0xSero

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gemma-4-21b-a4b-it-REAP

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Hi, everything below is claude writing. I was interested in what you posted on X so i had my claude work on this. Apparently we built the GGUF conversion pipeline and A/B tested it against the gemma 4 26b model i was using myself for my vault project. Sorry for the AI slop but I hope this helps! (If it doesn't I'm all ears, roast me or let me know what could be done better. I'm new to this and don't really have a coding background, just having some fun)

Hey @0xSero — great work on this. I converted to GGUF and ran it through a real production workload (automated knowledge synthesis pipeline running 24/7 on local GPU). Here are the results.

Setup: llama.cpp (Vulkan backend), RX 9070 XT 16GB, Q4_K_M quantization, Arch Linux

Conversion Pipeline

# Needs recent llama.cpp with gemma4 arch support
# Install deps in a venv: pip install huggingface-hub gguf safetensors numpy sentencepiece protobuf transformers torch

# 1. Download
hf download 0xSero/gemma-4-21b-a4b-it-REAP --local-dir ./gemma-4-21b-reap-bf16

# 2. Convert HF safetensors to GGUF F16
python3 convert_hf_to_gguf.py ./gemma-4-21b-reap-bf16/ --outfile gemma-4-21b-reap-F16.gguf --outtype f16

# 3. Quantize to Q4_K_M
llama-quantize gemma-4-21b-reap-F16.gguf gemma-4-21b-reap-Q4_K_M.gguf Q4_K_M

Important notes:

• You need a recent llama.cpp build that supports the gemma4 architecture. Older builds fail with unknown model architecture: 'gemma4'.
• Use --reasoning off flag in llama-server or it tries to emit thinking tokens and behaves oddly.
• The Vulkan build has the latest arch support — the default build may not.

Results vs Original 26B (same Q4_K_M quant, same hardware)

llama-server config: --ctx-size 2048 --ubatch-size 64 --cache-type-k q8_0 --cache-type-v q8_0 --parallel 1

Quality Assessment

Tested on 3 task types from our production pipeline (automated vault knowledge system):

1. Structured knowledge notes (YAML frontmatter, LaTeX, citations, wikilinks) — indistinguishable from original. Proper section hierarchy, technical depth maintained.
2. Cross-domain synthesis (connecting concepts across biology and ML) — equally strong reasoning, slightly more concise output. Generated clean comparison tables.
3. Python code generation (type hints, docstrings, edge-case handling) — equivalent quality. Clean exponential backoff implementation with full jitter.

No looping observed across any prompt with sampling params temp=0.3, top_p=0.9, max_tokens=1024. No vocabulary collapse. The 20% expert pruning is invisible on these tasks.

The Real Win: Context Window on 16GB Cards

The 3GB VRAM savings is the killer feature. The original 26B Q4_K_M barely fits with 4k context on 16GB. REAP comfortably runs 8k context at 17.3 tok/s on the same card. That's the difference between toy context and actually useful for long prompts.

For anyone running this model as a local inference workhorse (heartbeat tasks, knowledge pipelines, agentic loops), that 2x context headroom matters more than the modest speed bump.

Q3_K_S Quant Coming Next

I'm planning to also make a Q3_K_S variant. The original 26B Q3_K_S is 12GB — a REAP Q3_K_S should come in around ~10GB, which would allow 16k+ context on 16GB VRAM. That's genuinely exciting for local MoE inference.

Will post the Q3_K_S results once I have them. Happy to share the GGUF files if you want to host them under this repo — just let me know.

Bottom Line

This is a strict upgrade for anyone on 12-16GB VRAM. Same quality, more speed, double the context. The pruned experts genuinely don't matter for real workloads. Swapping my production server over today.

Hardware: AMD RX 9070 XT (16GB VRAM), Ryzen 5 7600X, Arch Linux, llama.cpp (Vulkan)
Workload: 24/7 automated knowledge synthesis pipeline (structured note generation, cross-domain synthesis, code generation)