62},"Gemma 4 Launch Blog","https:\u002F\u002Fblog.google\u002Finnovation-and-ai\u002Ftechnology\u002Fdevelopers-tools\u002Fgemma-4\u002F",{"type":64,"title":4472,"url":4473,"context":62},"Gemma Documentation","https:\u002F\u002Fai.google.dev\u002Fgemma\u002Fdocs\u002Fcore",{"type":64,"title":4475,"url":4476,"context":62},"Gemma 4 License","https:\u002F\u002Fai.google.dev\u002Fgemma\u002Fdocs\u002Fgemma_4_license",{"type":75,"title":4478,"url":4479,"context":62},"Google Gemma GitHub","https:\u002F\u002Fgithub.com\u002Fgoogle-gemma",{"type":64,"title":4481,"url":4482,"context":4483},"Google’s AI Principles","https:\u002F\u002Fai.google\u002Fprinciples\u002F","cited",{"relevance":79,"novelty":80,"quality":80,"actionability":80,"composite":4485,"reasoning":4486},4.35,"Category: AI & LLMs. The article provides in-depth technical details about the Gemma 4 model, including its architecture and performance benchmarks, which are crucial for developers looking to integrate AI models into their products. It also includes practical integration code and best practices for production, making it actionable for the target audience.","\u002Fsummaries\u002Fgemma-4-31b-it-multimodal-open-model-with-256k-con-summary","2026-04-16 03:04:51",{"title":4401,"description":46},{"loc":4487},"c2e1f12b3205a3e8","__oneoff__","https:\u002F\u002Fhuggingface.co\u002Fgg-hf-gg\u002Fgemma-4-31B-it","summaries\u002Fgemma-4-31b-it-multimodal-open-model-with-256k-con-summary",[95,4496,96,97],"open-source","Gemma 4 31B-IT achieves 85.2% MMLU Pro, 80% LiveCodeBench, supports text\u002Fimage (video\u002Faudio on small), 256K context via hybrid attention, Apache 2.0 for phones to servers.",[97],"E5zMk4XiNoVTTBaZPDYFuAMz6sxQ8rneF7n1pJcuFDY",{"id":4501,"title":4502,"ai":4503,"body":4508,"categories":4542,"created_at":54,"date_modified":54,"description":46,"extension":55,"faq":54,"featured":56,"kicker_label":54,"meta":4543,"navigation":83,"path":4569,"published_at":54,"question":54,"scraped_at":4570,"seo":4571,"sitemap":4572,"source_id":4573,"source_name":4492,"source_type":91,"source_url":4574,"stem":4575,"tags":4576,"thumbnail_url":54,"tldr":4577,"unknown_tags":4578,"__hash__":4579},"summaries\u002Fsummaries\u002Fglasswing-ai-finds-zero-days-to-secure-critical-so-summary.md","Glasswing: AI Finds Zero-Days to Secure Critical Software",{"provider":7,"model":8,"input_tokens":4504,"output_tokens":4505,"processing_time_ms":4506,"cost_usd":4507},8888,2041,17765,0.00277545,{"type":14,"value":4509,"toc":4537},[4510,4514,4517,4520,4524,4527,4530,4534],[17,4511,4513],{"id":4512},"mythos-previews-superior-vulnerability-detection","Mythos Preview's Superior Vulnerability Detection",[22,4515,4516],{},"Claude Mythos Preview, an unreleased frontier model, autonomously identifies thousands of zero-day vulnerabilities—many critical—in every major operating system and web browser, plus tools like FFmpeg and the Linux kernel. Specific examples include a 27-year-old OpenBSD flaw allowing remote crashes on firewalls (patched), a 16-year-old FFmpeg bug missed by 5 million automated tests, and a chained Linux kernel exploit escalating user access to full control. It outperforms Claude Opus 4.6 on CyberGym (83.1% vs 66.6% vulnerability reproduction) and agentic coding benchmarks like SWE-bench Verified (93.9% vs 80.8%), Terminal-Bench 2.0 (82.0% vs 65.4%), and GPQA Diamond (94.6% vs 91.3%). These capabilities stem from advanced agentic coding, reasoning, and search, enabling it to spot flaws surviving decades of human and automated scrutiny, while developing sophisticated