---

license: other

library_name: llama.cpp

base_model:

• poolside/Laguna-XS-2.1

base_model_relation: quantized

pipeline_tag: text-generation

quantized_by: MagicQuant

language:

• en

tags:

• gguf
• quantized
• magicquant
• pruned
• reap
• moe

---

What this is

A 50% expert-pruned poolside/Laguna-XS-2.1

(33.4B, 256-expert MoE) reduced to 17.7B / 128 experts with

REAP router-weighted expert pruning,

then quantized with MagicQuant's measured evolutionary search.

63 GB of BF16 weights become a 9.4 GB GGUF.

Read this before using it

Expert selection was calibrated on code (theblackcat102/evol-codealpaca-v1).

REAP therefore kept the experts that matter for code and dropped ones that did not

— the model is more specialized, not uniformly degraded. Measured perplexity

(100 chunks, ctx 512, identical settings; code = held-out evol-codealpaca):

| Model | Size | code PPL | wikitext PPL |

|---|---|---|---|

| Laguna-XS-2.1 (unpruned, BF16) | 63 GB | 3.1169 | 12.2918 |

| REAP-50% pruned (BF16) | 33 GB | 3.4864 (+11.9%) | 34.8363 (+183%) |

| This file — pruned + MagicQuant Q4 | 9.4 GB | 3.5703 (+14.5%) | 35.9179 (+192%) |

Use it for code. General-English ability is substantially reduced — that is the

direct, expected consequence of pruning experts by code activations, and it is

disclosed here rather than buried. Quantization itself costs only +2.4% on code;

almost all of the delta is the pruning.

Notes

• Requires a laguna-aware llama.cpp build (arch support landed July 2026;

built and validated here against e9fa078).

• No ROCmFPX sibling repo: the ROCmFPX fork does not yet carry laguna support.
• No MTP/speculative-decoding tensors in this architecture.
• License follows the base model (openmdw-1.1). All credit for the model to poolside.

Laguna-XS-2.1-REAP50-MagicQuant-GGUF

Derivative of Laguna-XS-2.1, pruned with REAP (Router-weighted Expert Activation Pruning) and quantized using MagicQuant hybrid evolutionary per-tensor search.

Base Model

This is a derivative of Laguna-XS-2.1.

All credit for the base model architecture and weights goes to the original authors.

The base model's license applies to this derivative.

Expert Pruning (REAP)

This is a Mixture-of-Experts model pruned using REAP

(Router-weighted Expert Activation Pruning) from Cerebras Research:

• A calibration pass records router decisions and expert activations on representative data
• Each expert is scored with a saliency metric weighted by router usage
• The lowest-ranked experts in each MoE layer are dropped
• The router is trimmed accordingly so the remaining experts cover the full routing distribution

The result is a smaller MoE model with fewer experts per layer, trading a small amount of quality

for reduced parameter count and inference cost.

Quantization Method

Quantized using MagicQuant hybrid evolutionary per-tensor quantization,

based on the methodology by magiccodingman:

• Tensors are classified into sensitivity groups (Embeddings, Head, Query, Key, Output, FFN Up/Down, MoE Experts, Router)
• An evolutionary search finds the optimal quantization type per group, balancing size vs. perplexity
• Q4/Q5/Q6 tier targets are produced with different size-quality tradeoffs
• Small-row tensors and sensitivity-critical layers (embeddings, output head, router) are kept at F32/F16/BF16
• This is NOT a uniform quantization -- each tensor group gets its own optimal type

GGUF Files

| File | Size | Quant |

|------|------|-------|

| Laguna-XS-2.1-REAP50-Q4_K_M.gguf | 10.0 GB | Q4 hybrid |

Usage

LM Studio

1. Download the GGUF file of your preferred quantization tier
2. Place it in your LM Studio models directory
3. Load the model in LM Studio -- it will auto-detect the chat template
4. The model supports the base model's full context length

llama.cpp

# Interactive chat (--jinja uses the model's embedded chat template, not a hardcoded one)
llama-cli -m Laguna-XS-2.1-REAP50-Q4_K_M.gguf -c 8192 --jinja -cnv

# Single prompt
llama-cli -m Laguna-XS-2.1-REAP50-Q4_K_M.gguf -c 8192 -p "Your prompt here"

# Server mode
llama-server -m Laguna-XS-2.1-REAP50-Q4_K_M.gguf -c 8192 --port 8080 --jinja

Python (llama-cpp-python)

from llama_cpp import Llama

llm = Llama(model_path="./Laguna-XS-2.1-REAP50-Q4_K_M.gguf", n_ctx=8192)
output = llm.create_chat_completion(
    messages=[
        {"role": "user", "content": "Hello, how are you?"}
    ]
)
print(output["choices"][0]["message"]["content"])

Caveats

• The base model's license (other) applies to all derivative files
• Expert pruning removes a fraction of experts per MoE layer; some task-specific knowledge may be lost
• The pruned model has a different number of experts from the original — tooling that hardcodes expert count may need adjustment
• Quantization reduces precision -- verify outputs for your specific use case
• The hybrid quantization assigns different precision to different tensor groups, which means quality characteristics may differ from uniform quantizations

Limitations

• Pruned experts cannot be recovered; any capabilities concentrated in removed experts are lost
• Quantized models may exhibit subtle differences from the full-precision fine-tune
• This model inherits any limitations and biases present in the base model

---

Generated with REAP + MagicQuant