llama.cpp Adds MageVL-Cowork, Expanding the Local Multimodal Ecosystem
llama.cpp now supports MageVL-Cowork with ready-to-use GGUF weights, lowering the barrier to running vision-language models locally.
18 articles tagged with this topic
llama.cpp now supports MageVL-Cowork with ready-to-use GGUF weights, lowering the barrier to running vision-language models locally.
KlangAI's Pianissimo-da leads a Danish speech-recognition benchmark and offers GGUF, ONNX, MLX, and CoreML formats.
Unsloth's Dynamic v3.1 quantization boosts accuracy by 10%–15% and cuts the smallest Qwen3.8-Flash-Next build to 28.8GB.
Hugging Face adds native GGUF support to Transformers, letting Qwen3.5-4B quantized hit 98% of llama.cpp speed on M2 Max (70.4 vs 71.8 tok/s).
Audit of 443 GGUF files across 25 Hugging Face repos found 64 (~14%) with filenames that don't match actual precision. Local AI users should recheck t
Alibaba's Qwen local GGUF build updates with lower VRAM needs. The bar for self-hosted LLMs drops a notch, but this is routine open-source maintenance
AI9Stars' G9v3-39A5B was quantized to GGUF by Reddit user linuxid10t via a llama.cpp fork—open-source LLM tooling still runs on community relay.
An undergrad with a rented 4090 quantized Alibaba's Qwen3.5-9B to near-lossless, beating six established compression teams 31-3-0.
Unsloth ships a GGUF-quantized Muse-Glimmer-30B, letting consumer laptops run a 30-billion-parameter LLM locally. On-prem AI is shifting from hobbyist
Google fixed Gemma 4's chat template bug; community quantized versions updated. Not major news, but proves local AI usability inches up via detail ref
Mistral Medium 3.5 GGUF files corrupted, community-fixed. Reveals open source QA gap: APIs tested, local formats not—impacts enterprise deployments.
Un sloth claims top KLD-vs-disk-space performance for Qwen3.6-35B-A3B quants in 21 of 22 pareto frontier comparisons.
Oobabooga published 5 benchmark reports covering 70-90 GGUF quants each for Gemma 4 and Qwen 3.5 models using KL Divergence methodology.
KLD benchmarks across community GGUF quants show Q8_0 variants cluster near 0.001 KLD, with quality degrading shar ply below Q5.
Step-by-step JNI bridge implementation for running quantized LLMs on Android using llama.cpp.
Unsloth uploads 22 GGUF quantizations of MiniMax M2.7, ranging from 1-bit (60.7 GB) to BF16 (457 GB).
MiniMax-M2.7 (229B MoE) quantized to Q3_K_L (110GB) and Q8_0 (243GB) GGUF formats, now on HuggingFace.
Running Gemma 4 locally on CUDA requires strict dtype matching at KV cache boundaries or output degenerates silently.