exploits.",[22,4518,4519],{},"To counter proliferation risks—where AI lowers expertise barriers for attackers, potentially amplifying $500B annual global cybercrime costs—defenders gain an edge by using the same tools proactively. Partners report it uncovers complex issues prior models missed, accelerating fixes at scale.",[17,4521,4523],{"id":4522},"project-glasswing-enables-industry-wide-defense","Project Glasswing Enables Industry-Wide Defense",[22,4525,4526],{},"Launched with partners including AWS, Anthropic, Apple, Broadcom, Cisco, CrowdStrike, Google, JPMorganChase, Linux Foundation, Microsoft, NVIDIA, and Palo Alto Networks—plus 40+ critical infrastructure orgs—Project Glasswing provides Mythos Preview access for scanning first-party and open-source systems. Focus areas: local vulnerability detection, black-box binary testing, endpoint securing, and penetration testing. Anthropic commits $100M in usage credits (post-preview: $25\u002F$125 per million tokens via Claude API, Bedrock, Vertex AI, Microsoft Foundry) and $4M donations ($2.5M to Alpha-Omega\u002FOpenSSF, $1.5M to Apache). Open-source maintainers apply via Claude for Open Source program.",[22,4528,4529],{},"Partners like Cisco emphasize AI's pace\u002Fscale shift demands new hardening; AWS integrates it into 400T daily network flows; Microsoft notes CTI-REALM gains; CrowdStrike warns of collapsing exploit timelines (months to minutes); Linux Foundation sees it as a 'sidekick' for maintainers lacking teams. Google highlights ecosystem tools like Big Sleep\u002FCodeMender. This collaboration shares learnings to harden shared cyber surfaces before adversarial use.",[17,4531,4533],{"id":4532},"balancing-ai-cyber-risks-with-safeguarded-deployment","Balancing AI Cyber Risks with Safeguarded Deployment",[22,4535,4536],{},"AI cyber skills rival top humans (echoing DARPA's 2016 Cyber Grand Challenge), risking frequent\u002Fdestructive attacks on banking, healthcare, energy, transport, and government without safeguards. Yet optimism prevails: Mythos aids bug-free software creation. Anthropic won't release it publicly but plans safeguards in upcoming Claude Opus for safe, scaled deployment in cybersecurity and beyond. Cryptographic hashes disclosed for unpatched vulns; full details post-fix via Frontier Red Team blog.",{"title":46,"searchDepth":47,"depth":47,"links":4538},[4539,4540,4541],{"id":4512,"depth":47,"text":4513},{"id":4522,"depth":47,"text":4523},{"id":4532,"depth":47,"text":4533},[53],{"content_references":4544,"triage":4565},[4545,4550,4555,4559,4562],{"type":4546,"title":4547,"publisher":4548,"url":4549,"context":4483},"report","Estimating Global Yearly Cybercrime Damage Costs","Governance.ai","https:\u002F\u002Fwww.governance.ai\u002Fresearch-paper\u002Festimating-global-yearly-cybercrime-damage-costs",{"type":4551,"title":4552,"publisher":4553,"url":4554,"context":62},"event","DARPA Cyber Grand Challenge","DARPA","https:\u002F\u002Fwww.darpa.mil\u002Fresearch\u002Fprograms\u002Fcyber-grand-challenge",{"type":64,"title":4556,"publisher":4557,"url":4558,"context":62},"Claude Mythos Preview System Card","Anthropic","https:\u002F\u002Fanthropic.com\u002Fclaude-mythos-preview-system-card",{"type":64,"title":4560,"publisher":4557,"url":4561,"context":4483},"Frontier Red Team Blog: Mythos Preview","https:\u002F\u002Fred.anthropic.com\u002F2026\u002Fmythos-preview",{"type":64,"title":4563,"url":4564,"context":67},"Claude for Open Source","https:\u002F\u002Fclaude.com\u002Fcontact-sales\u002Fclaude-for-oss",{"relevance":4566,"novelty":4566,"quality":80,"actionability":47,"composite":4567,"reasoning":4568},3,3.05,"Category: AI & LLMs. The article discusses a new AI model's capabilities in detecting vulnerabilities, which is relevant to AI engineering and security. However, it lacks practical applications or frameworks that the audience can directly implement in their product development.","\u002Fsummaries\u002Fglasswing-ai-finds-zero-days-to-secure-critical-so-summary","2026-04-14 14:30:00",{"title":4502,"description":46},{"loc":4569},"b0e284183065467e","https:\u002F\u002Fwww.anthropic.com\u002Fglasswing","summaries\u002Fglasswing-ai-finds-zero-days-to-secure-critical-so-summary",[95,96,4496,97],"Claude Mythos Preview autonomously detects thousands of high-severity zero-days in every major OS\u002Fbrowser; Project Glasswing shares access with 40+ orgs via $100M credits to prioritize defense over attack.",[97],"q5rg5XUIZZYnOo9KD9cJfaYVxdU7yTaMkVXI0XjNlro",{"id":4581,"title":4582,"ai":4583,"body":4588,"categories":4692,"created_at":54,"date_modified":54,"description":46,"extension":55,"faq":54,"featured":56,"kicker_label":54,"meta":4693,"navigation":83,"path":4718,"published_at":54,"question":54,"scraped_at":4719,"seo":4720,"sitemap":4721,"source_id":4722,"source_name":4492,"source_type":91,"source_url":4723,"stem":4724,"tags":4725,"thumbnail_url":54,"tldr":4727,"unknown_tags":4728,"__hash__":4729},"summaries\u002Fsummaries\u002Fload-4-bit-awq-llms-in-transformers-for-low-memory-summary.md","Load 4-Bit AWQ LLMs in Transformers for Low-Memory Inference",{"provider":7,"model":8,"input_tokens":4584,"output_tokens":4585,"processing_time_ms":4586,"cost_usd":4587},5033,1989,8133,0.00149425,{"type":14,"value":4589,"toc":4687},[4590,4594,4617,4621,4636,4676,4680],[17,4591,4593],{"id":4592},"load-awq-quantized-models-with-one-line","Load AWQ-Quantized Models with One Line",[22,4595,4596,4597,4600,4601,4604,4605,4608,4609,4612,4613,4616],{},"AWQ (Activation-aware Weight Quantization) compresses LLMs to 4-bit weights while preserving a small set of performance-critical weights in higher precision, minimizing accuracy loss versus full quantization. Identify AWQ models by ",[4434,4598,4599],{},"quant_method: \"awq\""," in their config.json. Install autoawq (which pins Transformers to v4.47.1—reinstall Transformers after for compatibility), then load with ",[4434,4602,4603],{},"AutoModelForCausalLM.from_pretrained(model_id, quant_method=\"awq\")",". This auto-converts non-quantized weights (e.g., embeddings) to fp16 for speed; override via ",[4434,4606,4607],{},"dtype=torch.bfloat16",". Move to GPU with ",[4434,4610,4611],{},"device_map=\"auto\""," or CPU otherwise. Add ",[4434,4614,4615],{},"attn_implementation=\"flash_attention_2\""," for further acceleration, but it conflicts with fused modules below. Trade-off: AWQ prioritizes salient weights per channel, beating round-to-nearest methods on benchmarks like perplexity and zero-shot tasks.",[17,4618,4620],{"id":4619},"fused-modules-double-prefilldecode-throughput","Fused Modules Double Prefill\u002FDecode Throughput",[22,4622,4623,4624,4627,4628,4631,4632,4635],{},"Fuse AWQ linear layers into single kernels for 2x faster prefill (up to 3184 → 3044 tokens\u002Fs at 1024 length) and decode (31 → 89 tokens\u002Fs at 2048 length) at batch_size=1, using just 4-5.5GB VRAM on Mistral-7B-OpenOrca-AWQ. Native support for Llama\u002FMistral; extend to others manually. Create ",[4434,4625,4626],{},"AwqConfig(fuse_max_seq_len=2048, do_fuse=True, version=\"gemm\")","—",[4434,4629,4630],{},"fuse_max_seq_len"," covers