[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"summary-5db8bfac0c40dc1f-load-4-bit-awq-llms-in-transformers-for-low-memory-summary":3,"summaries-facets-categories":180,"summary-related-5db8bfac0c40dc1f-load-4-bit-awq-llms-in-transformers-for-low-memory-summary":3749},{"id":4,"title":5,"ai":6,"body":13,"categories":123,"created_at":125,"date_modified":125,"description":117,"extension":126,"faq":125,"featured":127,"kicker_label":125,"meta":128,"navigation":162,"path":163,"published_at":125,"question":125,"scraped_at":164,"seo":165,"sitemap":166,"source_id":167,"source_name":168,"source_type":169,"source_url":170,"stem":171,"tags":172,"thumbnail_url":125,"tldr":177,"tweet":125,"unknown_tags":178,"__hash__":179},"summaries\u002Fsummaries\u002F5db8bfac0c40dc1f-load-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":9,"output_tokens":10,"processing_time_ms":11,"cost_usd":12},"openrouter","x-ai\u002Fgrok-4.1-fast",5033,1989,8133,0.00149425,{"type":14,"value":15,"toc":116},"minimark",[16,21,46,50,65,105,109],[17,18,20],"h2",{"id":19},"load-awq-quantized-models-with-one-line","Load AWQ-Quantized Models with One Line",[22,23,24,25,29,30,33,34,37,38,41,42,45],"p",{},"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 ",[26,27,28],"code",{},"quant_method: \"awq\""," in their config.json. Install autoawq (which pins Transformers to v4.47.1—reinstall Transformers after for compatibility), then load with ",[26,31,32],{},"AutoModelForCausalLM.from_pretrained(model_id, quant_method=\"awq\")",". This auto-converts non-quantized weights (e.g., embeddings) to fp16 for speed; override via ",[26,35,36],{},"dtype=torch.bfloat16",". Move to GPU with ",[26,39,40],{},"device_map=\"auto\""," or CPU otherwise. Add ",[26,43,44],{},"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.",[17,47,49],{"id":48},"fused-modules-double-prefilldecode-throughput","Fused Modules Double Prefill\u002FDecode Throughput",[22,51,52,53,56,57,60,61,64],{},"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 ",[26,54,55],{},"AwqConfig(fuse_max_seq_len=2048, do_fuse=True, version=\"gemm\")","—",[26,58,59],{},"fuse_max_seq_len"," covers context + generation (oversize safely). Pass to ",[26,62,63],{},"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.",[66,67,68,87],"table",{},[69,70,71],"thead",{},[72,73,74,78,81,84],"tr",{},[75,76,77],"th",{},"Prefill Length",[75,79,80],{},"Unfused Prefill\u002FDecode (tokens\u002Fs)",[75,82,83],{},"Fused Prefill\u002FDecode (tokens\u002Fs)",[75,85,86],{},"VRAM Savings",[88,89,90],"tbody",{},[72,91,92,96,99,102],{},[93,94,95],"td",{},"2048",[93,97,98],{},"2927 \u002F 35",[93,100,101],{},"2715 \u002F 89",[93,103,104],{},"~0.16GB",[17,106,108],{"id":107},"exllamav2-kernels-for-amdextreme-speed","ExLlamaV2 Kernels for AMD\u002FExtreme Speed",[22,110,111,112,115],{},"For fastest prefill\u002Fdecode, install autoawq with ExLlamaV2 support and set ",[26,113,114],{},"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":117,"searchDepth":118,"depth":118,"links":119},"",2,[120,121,122],{"id":19,"depth":118,"text":20},{"id":48,"depth":118,"text":49},{"id":107,"depth":118,"text":108},[124],"AI & LLMs",null,"md",false,{"content_references":129,"triage":157},[130,135,140,143,146,149,152],{"type":131,"title":132,"url":133,"context":134},"paper","Activation-aware Weight Quantization (AWQ)","https:\u002F\u002Fhf.co\u002Fpapers\u002F2306.00978","cited",{"type":136,"title":137,"url":138,"context":139},"tool","llm-awq","https:\u002F\u002Fgithub.com\u002Fmit-han-lab\u002Fllm-awq","mentioned",{"type":136,"title":141,"url":142,"context":139},"autoawq","https:\u002F\u002Fgithub.com\u002Fcasper-hansen\u002FAutoAWQ",{"type":136,"title":144,"url":145,"context":139},"optimum-intel","https:\u002F\u002Fhuggingface.co\u002Fdocs\u002Foptimum\u002Fmain\u002Fen\u002Fintel\u002Foptimization_inc",{"type":136,"title":147,"url":148,"context":139},"ExLlamaV2","https:\u002F\u002Fgithub.com\u002Fturboderp\u002Fexllamav2",{"type":136,"title":150,"url":151,"context":139},"optimum-benchmark","https:\u002F\u002Fgithub.com\u002Fhuggingface\u002Foptimum-benchmark",{"type":153,"title":154,"url":155,"context":156},"other","AWQ demo notebook","https:\u002F\u002Fcolab.research.google.com\u002Fdrive\u002F1HzZH89yAXJaZgwJDhQj9LqSBux932BvY#scrollTo=Wwsg6nCwoThm","recommended",{"relevance":158,"novelty":159,"quality":159,"actionability":158,"composite":160,"reasoning":161},5,4,4.55,"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.",true,"\u002Fsummaries\u002F5db8bfac0c40dc1f-load-4-bit-awq-llms-in-transformers-for-low-memory-summary","2026-04-16 03:08:27",{"title":5,"description":117},{"loc":163},"5db8bfac0c40dc1f","__oneoff__","article","https:\u002F\u002Fhuggingface.co\u002Fdocs\u002Ftransformers\u002Fquantization\u002Fawq","summaries\u002F5db8bfac0c40dc1f-load-4-bit-awq-llms-in-transformers-for-low-memory-summary",[173,174,175,176],"llm","python","ai-tools","ai-llms","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 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0.32a0: Messages and Typed Streaming for LLMs",{"provider":7,"model":8,"input_tokens":3754,"output_tokens":3755,"processing_time_ms":3756,"cost_usd":3757},6641,1874,19176,0.00175835,{"type":14,"value":3759,"toc":4032},[3760,3764,3783,3786,3812,3815,3818,3854,3865,3869,3880,3883,3965,3984,3991,3998,4002,4021,4028],[17,3761,3763],{"id":3762},"message-sequences-replace-prompt-for-conversations","Message Sequences Replace Prompt for Conversations",[22,3765,3766,3767,3770,3771,3774,3775,3778,3779,3782],{},"Build conversations by passing lists of ",[26,3768,3769],{},"llm.user()"," and ",[26,3772,3773],{},"llm.assistant()"," messages to ",[26,3776,3777],{},"model.prompt(messages=...)",", enabling you to preload prior exchanges without SQLite hacks. Old ",[26,3780,3781],{},"prompt=\"text\""," still works—it converts to a single user message internally.",[22,3784,3785],{},"Before:",[3787,3788,3791],"pre",{"className":3789,"code":3790,"language":174,"meta":117,"style":117},"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",[26,3792,3793,3801,3806],{"__ignoreMap":117},[3794,3795,3798],"span",{"class":3796,"line":3797},"line",1,[3794,3799,3800],{},"conversation = model.conversation()\n",[3794,3802,3803],{"class":3796,"line":118},[3794,3804,3805],{},"r1 = conversation.prompt(\"Capital of France?\")  # \"Paris\"\n",[3794,3807,3809],{"class":3796,"line":3808},3,[3794,3810,3811],{},"r2 = conversation.prompt(\"Germany?\")  # \"Berlin\"\n",[22,3813,3814],{},"This couldn't ingest external histories easily.",[22,3816,3817],{},"Now:",[3787,3819,3821],{"className":3789,"code":3820,"language":174,"meta":117,"style":117},"response = model.prompt([\n    llm.user(\"Capital of France?\"),\n    llm.assistant(\"Paris\"),\n    llm.user(\"Germany?\")\n])\nprint(response.text)  # \"Berlin\"\n",[26,3822,3823,3828,3833,3838,3843,3848],{"__ignoreMap":117},[3794,3824,3825],{"class":3796,"line":3797},[3794,3826,3827],{},"response = model.prompt([\n",[3794,3829,3830],{"class":3796,"line":118},[3794,3831,3832],{},"    llm.user(\"Capital of France?\"),\n",[3794,3834,3835],{"class":3796,"line":3808},[3794,3836,3837],{},"    llm.assistant(\"Paris\"),\n",[3794,3839,3840],{"class":3796,"line":159},[3794,3841,3842],{},"    llm.user(\"Germany?