---
license: other
license_name: modified-mit
license_link: LICENSE
base_model:
- Ex0bit/MiniMax-SLURPY
base_model_relation: quantized
tags:
- mlx
- apple-silicon
- moe
- prism-dq
- dynamic-quantization
- minimax
- minimax_m2
- code
- reasoning
- agents
- quantized
model_type: minimax_m2
pipeline_tag: text-generation
library_name: mlx
quantized_by: Ex0bit
---
# MiniMax-SLURPY-DQ-MLX
**Per-tensor mixed-precision quantization of [MiniMax-SLURPY](https://huggingface.co/Ex0bit/MiniMax-SLURPY) for Apple Silicon — 2.54 BPW with 498 per-tensor-projection allocations (plus 16,122 per-expert PRISM decisions collapsed into MLX's SwitchGLU format).**
The full SLURPY model (228.7B params) compressed from 215 GB → 68 GB (68% reduction) using **PRISM Dynamic Quantization** — a per-tensor-class mixed-precision allocation derived entirely from weight structure sensitivity analysis. Zero calibration data, zero training, zero datasets.
Created by [Ex0bit](https://hf.co/Ex0bit)
---
### 💡 Support our Research & Development efforts. PRISM Members Receive access to the latest PRISM-PRO Model drops on Day-0
[](https://ko-fi.com/Ex0bit)
---
## Model Details
| Property | Value |
|----------|-------|
| Base Model | [Ex0bit/MiniMax-SLURPY](https://huggingface.co/Ex0bit/MiniMax-SLURPY) |
| Architecture | MiniMax M2 MoE (256 experts, top-8) |
| Parameters | 228.7B total / ~10B active |
| Quantization | PRISM-DYNAMIC-QUANT (MLX native) |
| Achieved BPW | 2.54 |
| File Size | 68 GB (vs 215 GB source = 68% reduction) |
| Per-tensor overrides | 498 (MoE: per-layer-projection modal of 16,122 per-expert decisions) |
| Default precision | 2-bit |
| Group size | 64 |
| Context Length | 196,608 tokens |
| Runtime | mlx-lm (Apple Silicon Metal) |
| Creator | [Ex0bit](https://hf.co/Ex0bit) |
## What SLURPY inherits
A mathematically unique Designer Baby of [MiniMax-M2.5](https://huggingface.co/MiniMaxAI/MiniMax-M2.5) and [MiniMax-M2.7](https://huggingface.co/MiniMaxAI/MiniMax-M2.7) — neither parent, entirely its own model.
SLURPY inherits M2.5's architect-first coding style and MIT freedom, absorbs M2.7's RL-tuned precision on multi-agent collaboration and real-world engineering — without a single training step.
| Benchmark | M2.5 | M2.7 | SLURPY |
|---|---|---|---|
| HumanEval pass@5 | 85.4% | — | **89.6%** |
| SWE-Bench Verified | 80.2% | — | inherited |
| SWE-Pro | — | 56.2% | inherited |
| MLE Bench Lite | — | 66.6% | inherited |
| GDPval-AA ELO | — | 1495 | inherited |
See [Ex0bit/MiniMax-SLURPY](https://huggingface.co/Ex0bit/MiniMax-SLURPY) for full benchmark details.
---
## PRISM Dynamic Quantization
This model uses **PRISM Dynamic Quantization** — a per-tensor mixed-precision allocation that assigns different quantization types to different tensor classes based on weight structure sensitivity analysis.
Unlike uniform quantization (Q3, Q4, Q5), PRISM-DQ analyzes each tensor's sensitivity to quantization error and allocates precision where it matters most. Critical tensors (attention projections, key MoE experts, lm_head) receive higher precision while less impactful tensors get aggressive compression.
PRISM produced 16,122 per-expert decisions (256 experts × 62 layers × 3 projections, plus attention and embeddings). MLX's `SwitchGLU` packs all 256 experts per layer-projection into a single 3D tensor sharing one bit width, so the per-expert decisions collapse to the modal bit width for each of the 186 MoE projections. The remaining 312 per-tensor decisions (attention, embeddings, lm_head, routers) retain full PRISM granularity, giving 498 effective overrides.
The model's `config.json` contains the per-tensor quantization overrides that mlx-lm loads natively — no custom runtime required. Apple Silicon's compiled Metal kernels automatically handle mixed-precision tensors in a single forward pass at full GPU speed.
