[Перевод] Локальный запуск GLM-5.1
Перевод подготовил автор канала Друг Опенсурса , приятного прочтения, заранее благодарю за подписку В этой статье мы подробно разберем процесс развертывания GLM-5.1 с использованием llama.cpp и форматов GGUF. Узнаем о системных требованиях, сборке и настройках, оптимизации и практическом применении.
https://habr.com/ru/articles/1022242/
#glm51 #llm #Llamacpp #Unsloth #GGUF #Локальный_запуск #tool_calling #Zai #искусственный_интеллект
Unsloth AI (@UnslothAI)
Google DeepMind이 Gemma 4 해커톤을 개최하고, Unsloth로 파인튜닝한 Gemma 4 모델을 선보이는 참가자에게 총 20만 달러 규모의 상금을 제공합니다. 특히 Unsloth 관련 1만 달러 특별상이 포함되어 있어 Gemma 4와 효율적 파인튜닝 도구에 관심 있는 개발자들에게 주목할 만한 행사입니다.
RT @dr_cintas: Sie können Gemma 4 jetzt komplett KOSTENLOS fine-tunen 🤯 Ohne GPU. Ohne Kreditkarte. Ohne Programmierkenntnisse. Nur ein Browser und über 500 Modelle zur Auswahl. → Öffnen Sie das Unsloth Colab-Notebook → Wählen Sie Ihr Modell + Datensatz → Klicken Sie auf Start Training Video
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RT @JoelDeTeves: Testing @DJLougen "Ornstein" 27B - Q4_K_M Harmonic (same author) already felt like the smartest Qwen3.5-27B variant I'd tested. However *THIS* model @ Q4 feels much more intelligent than it has any business being. Here is my layman's understanding of the difference (I might be completely wrong - I am not a neuroscientist): - Both draw from the same high-quality "premium" reasoning traces (exactly 799 premium examples in both cases). These traces are deep (~1,667 words on average), statistically validated, and engineered to include self-correction (100% of rows), verification, and exploration of alternatives. - However, the key difference is that Harmonic-27B uses *only* the 799 premium traces, whereas Ornstein-27B builds on those same 799 premium traces and *adds 430 curated degenerate traces* (total 1,229). In other words it deliberately includes examples of bad reasoning - loops, restating without progress, filler, superficial padding, so the model learns what effective thinking is *not*. Absolute mad science happening here. Follow @DJLougen for more! Speed: 31 tokens/second (good) Mmproj -> Unsloth/Qwen3.5-27B (image recognition tested and works great) VRAM usage: 21.6 GB Configuration: -m Ornstein-27B-Q4_K_M.gguf --mmproj mmproj-F16.gguf --n-gpu-layers 99 --ctx-size 262144 --cache-type-k turbo4 --cache-type-v turbo4 --fit on --jinja --reasoning-format auto --flash-attn on Using @spiritbuun TurboQuant fork of Llama.cpp - running @ max context to test the limits of this version and see where context rot starts to happen. Also worth follow! Next test: will it perform in…
#for #gguf #Llama #Llamacpp #max #science #the #Unsloth #arint_info
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RT @UnslothAI: You can now fine-tune Gemma 4 with our free notebooks! 🔥 You just need 8GB VRAM to train Gemma 4 locally! Unsloth trains Gemma4 1.5x faster with 50% less VRAM. GitHub: github.com/unslothai/unsloth Guide: unsloth.ai/docs/models/gemma… Gemma-4-E4B Colab: colab.research.google.com/gi…
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Paul Couvert (@itsPaulAi)
구글 Colab에서 무료로 Gemma 4를 포함한 500개 이상의 오픈소스 모델을 파인튜닝할 수 있게 되었으며, Unsloth Studio를 통해 간단한 절차로 학습을 시작할 수 있다는 소식입니다. 오픈소스 모델 학습 접근성을 크게 높인 업데이트입니다.
RT @mr_r0b0t: Hermes made itself an “unsloth model training” skillset and is currently training Gemma4. Good luck doing that with 🦞 Simony (@0xSimony) Hermes ou OpenClaw ? — https://nitter.net/0xSimony/status/2041095341630435808#m
RT @bnjmn_marie: Gemma 4 GGUF Evaluation: Unsloth's UD IQ3_XXS is my recommendation for the 31B You save 50 GB with almost no visible impact on accuracy. Gemma 4 is overall very robust to quantization (more than Qwen3.5 it seems, but I need more results to confirm). And no, I couldn't find that APEX versions are superior to Unsloth's UDs. For the same sizes, on the benchmarks I ran, Unsloth's GGUFs recover better the original accuracy. All my results here: kaitchup.substack.com/p/best…
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金のニワトリ (@gosrum)
Unsloth의 Gemma-4 GGUF가 업데이트되어 재다운로드 후 벤치마크를 돌리고 있다는 내용입니다. 로컬에서 최신 오픈소스 모델 파일이 갱신된 점과 RTX 5090으로 장시간 테스트 중이라는 점이 언급되어, 개발자들에게는 모델 배포·성능 확인 측면에서 참고할 만합니다.
RT @TeksEdge: 🔥 RTX 5090 + Gemma 4 31B: Real user testing right now 💳️ 32GB GDDR7 gives excellent headroom for higher quants on this dense 31B model. 🧪 Typical performance (llama.cpp + early user reports): QuantApprox. VRAM (weights + overhead)Expected TPS (generation) ⚡ Q4_K_M ~18–21GB 55–75+ t/s 📈 Q5_K_XL ~22–25GB 45–65 t/s 🐢 Q6_K / Q8 ~26–32+GB 35–55 t/s Users are actively testing 🐌 Unsloth UD-Q5_K_XL on RTX 5090 and tuning with TurboQuant / KV cache compression for better speed. Great quality + performance balance for local Gemma 4 31B inference 👌 Who else is running it? 👀
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