context + generation (oversize safely). Pass to ",[4434,4633,4634],{},"from_pretrained(..., quantization_config=AwqConfig(...))",". Benchmarks show fused wins peak at mid-lengths (e.g., 512: prefill 3184→2848, decode 31→97 tokens\u002Fs), but VRAM rises slightly at long contexts (4GB → 5.57GB at 2048). optimum-benchmark graphs confirm fused generate throughput doubles vs. unfused up to batch=8. Can't combine with FlashAttention2—pick based on your seq_len\u002Fbatch needs.",[4637,4638,4639,4658],"table",{},[4640,4641,4642],"thead",{},[4643,4644,4645,4649,4652,4655],"tr",{},[4646,4647,4648],"th",{},"Prefill Length",[4646,4650,4651],{},"Unfused Prefill\u002FDecode (tokens\u002Fs)",[4646,4653,4654],{},"Fused Prefill\u002FDecode (tokens\u002Fs)",[4646,4656,4657],{},"VRAM Savings",[4659,4660,4661],"tbody",{},[4643,4662,4663,4667,4670,4673],{},[4664,4665,4666],"td",{},"2048",[4664,4668,4669],{},"2927 \u002F 35",[4664,4671,4672],{},"2715 \u002F 89",[4664,4674,4675],{},"~0.16GB",[17,4677,4679],{"id":4678},"exllamav2-kernels-for-amdextreme-speed","ExLlamaV2 Kernels for AMD\u002FExtreme Speed",[22,4681,4682,4683,4686],{},"For fastest prefill\u002Fdecode, install autoawq with ExLlamaV2 support and set ",[4434,4684,4685],{},"AwqConfig(version=\"exllama\")",". These kernels excel on AMD GPUs, outperforming standard AWQ on long contexts. Supports fused modules too. Trade-off: ExLlamaV2 ties you to autoawq ecosystem, less flexible than pure Transformers.",{"title":46,"searchDepth":47,"depth":47,"links":4688},[4689,4690,4691],{"id":4592,"depth":47,"text":4593},{"id":4619,"depth":47,"text":4620},{"id":4678,"depth":47,"text":4679},[53],{"content_references":4694,"triage":4716},[4695,4698,4701,4704,4707,4710,4713],{"type":60,"title":4696,"url":4697,"context":4483},"Activation-aware Weight Quantization (AWQ)","https:\u002F\u002Fhf.co\u002Fpapers\u002F2306.00978",{"type":75,"title":4699,"url":4700,"context":62},"llm-awq","https:\u002F\u002Fgithub.com\u002Fmit-han-lab\u002Fllm-awq",{"type":75,"title":4702,"url":4703,"context":62},"autoawq","https:\u002F\u002Fgithub.com\u002Fcasper-hansen\u002FAutoAWQ",{"type":75,"title":4705,"url":4706,"context":62},"optimum-intel","https:\u002F\u002Fhuggingface.co\u002Fdocs\u002Foptimum\u002Fmain\u002Fen\u002Fintel\u002Foptimization_inc",{"type":75,"title":4708,"url":4709,"context":62},"ExLlamaV2","https:\u002F\u002Fgithub.com\u002Fturboderp\u002Fexllamav2",{"type":75,"title":4711,"url":4712,"context":62},"optimum-benchmark","https:\u002F\u002Fgithub.com\u002Fhuggingface\u002Foptimum-benchmark",{"type":64,"title":4714,"url":4715,"context":67},"AWQ demo notebook","https:\u002F\u002Fcolab.research.google.com\u002Fdrive\u002F1HzZH89yAXJaZgwJDhQj9LqSBux932BvY#scrollTo=Wwsg6nCwoThm",{"relevance":79,"novelty":80,"quality":80,"actionability":79,"composite":81,"reasoning":4717},"Category: AI & LLMs. The article provides a detailed guide on using AWQ quantization for LLMs, addressing practical implementation steps that are highly relevant for developers looking to optimize AI models. It includes specific code snippets and performance benchmarks, making it immediately actionable for the target audience.","\u002Fsummaries\u002Fload-4-bit-awq-llms-in-transformers-for-low-memory-summary","2026-04-16 03:08:27",{"title":4582,"description":46},{"loc":4718},"5db8bfac0c40dc1f","https:\u002F\u002Fhuggingface.co\u002Fdocs\u002Ftransformers\u002Fquantization\u002Fawq","summaries\u002Fload-4-bit-awq-llms-in-transformers-for-low-memory-summary",[95,4726,96,97],"python","AWQ quantizes LLMs to 4-bits by preserving key