**No calibration data, no importance matrices, no training data required.**
---
## Architecture
Identical to MiniMax-M2.5 / M2.7 — quantization-only:
- **Model type**: `minimax_m2` / `MiniMaxM2ForCausalLM`
- **Parameters**: 228.7B total, ~10B active (MoE)
- **Layers**: 62
- **Hidden size**: 3072
- **MoE**: 256 experts, top-8, sigmoid routing + learned bias
- **Attention**: 48 query / 8 KV heads (GQA 6:1), head_dim=128
- **Quantization**: MLX affine, mixed 2-6 bit
- **Vocab**: 200,064 tokens
- **Context**: up to 196,608 tokens
- **Thinking**: Interleaved `...` (always-on)
- **`trust_remote_code=True` required**
---
## Usage on Apple Silicon
### mlx-lm (CLI)
```bash
pip install mlx-lm
# Interactive chat
mlx_lm.chat --model Ex0bit/MiniMax-SLURPY-PRISM-3BPW-MLX \
--temperature 1.0 --top-p 0.95 --max-tokens 4096
# Single prompt
python -m mlx_lm.generate \
--model Ex0bit/MiniMax-SLURPY-PRISM-3BPW-MLX \
--prompt "Write a Python function that reverses a linked list." \
--max-tokens 2048 \
--temp 1.0 --top-p 0.95
```
### Python API
```python
from mlx_lm import load, generate
model, tokenizer = load("Ex0bit/MiniMax-SLURPY-PRISM-3BPW-MLX")
response = generate(
model, tokenizer,
prompt="Write a Python function that reverses a linked list.",
max_tokens=2048,
temp=1.0,
top_p=0.95,
)
print(response)
```
### Recommended sampling parameters
| Parameter | Value |
|---|---|
| temperature | 1.0 |
| top_p | 0.95 |
| top_k | 40 |
### Important: preserve thinking in conversation history
MiniMax-M2 uses interleaved thinking. The model outputs `...` blocks during generation. **You must pass these back verbatim in conversation history.** Removing them degrades performance.
---
## Tool calling
Same format as base SLURPY. Tool calls use `` / `` XML wrappers:
```xml
San Francisco
```
---
## Hardware requirements
- **Apple Silicon Mac** with unified memory
- **80 GB RAM minimum** (model is 68 GB; needs headroom for KV cache)
- **128 GB RAM recommended** for full context length
- **M2 Ultra / M3 Max / M4 Max** for best throughput
For non-Apple platforms, use the FP8 [Ex0bit/MiniMax-SLURPY](https://huggingface.co/Ex0bit/MiniMax-SLURPY) variant with vLLM.
---
## Files
- 14 MLX safetensors shards (68 GB total)
- `config.json` with 498 per-tensor quantization overrides (collapsed from 16,122 PRISM decisions via SwitchGLU packing)
- `chat_template.jinja` — M2.7's chat template with tool calling support
- `modeling_minimax_m2.py` / `configuration_minimax_m2.py` — custom model code (inherited from base)
---
## License
Modified MIT — same as MiniMax-M2.5. See [LICENSE](LICENSE) for full text.
The only modification to the standard MIT license: if the Software (or any derivative works) is used for commercial products or services with more than 100 million monthly active users or more than $30M annual recurring revenue, you must prominently display "MiniMax M2" on the user interface.
---
## Credits
- Creator: [Ex0bit](https://hf.co/Ex0bit)
- Base model: [Ex0bit/MiniMax-SLURPY](https://huggingface.co/Ex0bit/MiniMax-SLURPY)
- Parents: [MiniMaxAI/MiniMax-M2.5](https://huggingface.co/MiniMaxAI/MiniMax-M2.5), [MiniMaxAI/MiniMax-M2.7](https://huggingface.co/MiniMaxAI/MiniMax-M2.7)
- Quantization engine: PRISM-DQ by [Ex0bit](https://hf.co/Ex0bit)
---
## Citation
```
@misc{minimax-slurpy-prism-mlx-2026,
title={MiniMax-SLURPY-PRISM-3BPW-MLX: Per-tensor mixed-precision quantization of MiniMax-SLURPY for Apple Silicon},
author={Ex0bit},
year={2026},
url={https://huggingface.co/Ex0bit/MiniMax-SLURPY-PRISM-3BPW-MLX}
}
```