--- base_model: MiniMaxAI/MiniMax-M2.7 library_name: mlx pipeline_tag: text-generation license: other license_name: minimax-model-license license_link: https://huggingface.co/MiniMaxAI/MiniMax-M2.7/blob/main/LICENSE tags: - minimax - m2.7 - moe - quantized - turboquant - kv-cache-quantization - mlx --- > [!NOTE] > **Status (2026-07-07): no weights published yet.** This repository currently contains only the model card — it marks a planned variant that has not been released. Follow the repo to be notified when files land. > [!TIP] > **KV-cache quantization without any fork (recommended, 2026):** upstream > llama.cpp/Ollama now cover this natively — use `-ctk q8_0 -ctv q8_0` > (~half KV memory, negligible quality loss: perplexity +0.002–0.05) or > `-ctk q4_0 -ctv q4_0` (~quarter memory, ≈7.6% perplexity increase). In > Ollama: `OLLAMA_KV_CACHE_TYPE=q8_0` with `OLLAMA_FLASH_ATTENTION=1`. Keep > K and V types symmetric to stay on the fast fused Flash-Attention path. > Since April 2026, mainline llama.cpp also applies Hadamard rotation to > KV activations ([PR #21038](https://github.com/ggml-org/llama.cpp/pull/21038)), > which greatly improves low-bit KV quality (opt-out: > `LLAMA_ATTN_ROT_DISABLE=1`). > > The RotorQuant/TurboQuant fork flow below is **experimental/legacy**: the > TurboQuant llama.cpp PR was closed without merging (June 2026) and the fork > is unmaintained relative to mainline. It is NOT required to use this model. # MiniMax-M2.7-TurboQuant-MLX-2bit **MLX 2-bit quantized variant of [MiniMaxAI/MiniMax-M2.7](https://huggingface.co/MiniMaxAI/MiniMax-M2.7) with TurboQuant KV-cache compression, optimized for Apple Silicon.** ## Overview MiniMax-M2.7 is a massive 256-expert Mixture-of-Experts (MoE) model with 8 experts active per token, totaling approximately 456 billion parameters. This variant combines **2-bit MLX weight quantization** with **TurboQuant** KV-cache quantization for deployment on Apple Silicon hardware. TurboQuant uses asymmetric per-channel quantization on the KV cache. At 2-bit, this is the most aggressively compressed variant -- it enables running on 128 GB Apple Silicon but comes with meaningful quality degradation. Best suited for experimentation, prototyping, and latency-sensitive applications where approximate outputs are acceptable. | Property | Value | |---|---| | Architecture | MoE (256 experts, 8 active/token) | | Total Parameters | ~456B | | Layers | 62 | | Hidden Size | 3072 | | Attention Heads | 48 | | Weight Quantization | 2-bit (MLX) | | KV-Cache Quantization | TurboQuant | | Estimated Size | ~110 GB | | Base Model | MiniMaxAI/MiniMax-M2.7 | ## Quickstart ```bash pip install mlx-lm ``` ```python from mlx_lm import load, generate model, tokenizer = load("majentik/MiniMax-M2.7-TurboQuant-MLX-2bit") prompt = "What is a Comprehensive Geriatric Assessment?" messages = [{"role": "user", "content": prompt}] text = tokenizer.apply_chat_template(messages, add_generation_prompt=True, tokenize=False) response = generate( model, tokenizer, prompt=text, max_tokens=512, ) print(response) ``` ## TurboQuant vs RotorQuant | Feature | TurboQuant | RotorQuant | |---|---|---| | Technique | Asymmetric per-channel KV quantization | Rotation-based KV quantization (Hadamard transform) | | Throughput | Higher throughput, lower latency | Slightly lower throughput | | Quality | Good quality preservation | Better quality preservation at low bit-widths | | Best For | High-throughput serving, long contexts | Quality-sensitive tasks, research | > At 2-bit quantization, quality loss is significant for both methods. The [RotorQuant