weights, loadable via autoawq in Transformers; fused modules boost prefill\u002Fdecode speeds 2x with 4-5GB VRAM at batch=1.",[97],"m9PCVjMB3L6viVQiol3KzU9aP3MslYtqlss7tvnAomk",{"id":4731,"title":4732,"ai":4733,"body":4738,"categories":5015,"created_at":54,"date_modified":54,"description":46,"extension":55,"faq":54,"featured":56,"kicker_label":54,"meta":5016,"navigation":83,"path":5032,"published_at":54,"question":54,"scraped_at":5033,"seo":5034,"sitemap":5035,"source_id":5036,"source_name":5037,"source_type":91,"source_url":5038,"stem":5039,"tags":5040,"thumbnail_url":54,"tldr":5041,"unknown_tags":5042,"__hash__":5043},"summaries\u002Fsummaries\u002Fllm-0-32a0-messages-and-typed-streaming-for-llms-summary.md","LLM 0.32a0: Messages and Typed Streaming for LLMs",{"provider":7,"model":8,"input_tokens":4734,"output_tokens":4735,"processing_time_ms":4736,"cost_usd":4737},6641,1874,19176,0.00175835,{"type":14,"value":4739,"toc":5010},[4740,4744,4763,4766,4791,4794,4797,4833,4844,4848,4859,4862,4944,4962,4969,4976,4980,4999,5006],[17,4741,4743],{"id":4742},"message-sequences-replace-prompt-for-conversations","Message Sequences Replace Prompt for Conversations",[22,4745,4746,4747,4750,4751,4754,4755,4758,4759,4762],{},"Build conversations by passing lists of ",[4434,4748,4749],{},"llm.user()"," and ",[4434,4752,4753],{},"llm.assistant()"," messages to ",[4434,4756,4757],{},"model.prompt(messages=...)",", enabling you to preload prior exchanges without SQLite hacks. Old ",[4434,4760,4761],{},"prompt=\"text\""," still works—it converts to a single user message internally.",[22,4764,4765],{},"Before:",[4767,4768,4771],"pre",{"className":4769,"code":4770,"language":4726,"meta":46,"style":46},"language-python shiki shiki-themes github-light github-dark","conversation = model.conversation()\nr1 = conversation.prompt(\"Capital of France?\")  # \"Paris\"\nr2 = conversation.prompt(\"Germany?\")  # \"Berlin\"\n",[4434,4772,4773,4781,4786],{"__ignoreMap":46},[4774,4775,4778],"span",{"class":4776,"line":4777},"line",1,[4774,4779,4780],{},"conversation = model.conversation()\n",[4774,4782,4783],{"class":4776,"line":47},[4774,4784,4785],{},"r1 = conversation.prompt(\"Capital of France?\")  # \"Paris\"\n",[4774,4787,4788],{"class":4776,"line":4566},[4774,4789,4790],{},"r2 = conversation.prompt(\"Germany?\")  # \"Berlin\"\n",[22,4792,4793],{},"This couldn't ingest external histories easily.",[22,4795,4796],{},"Now:",[4767,4798,4800],{"className":4769,"code":4799,"language":4726,"meta":46,"style":46},"response = model.prompt([\n    llm.user(\"Capital of France?\"),\n    llm.assistant(\"Paris\"),\n    llm.user(\"Germany?\")\n])\nprint(response.text)  # \"Berlin\"\n",[4434,4801,4802,4807,4812,4817,4822,4827],{"__ignoreMap":46},[4774,4803,4804],{"class":4776,"line":4777},[4774,4805,4806],{},"response = model.prompt([\n",[4774,4808,4809],{"class":4776,"line":47},[4774,4810,4811],{},"    llm.user(\"Capital of France?\"),\n",[4774,4813,4814],{"class":4776,"line":4566},[4774,4815,4816],{},"    llm.assistant(\"Paris\"),\n",[4774,4818,4819],{"class":4776,"line":80},[4774,4820,4821],{},"    llm.user(\"Germany?\")\n",[4774,4823,4824],{"class":4776,"line":79},[4774,4825,4826],{},"])\n",[4774,4828,4830],{"class":4776,"line":4829},6,[4774,4831,4832],{},"print(response.text)  # \"Berlin\"\n",[22,4834,4835,4836,4839,4840,4843],{},"Or chain with ",[4434,4837,4838],{},"response.reply(\"Hungary?