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Gemma 4 E4B (4-bit) completed a full repo audit by executing Bash code and tool calls locally. Runs on just 6GB RAM.

Unsloth AI

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150,187 次观看 • 5 个月前 •via X (Twitter)

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Gemma 4 E4B was able to search and cite 10+ websites, execute code to find the best answer! 🔥 You only need 6GB RAM to try this in Unsloth Studio. GitHub repo:

Gemma 4 E4B was able to search and cite 10+ websites, execute code to find the best answer! 🔥 You only need 6GB RAM to try this in Unsloth Studio. GitHub repo:

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Gemma 4 looks at a parking lot. Decides what to ask. Calls SAM 3.1. "Segment all vehicles." 64 found. "Now just the white ones." 23 found. One model reasoning and orchestrating. One model executing. Both running locally on a MacBook. MLX. No cloud. No API.

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Qwen3.5-4B searched 20+ websites, cited its sources, and found the best answer! 🔥 Try this locally with just 4GB RAM via Unsloth Studio. The 4B model did this by executing tool calls + web search directly during its thinking trace.

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returning to deepdream in the times of LLMs here I am using gemma 4 e4b, starting from a random image and optimizing its pixels to maximise the probability gemma captions them as a fantasy map with mountains, rivers, and castles actual AI art 😅

returning to deepdream in the times of LLMs here I am using gemma 4 e4b, starting from a random image and optimizing its pixels to maximise the probability gemma captions them as a fantasy map with mountains, rivers, and castles actual AI art 😅

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Omg.. this is wild... This Github repo removes LLM censorship permanently in 45 minutes. It's called Heretic - 100% Open Source. No jailbreaks, prompts, and just One command. ↳ Zero configuration ↳ Keeps model intelligence intact ↳ Works with Llama, Qwen, Gemma ↳ Runs locally But it won't stay under the radar forever.

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As promised, instead of for $15,000 GPUs, we max optimized a model that can fit comfortably in any GPU with 8GB VRAM running Windows 200 tok/s, *128k context, text/image/audio. Anyone who wants to join the local AI community with 5 year old laptop can run this with recipe below, and this test was done on 3070ti laptop I forgot I had. We pulled every instruct model released since May that fits 8 or 12 GB, and ran the six that mattered through the same 20 tasks: JSON extraction, code, a table, arithmetic, a fake-treaty honesty check, tool calls, language, a persona, a fact planted deep in a long prompt, and three photos. Gemma 4 E4B, Gemma 4 12B, Qwen3.5-9B, Qwen3.5-4B, Ornith-1.5-9B, the Qwen3.8-9B distill. E4B is not the top scorer. Qwen3.5-9B and Ornith beat it on long context and on reading fine print in a photo. It won the pick for the things a newcomer on an old card actually needs: Apache 2.0, text + image + audio in, 3 GB of headroom on an 8 GB card, Weights Google trained to be 4-bit (QAT), and Google's own speculative drafter shipped next to them. Nothing else in the tier has all five, and the drafter alone is worth 2–3×.

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Meet Gemma 3n, a model that runs on as little as 2GB of RAM 🤯 It shares the same architecture as Gemini Nano, and is engineered for incredible performance. We added audio understanding, so now it’s multimodal, fast and lean, and runs on-device (no cloud connection required!)

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228,990 次观看 • 1 年前

gemma-4-12B-agentic-fable5-composer2.5 V2 is out. the agentic upgrade to the model trained on Fable 5's reasoning. Running it now with TurboQuant llama.cpp on a single RTX 4060( 8 GB VRAM) at 30 tokens/second with full 25000 context and reasoning: # The benchmarks v2 is built for coding + agentic work. writing code, running commands, using tools, debugging, multi step technical tasks. The clearest signal is tau2 bench telecom, an agentic tool use benchmark whose diagnose → fix → verify loop mirrors real terminal/debugging work: tau2 bench telecom numbers: base Gemma 4 12B: ~15% this finetune: ~55%. (Self reported) thats a huge jump # TheTom/llama-cpp-turboquant flags: llama-server.exe -m gemma4-v2-Q4_K_M.gguf -ngl 99 -c 25000 --cache-type-k q8_0 --cache-type-v turbo3 --port 8080 Flag breakdown: -ngl 99 → full GPU offload -c 25000 → 25K context --cache-type-k q8_0 --cache-type-v turbo3 → mixed-precision KV cache — K at 8-bit, V at ~3-bit via TurboQuant (Walsh Hadamard rotated polar quant, Google's own KV-compression research). Not even merged into mainline llama.cpp. running it off a fork. No API. No cloud. Just llama.cpp. well, a fork of it and any 6gb+ GPU. If you tried yuxinlu1/gemma-4-12B-coder-fable5-composer2.5-v1-GGUF, check this out and share your experience with the models