variant](https://huggingface.co/majentik/MiniMax-M2.7-RotorQuant-MLX-2bit) will generally produce better outputs at this bit-width due to its rotation-based outlier smoothing. ## Memory Estimates (Apple Silicon) | Variant | Estimated Size | Minimum Unified Memory | |---|---|---| | MLX 8-bit | ~456 GB | 512 GB (Mac Studio M2/M3/M4 Ultra) | | MLX 5-bit | ~280 GB | 384 GB | | MLX 4-bit | ~225 GB | 256 GB | | MLX 3-bit | ~170 GB | 192 GB | | MLX 2-bit | ~110 GB | 128 GB | > **Note**: 2-bit is the most accessible variant, fitting on Apple Silicon with 128 GB+ unified memory (e.g., M2/M3/M4 Max or Ultra). Expect noticeable quality degradation compared to higher bit-widths. ## See Also - [MiniMaxAI/MiniMax-M2.7](https://huggingface.co/MiniMaxAI/MiniMax-M2.7) -- Base model - [majentik/MiniMax-M2.7-TurboQuant](https://huggingface.co/majentik/MiniMax-M2.7-TurboQuant) -- KV-cache only (transformers) - [majentik/MiniMax-M2.7-RotorQuant-MLX-2bit](https://huggingface.co/majentik/MiniMax-M2.7-RotorQuant-MLX-2bit) -- RotorQuant MLX 2-bit - [majentik/MiniMax-M2.7-TurboQuant-MLX-3bit](https://huggingface.co/majentik/MiniMax-M2.7-TurboQuant-MLX-3bit) -- MLX 3-bit - [majentik/MiniMax-M2.7-TurboQuant-MLX-4bit](https://huggingface.co/majentik/MiniMax-M2.7-TurboQuant-MLX-4bit) -- MLX 4-bit ## Quant trade-off (MLX lane) | Bits | Approx size | Use case | Recommendation | |---|---|---|---| | **2-bit** | ~119 GB | Aggressive quantization | **Very low-RAM Macs** | | 3-bit | ~164 GB | Lossy but small | Low-RAM Macs | | 4-bit | ~192 GB | Balanced default | Recommended for most Macs | | 5-bit | ~228 GB | Higher fidelity | Quality-sensitive | | 6-bit | ~274 GB | Approaching FP16 quality | High-fidelity | | 8-bit | ~347 GB | Near-lossless reference | Fidelity-critical work | (Current variant — **2bit** — is bolded.) ## Variants in this family (Showing 12 sibling variants under `majentik/minimax-m2.7-*`. The current variant — `TurboQuant-MLX-2bit` — is **bolded**.) | Variant | Runtime | Approx size | Use case | |---|---|---|---| | [RotorQuant](https://huggingface.co/majentik/minimax-m2.7-rotorquant) | runtime modifier | n/a | KV-cache root (weight-agnostic) | | [RotorQuant-MLX-2bit](https://huggingface.co/majentik/minimax-m2.7-rotorquant-mlx-2bit) | mlx-lm | ~885 MB | Apple Silicon, smallest | | [RotorQuant-MLX-3bit](https://huggingface.co/majentik/minimax-m2.7-rotorquant-mlx-3bit) | mlx-lm | ~1.2 GB | Apple Silicon, small | | [RotorQuant-MLX-4bit](https://huggingface.co/majentik/minimax-m2.7-rotorquant-mlx-4bit) | mlx-lm | ~1.7 GB | Apple Silicon balanced | | [RotorQuant-MLX-5bit](https://huggingface.co/majentik/minimax-m2.7-rotorquant-mlx-5bit) | mlx-lm | ~2.1 GB | Apple Silicon, higher fidelity | | [RotorQuant-MLX-8bit](https://huggingface.co/majentik/minimax-m2.7-rotorquant-mlx-8bit) | mlx-lm | ~3.2 GB | Apple Silicon reference | | [TurboQuant](https://huggingface.co/majentik/minimax-m2.7-turboquant) | runtime modifier | n/a | KV-cache root (weight-agnostic) | | **TurboQuant-MLX-2bit** | mlx-lm | ~885 MB | Apple Silicon, smallest | | [TurboQuant-MLX-3bit](https://huggingface.co/majentik/minimax-m2.7-turboquant-mlx-3bit) | mlx-lm | ~1.2 GB | Apple Silicon, small | | [TurboQuant-MLX-4bit](https://huggingface.co/majentik/minimax-m2.7-turboquant-mlx-4bit) | mlx-lm | ~1.7 GB | Apple Silicon balanced | | [TurboQuant-MLX-5bit](https://huggingface.co/majentik/minimax-m2.7-turboquant-mlx-5bit) | mlx-lm | ~2.1 GB | Apple Silicon, higher fidelity | | [TurboQuant-MLX-8bit](https://huggingface.co/majentik/minimax-m2.7-turboquant-mlx-8bit) | mlx-lm | ~3.2 GB | Apple Silicon reference |