\")"," to extend naturally. This mirrors OpenAI's chat completions API ",[4434,4841,4842],{},"messages"," array, simplifying emulations and multi-turn flows across 1000+ models via plugins.",[17,4845,4847],{"id":4846},"typed-streaming-handles-mixed-response-parts","Typed Streaming Handles Mixed Response Parts",[22,4849,4850,4851,4854,4855,4858],{},"Iterate ",[4434,4852,4853],{},"response.stream_events"," (sync) or ",[4434,4856,4857],{},"astream_events"," (async) to process text, tool calls, reasoning, images, or audio as they arrive—crucial for models like Claude that interleave reasoning before tools.",[22,4860,4861],{},"Example with tool:",[4767,4863,4865],{"className":4769,"code":4864,"language":4726,"meta":46,"style":46},"def describe_dog(name: str, bio: str) -> str:\n    return f\"{name}: {bio}\"\n\nresponse = model.prompt(\n    \"Invent 3 cool dogs, first talk about your motivations\",\n    tools=[describe_dog]\n)\nfor event in response.stream_events:\n    if event.type == \"text\":\n        print(event.chunk, end=\"\", flush=True)\n    elif event.type == \"tool_call_name\":\n        print(f\"\\nTool call: {event.chunk}(\", end=\"\", flush=True)\n    elif event.type == \"tool_call_args\":\n        print(event.chunk, end=\"\", flush=True)\n",[4434,4866,4867,4872,4877,4882,4887,4892,4897,4903,4909,4915,4921,4927,4933,4939],{"__ignoreMap":46},[4774,4868,4869],{"class":4776,"line":4777},[4774,4870,4871],{},"def describe_dog(name: str, bio: str) -> str:\n",[4774,4873,4874],{"class":4776,"line":47},[4774,4875,4876],{},"    return f\"{name}: {bio}\"\n",[4774,4878,4879],{"class":4776,"line":4566},[4774,4880,4881],{"emptyLinePlaceholder":83},"\n",[4774,4883,4884],{"class":4776,"line":80},[4774,4885,4886],{},"response = model.prompt(\n",[4774,4888,4889],{"class":4776,"line":79},[4774,4890,4891],{},"    \"Invent 3 cool dogs, first talk about your motivations\",\n",[4774,4893,4894],{"class":4776,"line":4829},[4774,4895,4896],{},"    tools=[describe_dog]\n",[4774,4898,4900],{"class":4776,"line":4899},7,[4774,4901,4902],{},")\n",[4774,4904,4906],{"class":4776,"line":4905},8,[4774,4907,4908],{},"for event in response.stream_events:\n",[4774,4910,4912],{"class":4776,"line":4911},9,[4774,4913,4914],{},"    if event.type == \"text\":\n",[4774,4916,4918],{"class":4776,"line":4917},10,[4774,4919,4920],{},"        print(event.chunk, end=\"\", flush=True)\n",[4774,4922,4924],{"class":4776,"line":4923},11,[4774,4925,4926],{},"    elif event.type == \"tool_call_name\":\n",[4774,4928,4930],{"class":4776,"line":4929},12,[4774,4931,4932],{},"        print(f\"\\nTool call: {event.chunk}(\", end=\"\", flush=True)\n",[4774,4934,4936],{"class":4776,"line":4935},13,[4774,4937,4938],{},"    elif event.type == \"tool_call_args\":\n",[4774,4940,4942],{"class":4776,"line":4941},14,[4774,4943,4920],{},[22,4945,4946,4947,4950,4951,4954,4955,4441,4958,4961],{},"Output shows motivations as text, then three ",[4434,4948,4949],{},"describe_dog"," calls with JSON args like ",[4434,4952,4953],{},"{\"name\": \"Nova Jetpaw\", \"bio\": \"...\"}",". Post-stream, run ",[4434,4956,4957],{},"response.execute_tool_calls()",[4434,4959,4960],{},"response.reply(\"Tell me about the dogs\")"," to loop tools back to the model.",[22,4963,4964,4965,4968],{},"CLI gains ",[4434,4966,4967],{},"-R\u002F--no-reasoning"," to suppress thinking tokens (to stderr, colored differently). Supports server-side tools like OpenAI code interpreter or Anthropic web search, plus emerging multimodal outputs.",[22,4970,4971,4972,4975],{},"Trade-off: More granular than old ",[4434,4973,4974],{},"for chunk in response",", but unlocks tool\u002Freasoning parsing without custom plugins.",[17,4977,4979],{"id":4978},"serialize-responses-for-custom-storage","Serialize