\")\n",[3794,3844,3845],{"class":3796,"line":158},[3794,3846,3847],{},"])\n",[3794,3849,3851],{"class":3796,"line":3850},6,[3794,3852,3853],{},"print(response.text)  # \"Berlin\"\n",[22,3855,3856,3857,3860,3861,3864],{},"Or chain with ",[26,3858,3859],{},"response.reply(\"Hungary?\")"," to extend naturally. This mirrors OpenAI's chat completions API ",[26,3862,3863],{},"messages"," array, simplifying emulations and multi-turn flows across 1000+ models via plugins.",[17,3866,3868],{"id":3867},"typed-streaming-handles-mixed-response-parts","Typed Streaming Handles Mixed Response Parts",[22,3870,3871,3872,3875,3876,3879],{},"Iterate ",[26,3873,3874],{},"response.stream_events"," (sync) or ",[26,3877,3878],{},"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.",[22,3881,3882],{},"Example with tool:",[3787,3884,3886],{"className":3789,"code":3885,"language":174,"meta":117,"style":117},"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",[26,3887,3888,3893,3898,3903,3908,3913,3918,3924,3930,3936,3942,3948,3954,3960],{"__ignoreMap":117},[3794,3889,3890],{"class":3796,"line":3797},[3794,3891,3892],{},"def describe_dog(name: str, bio: str) -> str:\n",[3794,3894,3895],{"class":3796,"line":118},[3794,3896,3897],{},"    return f\"{name}: {bio}\"\n",[3794,3899,3900],{"class":3796,"line":3808},[3794,3901,3902],{"emptyLinePlaceholder":162},"\n",[3794,3904,3905],{"class":3796,"line":159},[3794,3906,3907],{},"response = model.prompt(\n",[3794,3909,3910],{"class":3796,"line":158},[3794,3911,3912],{},"    \"Invent 3 cool dogs, first talk about your motivations\",\n",[3794,3914,3915],{"class":3796,"line":3850},[3794,3916,3917],{},"    tools=[describe_dog]\n",[3794,3919,3921],{"class":3796,"line":3920},7,[3794,3922,3923],{},")\n",[3794,3925,3927],{"class":3796,"line":3926},8,[3794,3928,3929],{},"for event in response.stream_events:\n",[3794,3931,3933],{"class":3796,"line":3932},9,[3794,3934,3935],{},"    if event.type == \"text\":\n",[3794,3937,3939],{"class":3796,"line":3938},10,[3794,3940,3941],{},"        print(event.chunk, end=\"\", flush=True)\n",[3794,3943,3945],{"class":3796,"line":3944},11,[3794,3946,3947],{},"    elif event.type == \"tool_call_name\":\n",[3794,3949,3951],{"class":3796,"line":3950},12,[3794,3952,3953],{},"        print(f\"\\nTool call: {event.chunk}(\", end=\"\", flush=True)\n",[3794,3955,3957],{"class":3796,"line":3956},13,[3794,3958,3959],{},"    elif event.type == \"tool_call_args\":\n",[3794,3961,3963],{"class":3796,"line":3962},14,[3794,3964,3941],{},[22,3966,3967,3968,3971,3972,3975,3976,3979,3980,3983],{},"Output shows motivations as text, then three ",[26,3969,3970],{},"describe_dog"," calls with JSON args like ",[26,3973,3974],{},"{\"name\": \"Nova Jetpaw\", \"bio\": \"...\"}",". Post-stream, run ",[26,3977,3978],{},"response.execute_tool_calls()"," or ",[26,3981,3982],{},"response.reply(\"Tell me about the dogs\")"," to loop tools back to the model.",[22,3985,3986,3987,3990],{},"CLI gains ",[26,3988,3989],{},"-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.",[22,3992,3993,3994,3997],{},"Trade-off: More granular than old ",[26,3995,3996],{},"for chunk in response",", but unlocks tool\u002Freasoning parsing without custom plugins.",[17,3999,4001],{"id":4000},"serialize-responses-for-custom-storage","Serialize Responses for Custom Storage",[22,4003,4004,4005,4008,4009,4012,4013,4016,4017,4020],{},"Convert any ",[26,4006,4007],{},"response"," to JSON via ",[26,4010,4011],{},"response.to_dict()"," (a ",[26,4014,4015],{},"TypedDict","), store anywhere, then