---

language:

• en

base_model:

• Akicou/Qwen3.8-Flash-Next-REAM-60Pct

license: other

tags:

• qwen
• ream
• merged
• compression
• mixture-of-experts
• moe
• gguf
• llama.cpp

pipeline_tag: text-generation

---

Qwen3.8-Flash-Next-REAM-60Pct-GGUF

GGUF quantizations of Akicou/Qwen3.8-Flash-Next-REAM-60Pct,

the REAM-compressed (Merged) version of Qwen/Qwen3.8-Flash-Next.

What this is

REAM (Router Expert Activation Merging) pruned 40% of the routed experts in

the original model, taking each layer from 512 down to 308 experts. The

compressed checkpoint was then converted to GGUF with

ggml-org/llama.cpp (convert_hf_to_gguf.py,

bf16) and quantized with llama-quantize. No importance matrix was used.

Files

| File | Quant | Bits per weight | Size |

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

| qwen4-ream-q8.gguf | Q8_0 | ~8.5 | ~137 GB |

| qwen4-ream-q4_k_s.gguf | Q4_K_S | ~4.5 | ~82 GB |

| qwen4-ream-q4_k_m.gguf | Q4_K_M | ~5.5 | ~87 GB |

The architecture is qwen4exp (hybrid linear attention + Qwen Sparse Attention

MoE), 48 layers, 308 routed experts per layer.

Usage

llama-cli -m qwen4-ream-q4_k_m.gguf -p "Explain reinforcement learning." -n 256

Notes

• Experimental release, not benchmarked.
• The base model requires trust_remote_code=True. These GGUF files are for

llama.cpp (and compatible runtimes), so remote code is not needed at load.

• Shared experts, attention, and n-gram embeddings are untouched; only routed

experts were merged.