Responses for Custom Storage",[22,4981,4982,4983,4986,4987,4990,4991,4994,4995,4998],{},"Convert any ",[4434,4984,4985],{},"response"," to JSON via ",[4434,4988,4989],{},"response.to_dict()"," (a ",[4434,4992,4993],{},"TypedDict","), store anywhere, then reconstruct with ",[4434,4996,4997],{},"Response.from_dict(serializable)",". Replaces rigid SQLite conversation persistence, letting you build pluggable backends.",[22,5000,5001,5002,5005],{},"Future: Graph-based SQLite logging for deduplicated chat histories (0.32 or 0.33). Alpha tests plugins like ",[4434,5003,5004],{},"llm-anthropic"," for Claude Sonnet 4.6 streaming.",[5007,5008,5009],"style",{},"html .default .shiki span {color: var(--shiki-default);background: var(--shiki-default-bg);font-style: var(--shiki-default-font-style);font-weight: var(--shiki-default-font-weight);text-decoration: var(--shiki-default-text-decoration);}html .shiki span {color: var(--shiki-default);background: var(--shiki-default-bg);font-style: var(--shiki-default-font-style);font-weight: var(--shiki-default-font-weight);text-decoration: var(--shiki-default-text-decoration);}html .dark .shiki span {color: var(--shiki-dark);background: var(--shiki-dark-bg);font-style: var(--shiki-dark-font-style);font-weight: var(--shiki-dark-font-weight);text-decoration: var(--shiki-dark-text-decoration);}html.dark .shiki span {color: var(--shiki-dark);background: var(--shiki-dark-bg);font-style: var(--shiki-dark-font-style);font-weight: var(--shiki-dark-font-weight);text-decoration: var(--shiki-dark-text-decoration);}",{"title":46,"searchDepth":47,"depth":47,"links":5011},[5012,5013,5014],{"id":4742,"depth":47,"text":4743},{"id":4846,"depth":47,"text":4847},{"id":4978,"depth":47,"text":4979},[],{"content_references":5017,"triage":5029},[5018,5020,5023,5026],{"type":75,"title":5004,"url":5019,"context":62},"https:\u002F\u002Fgithub.com\u002Fsimonw\u002Fllm-anthropic",{"type":75,"title":5021,"url":5022,"context":62},"code interpreter tool","https:\u002F\u002Fdevelopers.openai.com\u002Fapi\u002Fdocs\u002Fguides\u002Ftools-code-interpreter?lang=curl",{"type":75,"title":5024,"url":5025,"context":62},"web search tool","https:\u002F\u002Fplatform.claude.com\u002Fdocs\u002Fen\u002Fagents-and-tools\u002Ftool-use\u002Fweb-search-tool",{"type":64,"title":5027,"url":5028,"context":62},"LLM changelog","https:\u002F\u002Fllm.datasette.io\u002Fen\u002Flatest\u002Fchangelog.html#a0-2026-04-28",{"relevance":80,"novelty":4566,"quality":80,"actionability":80,"composite":5030,"reasoning":5031},3.8,"Category: AI & LLMs. The article provides a detailed overview of new features in LLM 0.32a0 that enhance conversation handling and typed streaming, addressing practical applications for developers integrating LLMs into their products. It includes concrete code examples that demonstrate how to implement these features, making it actionable for the target audience.","\u002Fsummaries\u002Fllm-0-32a0-messages-and-typed-streaming-for-llms-summary","2026-05-03 17:01:57",{"title":4732,"description":46},{"loc":5032},"faa30cdf115bba54","Simon Willison's Weblog","https:\u002F\u002Fsimonwillison.net\u002F2026\u002FApr\u002F29\u002Fllm\u002F#atom-everything","summaries\u002Fllm-0-32a0-messages-and-typed-streaming-for-llms-summary",[95,4726,96,97],"LLM 0.32a0 refactors inputs to message sequences and outputs to typed streaming parts, handling conversations, tools, and multimodal content backwards-compatibly without breaking existing prompt APIs.",[97],"PjhNrL0OG9aD-ZqVp2pOb1YXR28lGP74IjSabPFME9c"]