reconstruct with ",[26,4018,4019],{},"Response.from_dict(serializable)",". Replaces rigid SQLite conversation persistence, letting you build pluggable backends.",[22,4022,4023,4024,4027],{},"Future: Graph-based SQLite logging for deduplicated chat histories (0.32 or 0.33). Alpha tests plugins like ",[26,4025,4026],{},"llm-anthropic"," for Claude Sonnet 4.6 streaming.",[4029,4030,4031],"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":117,"searchDepth":118,"depth":118,"links":4033},[4034,4035,4036],{"id":3762,"depth":118,"text":3763},{"id":3867,"depth":118,"text":3868},{"id":4000,"depth":118,"text":4001},[],{"content_references":4039,"triage":4051},[4040,4042,4045,4048],{"type":136,"title":4026,"url":4041,"context":139},"https:\u002F\u002Fgithub.com\u002Fsimonw\u002Fllm-anthropic",{"type":136,"title":4043,"url":4044,"context":139},"code interpreter tool","https:\u002F\u002Fdevelopers.openai.com\u002Fapi\u002Fdocs\u002Fguides\u002Ftools-code-interpreter?lang=curl",{"type":136,"title":4046,"url":4047,"context":139},"web search tool","https:\u002F\u002Fplatform.claude.com\u002Fdocs\u002Fen\u002Fagents-and-tools\u002Ftool-use\u002Fweb-search-tool",{"type":153,"title":4049,"url":4050,"context":139},"LLM changelog","https:\u002F\u002Fllm.datasette.io\u002Fen\u002Flatest\u002Fchangelog.html#a0-2026-04-28",{"relevance":159,"novelty":3808,"quality":159,"actionability":159,"composite":4052,"reasoning":4053},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\u002Ffaa30cdf115bba54-llm-0-32a0-messages-and-typed-streaming-for-llms-summary","2026-05-03 17:01:57",{"title":3752,"description":117},{"loc":4054},"faa30cdf115bba54","Simon Willison's Weblog","https:\u002F\u002Fsimonwillison.net\u002F2026\u002FApr\u002F29\u002Fllm\u002F#atom-everything","summaries\u002Ffaa30cdf115bba54-llm-0-32a0-messages-and-typed-streaming-for-llms-summary",[173,174,175,176],"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.",[176],"ACn8AVHQKOVKf_QGekiHPpk0fS_MwuQXzoHekL3TaCo",{"id":4067,"title":4068,"ai":4069,"body":4074,"categories":4108,"created_at":125,"date_modified":125,"description":117,"extension":126,"faq":125,"featured":127,"kicker_label":125,"meta":4109,"navigation":162,"path":4127,"published_at":4128,"question":125,"scraped_at":4129,"seo":4130,"sitemap":4131,"source_id":4132,"source_name":4133,"source_type":169,"source_url":4134,"stem":4135,"tags":4136,"thumbnail_url":125,"tldr":4137,"tweet":125,"unknown_tags":4138,"__hash__":4139},"summaries\u002Fsummaries\u002F5b4ad3bf4e788775-gemini-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":4070,"output_tokens":4071,"processing_time_ms":4072,"cost_usd":4073},5186,1609,16896,0.00133485,{"type":14,"value":4075,"toc":4103},[4076,4080,4083,4086,4090,4093,4096,4100],[17,4077,4079],{"id":4078},"build-multimodal-rag-in-minutes-with-file-search-store","Build Multimodal RAG in Minutes with File Search Store",[22,4081,4082],{},"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.",[22,4084,4085],{},"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.",[17,4087,4089],{"id":4088},"traditional-rags-heavy-lift-vs-file-search-simplicity","Traditional RAG's Heavy Lift vs File Search Simplicity",[22,4091,4092],{},"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.",[22,4094,4095],{},"Result: Developers who spent a year on pipelines can now prototype and ship multimodal RAG apps instantly, focusing on app logic over infra.",[17,4097,4099],{"id":4098},"trade-offs-sledgehammer-for-most-cases-not-universal","Trade-offs: Sledgehammer for Most Cases, Not