gemma-4-12B-agentic-fable5-composer2.5 V2 is out. the agentic upgrade to the model trained on Fable 5's reasoning. Running it now with TurboQuant llama.cpp on a single RTX 4060( 8 GB VRAM) at 30 tokens/second with full 25000 context and reasoning: # The benchmarks v2 is built for coding + agentic work. writing code, running commands, using tools, debugging, multi step technical tasks. The clearest signal is tau2 bench telecom, an agentic tool use benchmark whose diagnose → fix → verify loop mirrors real terminal/debugging work: tau2 bench telecom numbers: base Gemma 4 12B: ~15% this finetune: ~55%. (Self reported) thats a huge jump # TheTom/llama-cpp-turboquant flags: llama-server.exe -m gemma4-v2-Q4_K_M.gguf -ngl 99 -c 25000 --cache-type-k q8_0 --cache-type-v turbo3 --port 8080 Flag breakdown: -ngl 99 → full GPU offload -c 25000 → 25K context --cache-type-k q8_0 --cache-type-v turbo3 → mixed-precision KV cache — K at 8-bit, V at ~3-bit via TurboQuant (Walsh Hadamard rotated polar quant, Google's own KV-compression research). Not even merged into mainline llama.cpp. running it off a fork. No API. No cloud. Just llama.cpp. well, a fork of it and any 6gb+ GPU. If you tried yuxinlu1/gemma-4-12B-coder-fable5-composer2.5-v1-GGUF, check this out and share your experience with the models

Alok

146,046 次观看 • 3 个月前

SOMEONE JUST BUILT A FREE OPEN-SOURCE TOOL THAT TURNS GPT-6 ASTRA OR ANY AI MODEL INTO A WORKFLOW BOT. IT RUNS LOCALLY, SUPPORTS 1,200 INTEGRATIONS AND PUTS A HUMAN APPROVAL GATE BEFORE EVERY EXTERNAL ACTION.

SOMEONE JUST BUILT A FREE OPEN-SOURCE TOOL THAT TURNS GPT-6 ASTRA OR ANY AI MODEL INTO A WORKFLOW BOT. IT RUNS LOCALLY, SUPPORTS 1,200 INTEGRATIONS AND PUTS A HUMAN APPROVAL GATE BEFORE EVERY EXTERNAL ACTION.

0xMarioNawfal

52,128 次观看 • 11 天前

Tried Gemma 4 ran locally on my iPhone today I thought it'd be useful in case the apocalypse happens and I need to ask it for survival tips Like how to make a fire 🔥 I guess I'll freeze to death instead 🫠

Tried Gemma 4 ran locally on my iPhone today I thought it'd be useful in case the apocalypse happens and I need to ask it for survival tips Like how to make a fire 🔥 I guess I'll freeze to death instead 🫠

@levelsio

614,736 次观看 • 5 个月前

Holy sh*t, this is f**king insane😳 i cancelled my higgsfield subscription for this a free repo with 7.9K stars dropped a full AI video studio that runs on your pc it runs on 6gb of vram, even old gpus wan 2.2, ltx-2, hunyuan video and flux built in no uploads, no subscriptions, no watermarks here is how you set it up: 1. git clone the repo 2. run the one-click install script 3. launch it and generate in your browser you will not find a FREE way to make AI Videos this year than this

Holy sh*t, this is f**king insane😳 i cancelled my higgsfield subscription for this a free repo with 7.9K stars dropped a full AI video studio that runs on your pc it runs on 6gb of vram, even old gpus wan 2.2, ltx-2, hunyuan video and flux built in no uploads, no subscriptions, no watermarks here is how you set it up: 1. git clone the repo 2. run the one-click install script 3. launch it and generate in your browser you will not find a FREE way to make AI Videos this year than this

painn

194,100 次观看 • 1 个月前