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h4rm0n1c/qwen3.5-24b-a10b-IQ4_NL-GGUF overview

qwen3.5b 24b a10b IQ4 NL GGUF Quanter's Note: Sibling of sandeshrajx/Qwen3.5 24B A3B REAP 0.32 https://huggingface.co/sandeshrajx/Qwen3.5 24B A3B REAP 0.32 , t…

ggufqwen3.5moeiq4_nlquantzhaiquantizedbase_model:sandeshrajx/qwen3.5b-24b-a10bbase_model:quantized:sandeshrajx/qwen3.5b-24b-a10blicense:mitendpoints_compatibleregion:us

Runs locally from ~12.93 GB disk (16 GB VRAM class GPUs with llama.cpp / guIDE).

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qwen3.5b-24b-a10b-IQ4_NL.ggufGGUFIQ4_NL12.93 GBDownload

Model Details

Model IDh4rm0n1c/qwen3.5-24b-a10b-IQ4_NL-GGUF
Authorh4rm0n1c
Pipeline
Licensemit
Base modelsandeshrajx/qwen3.5b-24b-a10b
Last modified2026-06-19T16:41:21.000Z

Model README

---

license: mit

library_name: gguf

base_model: sandeshrajx/qwen3.5b-24b-a10b

tags:

  • qwen3.5
  • moe
  • gguf
  • iq4_nl
  • quantzhai
  • quantized

---

qwen3.5b-24b-a10b IQ4_NL GGUF

Quanter's Note: Sibling of sandeshrajx/Qwen3.5-24B-A3B-REAP-0.32, this one has been passed over completely for reasoning distills, I'd be interested to see what difference 7b more active experts can make.

GGUF quantization of sandeshrajx/qwen3.5b-24b-a10b — a 24B-parameter MoE model with 10B active parameters per token. Architecture is Qwen3.5 MoE.

Source: sandeshrajx/qwen3.5b-24b-a10b

Converted by: QuantZhai benchmark pipeline

Quantization: IQ4_NL (importance-matrix 4-bit non-linear)

Model Details

| Property | Value |

|---|---|

| Architecture | Qwen3.5 MoE (Dense + Mamba-2 SSM interleaved) |

| Parameters | 24B total, 10B active per token |

| Experts | 39, 8 active per token |

| Context length | 262144 (256K) |

| Hidden size | 3072 |

| Attention heads | 32, KV heads = 2 |

| Head dim | 256 |

| RoPE | MRope (multimodal), theta = 10,000,000 |

| SSM | Mamba-2 inspired conv/state-space per 4th layer |

| Quantization | IQ4_NL (4.50 bpw) |

| File size | 13.9 GB |

| Tokenizer | Qwen2 (GPT-2 based BPE, vocab 248,320) |

Benchmarks

Hardware: dual-GPU (RTX 3080 10GB + V100-SXM2 32GB, 42 GB total)

Engine: llama.cpp with TurboQuant KV (q8_0 K / turbo3 V)

Perplexity: macvox68 code corpus, ctx=4096, stride=512

| Metric | Cold | Warm |

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

| PPL | 8.0217 | 3.9654 |

| TPS | 26.1 tok/s | 31.4 tok/s |

| TTFT | 1919 ms | 1594 ms |

QuantZhai Ranking

Rank #30 of 46 — combined score 50.5 (equal-weight: TPS, PPL, convergence).

Usage

llama-cli -m qwen3.5b-24b-a10b-IQ4_NL.gguf \
  -p "Write a mergesort in Python" \
  -n 1024 -t 12 --temp 0.6 --top-p 0.95

llama-server -m qwen3.5b-24b-a10b-IQ4_NL.gguf \
  --host 0.0.0.0 --port 8080 -ngl 99 -t 12 \
  --cache-type-k q8_0 --cache-type-v turbo3

Recommended: temp 0.6, top-p 0.95, context up to 256K.

License

MIT (this quantization).

Source model by sandeshrajx — review its license separately.

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