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MLX

2 mentions in 7 days · 2 in 30 days · 2 total · first seen · last

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prism-ml/Ternary-Bonsai-2-27B-gguf — new model trending #29 on Hugging Face

Prism ML released Ternary-Bonsai-2-27B, a 27B ternary-weight model derived from Qwen3.8-27B that runs full reasoning in ~5.95 GB GGUF.

Prism ML published Ternary-Bonsai-2-27B, an Apache 2.0 open-weights model derived from Qwen3.8-27B (27.36B parameters, hybrid attention ~75% linear, 262K-token context). Weights are end-to-end ternary {−1, 0, +1} with g128 FP16 scales at 1.72 bits/weight, shrinking the model from ~54 GB FP16 to 5.95 GB (PTQ1_0) or 7.21 GB (PQ2_0) while reportedly retaining 98.2% of FP16 quality (84.78 average across 14 thinking-mode benchmarks). It ships as GGUF for llama.cpp (CUDA, Metal, CPU) with custom ternary kernels, plus an MLX 2-bit build for Apple Silicon and an optional Q8_0 vision tower pack.

harshatheg/Qwen-2.5-1B-RLCD — new model trending #30 on Hugging Face

A community MLX inference engine evaluates constrained JSON schema fields in parallel on Apple Silicon, reporting 5.6-7.0x latency speedups with guaranteed schema validity.

The repository harshatheg/Qwen-2.5-1B-RLCD appeared at #30 on Hugging Face trending, but its content describes Parallel Constrained Decoding, an MLX-based inference engine for structured extraction and classification on Apple Silicon Macs. Benchmarked with mlx-community/Qwen2.5-1.5B-Instruct-4bit on an M4 Max, it reports 5.6x-7.0x latency reductions (e.g., 1,900 ms to 270 ms for a 28-field support triage task) with 100% syntactic validity and calibrated field-level probabilities. The engine prefills a single KV-cache, broadcasts it across all schema fields, and slices logits to valid candidate tokens for enum fields with up to 255 choices.

Hugging Face trending modelsupdated · 1d agofirst · 2d agoAI tools & infra 2 sources1