Universal",[22,4101,4102],{},"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":117,"searchDepth":118,"depth":118,"links":4104},[4105,4106,4107],{"id":4078,"depth":118,"text":4079},{"id":4088,"depth":118,"text":4089},{"id":4098,"depth":118,"text":4099},[124],{"content_references":4110,"triage":4125},[4111,4113,4116,4119,4122],{"type":131,"title":4112,"context":139},"Attention Is All You Need",{"type":153,"title":4114,"url":4115,"context":156},"Gemini API File Search docs","https:\u002F\u002Fai.google.dev\u002Fgemini-api\u002Fdocs\u002Ffile-search",{"type":153,"title":4117,"url":4118,"context":139},"Gemini API File Search multimodal RAG announcement","https:\u002F\u002Fblog.google\u002Ftechnology\u002Fdevelopers\u002Fgemini-api-file-search-multimodal-rag\u002F",{"type":153,"title":4120,"url":4121,"context":156},"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":136,"title":4123,"url":4124,"context":156},"AI Studio sample app","https:\u002F\u002Fai.studio\u002Fapps\u002Facb0ca81-7130-43ae-a31f-bedd96d28294",{"relevance":158,"novelty":159,"quality":159,"actionability":158,"composite":160,"reasoning":4126},"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.","\u002Fsummaries\u002F5b4ad3bf4e788775-gemini-file-search-2-0-cuts-multimodal-rag-to-4-ap-summary","2026-05-07 14:00:00","2026-05-07 16:31:32",{"title":4068,"description":117},{"loc":4127},"e7802614eaf8f398","AI with Surya","https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=4n9Z-9YEtyY","summaries\u002F5b4ad3bf4e788775-gemini-file-search-2-0-cuts-multimodal-rag-to-4-ap-summary",[173,175,176],"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 minutes.",[176],"mF9QuWKXO41cOfADOZV9UpQ4ocie5JW1fEf-FoJx32E",{"id":4141,"title":4142,"ai":4143,"body":4148,"categories":4203,"created_at":125,"date_modified":125,"description":117,"extension":126,"faq":125,"featured":127,"kicker_label":125,"meta":4204,"navigation":162,"path":4231,"published_at":4232,"question":125,"scraped_at":4233,"seo":4234,"sitemap":4235,"source_id":4236,"source_name":4237,"source_type":169,"source_url":4238,"stem":4239,"tags":4240,"thumbnail_url":125,"tldr":4241,"tweet":125,"unknown_tags":4242,"__hash__":4243},"summaries\u002Fsummaries\u002F352a655761b08b28-35b-models-on-rtx-4090-turboquant-kv-compression-u-summary.md","35B Models on RTX 4090: TurboQuant KV Compression Unlocks 32K Context",{"provider":7,"model":8,"input_tokens":4144,"output_tokens":4145,"processing_time_ms":4146,"cost_usd":4147},6914,2052,13171,0.00190195,{"type":14,"value":4149,"toc":4197},[4150,4154,4157,4161,4164,4168,4175,4181,4187,4190,4194],[17,4151,4153],{"id":4152},"q4_k_m-quantization-delivers-90-95-quality-at-60-original-size","Q4_K_M Quantization Delivers 90-95% Quality at 60% Original Size",[22,4155,4156],{},"Q4_K_M GGUF compresses model weights to ~0.6 GB per billion parameters (7B → 4GB, 32B → 19GB, 70B → 40GB) by storing weights in 4 bits with K-quant block grouping and M-medium mixed precision for sensitive layers. This preserves 90-95% of FP16 accuracy, making it the default for local runs on HuggingFace\u002FOllama. Dense 35B models need 21-22GB VRAM for weights alone on RTX 4090 (24GB total), leaving ~2GB for KV cache—insufficient beyond short contexts. MoE 35B (e.g., Qwen2.5-35B-A3B) activates only 3B params\u002Ftoken, fitting in ~20GB with 1.2GB KV at 64K context due to fewer active heads, reducing TurboQuant's necessity.",[17,4158,4160],{"id":4159},"turboquant-stacks-on-weights-for-long-context-memory-wins","TurboQuant Stacks on Weights for Long-Context Memory Wins",[22,4162,4163],{},"TurboQuant compresses KV cache to 2-4 bits at inference (PolarQuant + QJL, e.g., bits=3) without