2.7B parameters, 54 layers (9 hybrid attention blocks, 2 shared transformers cycling even/odd)
Hidden size: 2560, attention hidden: 5120
SSM: d_state=64, d_conv=4, ngroups=1
No RoPE (use_mem_rope=false) — attention without positional encoding
Requires llama.cpp with Zamba2 support (PR #21412)

Available Quantizations

Quant	Size	BPW	PPL (WikiText-2)	Prompt tok/s	Gen tok/s	Hardware
Q4_0	2.1 GB	4.56	17.53	280.9	26.6	RTX 4090
Q8_0	3.9 GB	8.51	16.94	278.3	26.0	RTX 4090

Perplexity Comparison (WikiText-2, n_ctx=512)

Config	Description	PPL
A	Q4_0 weights + F16 KV	17.53
D	Q8_0 weights + F16 KV	16.94
E	F32 weights + F16 KV	~16.8 (pending)
B/C	Q4/Q8 KV cache	Requires FA (head_dim=160)

Note on KV cache quantization: The 2.7B model has head_dim=160, which is not a multiple of 64. This prevents the Hadamard rotation used by llama.cpp's standard KV quantization. Quantized KV cache requires Flash Attention, which is not yet wired for Zamba2.

Bug Fix (v2 release — 2026-04-04)

These GGUFs replace an earlier release that had a critical bug. The 2.7B model has use_mem_rope=false in its HuggingFace config, meaning it does not use rotary position embeddings in attention. The original converter applied RoPE unconditionally, corrupting all attention computations. This was the ONLY Zamba2 size affected (1.2B and 7B both have use_mem_rope=true).

Before fix: PPL = 37.90 (Q4_0), 37.69 (Q8_0), 37.68 (F32)
After fix: PPL = 17.53 (Q4_0), 16.94 (Q8_0)
Now correctly ordered: 1.2B (22.5) > 2.7B (17.5) > 7B (13.8)

Sample Output (Q8_0)

Q: What is the capital of France?

A: The capital of France is Paris.

Usage

bash

# Build llama.cpp with Zamba2 support
git clone https://github.com/echo313unfolding/llama.cpp -b zamba2-support
cd llama.cpp && cmake -B build -DGGML_CUDA=ON && cmake --build build -j

# Run
./build/bin/llama-cli -m zamba2-2.7b-instruct-v2-q8_0.gguf \
  -p "<|im_start|>user\nWhat is quantum computing?<|im_end|>\n<|im_start|>assistant\n" \
  -n 256 -ngl 999 -e --no-conversation

Conversion Details

Converted from HF safetensors using a custom Zamba2-to-GGUF converter:

Mamba-2 SSM layers: A_log to A conversion, conv1d squeeze, dt projection
2 shared transformer blocks with per-layer LoRA unfolding (W_eff = W_shared + B @ A)
Per-layer n_head_kv array (0 for Mamba layers, 32 for attention layers)
rope_dimension_count=0 for use_mem_rope=false models (disables RoPE in attention)
BPE tokenizer (v2 format)
F32 master, quantized with llama-quantize

Credits

Architecture: Zyphra (Zamba2)
GGUF Conversion: Echo Labs
llama.cpp Zamba2 support: PR #21412

EchoLabs33/Zamba2-2.7B-Instruct-v2-GGUF

Zamba2 2.7B Instruct v2 - GGUF

Architecture