touching weights, enabling dense models like Mistral Small 3.1 24B or Qwen2.5-32B (64 layers, 8 GQA heads, head_dim=128) to handle 32K context on 24GB VRAM. Formula: 2 × layers × heads × head_dim × seq_len × bytes\u002Felement. Without it, 16K context KV hits ~4GB (total ~24GB borderline); with turbo3, drops to ~1.2GB, freeing space for 32K (~2.4GB). Fused Triton kernels compute attention on compressed KV, speeding up >8K contexts (major at 32K+). Asymmetric K@3bits\u002FV@2bits saves more with zero quality loss empirically.",[17,4165,4167],{"id":4166},"three-paths-to-turboquant-on-24gb-gpus-today","Three Paths to TurboQuant on 24GB GPUs Today",[22,4169,4170,4174],{},[4171,4172,4173],"strong",{},"PyPI turboquant-kv",": Wrap HF Transformers (load_in_4bit) with TurboQuantModel(bits=3).enable_decoder_fused_attention() for Python scripts; handles 512+ new tokens on long inputs.",[22,4176,4177,4180],{},[4171,4178,4179],{},"vLLM fork (0xSero\u002Fturboquant)",": install_turboquant_vllm(bits=3, head_dim=128) before LLM(model, gpu_memory_utilization=0.92); prebuilt codebooks for d=128\u002F256 at 2\u002F3\u002F4 bits; server-friendly.",[22,4182,4183,4186],{},[4171,4184,4185],{},"llama.cpp fork (turboquant_plus)",": Build with CUDA, run llama-server -m model-Q4_K_M.gguf --cache-type-k turbo3 --cache-type-v turbo2 -c 32768 -ngl 99. Turbo4 ≈ q8_0 quality, turbo3 best tradeoff, turbo2 extreme. Fits 32K on Qwen2.5-32B (19GB weights + \u003C4GB KV).",[22,4188,4189],{},"Quality holds ≥8B models; speedups context-dependent (\u003C2K: memory only). Experimental—await Google impl (Q2-Q3 2026), llama.cpp #20969, vLLM #38171 merges.",[17,4191,4193],{"id":4192},"optimal-stack-q4_k_m-gguf-turboquant_plus-turbo32","Optimal Stack: Q4_K_M GGUF + turboquant_plus turbo3\u002F2",[22,4195,4196],{},"Download Q4_K_M GGUF, use llama.cpp fork at 16-32K context. Achieves reliable 35B dense inference where defaults crash; 128K impossible (KV still GBs post-compression).",{"title":117,"searchDepth":118,"depth":118,"links":4198},[4199,4200,4201,4202],{"id":4152,"depth":118,"text":4153},{"id":4159,"depth":118,"text":4160},{"id":4166,"depth":118,"text":4167},{"id":4192,"depth":118,"text":4193},[124],{"content_references":4205,"triage":4228},[4206,4209,4211,4214,4216,4219,4222,4225],{"type":153,"title":4207,"url":4208,"context":139},"GGUF","https:\u002F\u002Fhuggingface.co\u002Fdocs\u002Fhub\u002Fgguf",{"type":153,"title":4210,"url":170,"context":139},"AWQ",{"type":153,"title":4212,"url":4213,"context":134},"VRAM Requirements for AI Models","https:\u002F\u002Fwillitrunai.com\u002Fblog\u002Fvram-requirements-for-ai-models",{"type":136,"title":4215,"context":156},"turboquant-kv",{"type":136,"title":4217,"url":4218,"context":156},"0xSero\u002Fturboquant","https:\u002F\u002Fgithub.com\u002F0xSero\u002Fturboquant.git",{"type":136,"title":4220,"url":4221,"context":156},"turboquant_plus","https:\u002F\u002Fgithub.com\u002FTheTom\u002Fturboquant_plus.git",{"type":153,"title":4223,"url":4224,"context":139},"llama.cpp discussion #20969","https:\u002F\u002Fgithub.com\u002Fggml-org\u002Fllama.cpp\u002Fdiscussions\u002F20969",{"type":153,"title":4226,"url":4227,"context":139},"vLLM issue #38171","https:\u002F\u002Fgithub.com\u002Fvllm-project\u002Fvllm\u002Fissues\u002F38171",{"relevance":158,"novelty":159,"quality":159,"actionability":159,"composite":4229,"reasoning":4230},4.35,"Category: AI & LLMs. The article provides in-depth technical insights on running large language models efficiently, addressing the pain point of integrating AI features into products. It offers specific implementation paths for using TurboQuant, which is actionable for developers looking to optimize AI model performance.","\u002Fsummaries\u002F352a655761b08b28-35b-models-on-rtx-4090-turboquant-kv-compression-u-summary","2026-04-15 12:31:01","2026-04-15 15:39:14",{"title":4142,"description":117},{"loc":4231},"352a655761b08b28","Towards AI","https:\u002F\u002Fpub.towardsai.net\u002Frunning-a-35b-model-locally-with-turboquant-whats-actually-possible-right-now-1ac5327430b0?source=rss----98111c9905da---4","summaries\u002F352a655761b08b28-35b-models-on-rtx-4090-turboquant-kv-compression-u-summary",[173,175,174],"Stack Q4_K_M weight quantization with TurboQuant's 3-bit KV cache compression to run dense 35B models at 32K context on 24GB VRAM, fitting weights (20GB) + KV cache (under 4GB) with room to spare—use llama.cpp forks today.",[],"sGRm9blteDamXpWHWdqmPUk1Seq5Nct_nBmywP1bFT0",{"id":4245,"title":4246,"ai":4247,"body":4252,"categories":4272,"created_at":125,"date_modified":125,"description":117,"extension":126,"faq":125,"featured":127,"kicker_label":125,"meta":4273,"navigation":162,"path":4274,"published_at":4275,"question":125,"scraped_at":125,"seo":4276,"sitemap":4277,"source_id":4278,"source_name":4279,"source_type":169,"source_url":4280,"stem":4281,"tags":4282,"thumbnail_url":125,"tldr":4283,"tweet":125,"unknown_tags":4284,"__hash__":4285},"summaries\u002Fsummaries\u002F50-line-rag-pipeline-chromadb-embeddings-anthropic-summary.md","50-Line RAG Pipeline: ChromaDB + Embeddings + Anthropic",{"provider":7,"model":8,"input_tokens":4248,"output_tokens":4249,"processing_time_ms":4250,"cost_usd":4251},3613,720,7647,0.00105995,{"type":14,"value":4253,"toc":4268},[4254,4258,4261,4265],[17,4255,4257],{"id":4256},"grasp-rag-by-building-and-running-it","Grasp RAG by Building and Running It",[22,4259,4260],{},"RAG (Retrieval-Augmented Generation) becomes intuitive not from diagrams but from executing code that queries unseen documents—like a paper the model never trained on—and gets accurate answers. Skip CRUD or Hello World; this 50-line pipeline is your essential first Python AI project for day-one production relevance. It demonstrates semantic search retrieving relevant chunks, then feeding them into an LLM via a tuned system prompt for grounded responses.",[17,4262,4264],{"id":4263},"core-mechanics-semantic-search-prompting","Core Mechanics: Semantic Search + Prompting",[22,4266,4267],{},"RAG relies on two elements: (1) semantic search via embeddings (using SentenceTransformers) stored in ChromaDB vector database for fast retrieval of contextually similar document chunks; (2) an effective system prompt that injects retrieved content into the LLM (Anthropic) to generate answers without hallucination. Provide your documents as input, embed them once, query semantically, and output synthesized responses—bypassing the LLM's static training data.",{"title":117,"searchDepth":118,"depth":118,"links":4269},[4270,4271],{"id":4256,"depth":118,"text":4257},{"id":4263,"depth":118,"text":4264},[124],{},"\u002Fsummaries\u002F50-line-rag-pipeline-chromadb-embeddings-anthropic-summary","2026-04-08 21:21:20",{"title":4246,"description":117},{"loc":4274},"6155f44abdae4444","Level Up Coding","https:\u002F\u002Funknown","summaries\u002F50-line-rag-pipeline-chromadb-embeddings-anthropic-summary",[174,173,176],"Build a working RAG system in Python using ChromaDB for storage, SentenceTransformers for semantic search embeddings, and Anthropic for generation—answers questions from unseen docs via retrieval + prompting.",[176],"BIJedy9i_JFeNsMJjn7eT_KsRnpCCYu15mqeltQb0d8"]