Gemma 4 GGUF Chat Template Fix: Re-download Guide
Gemma 4 GGUF chat template was fixed in early May 2026. See what broke, which Bartowski and Unsloth quants to re-download, and how to verify it locally.
TL;DR
- Every Gemma 4 GGUF chat template was patched in early May 2026. If you pulled a Gemma 4 GGUF before roughly May 1, the embedded Jinja template that controls chat formatting may produce broken
<start_of_turn>/<end_of_turn>markers. - Bartowski and Unsloth have both re-uploaded fixed Gemma 4 GGUF quants for the four official Gemma 4 sizes: 31B-it, 26B-A4B-it, E4B-it, and E2B-it.
- You don't always need to re-download the Gemma 4 GGUF. llama.cpp accepts
--chat-template-file path/to/template.jinja; KoboldCpp now exposes the same override under "loaded files → jinja template". - Fastest verification: load the Gemma 4 GGUF in LM Studio, send a 2-turn chat, and check whether the model echoes raw template tokens. If yes, your Gemma 4 GGUF file is stale.
What "Gemma 4 GGUF" Actually Means
GGUF (GPT-Generated Unified Format) is the binary file format used by llama.cpp and downstream runners — LM Studio, Ollama, KoboldCpp, Jan — to load quantized large language models on consumer hardware. A Gemma 4 GGUF is Google's Gemma 4 model converted to that format, typically by community quantizers like bartowski or unsloth, so the Gemma 4 GGUF can be re-quantized down to Q4_K_M, Q5_K_M, Q6_K, or Q8_0 sizes that fit in 6–48 GB of RAM or VRAM.
Each Gemma 4 GGUF file embeds two things worth caring about: the model weights, and a chat template — a Jinja2 string the runner uses to wrap user/assistant turns into the exact tokens Gemma was instruction-tuned on. The chat template is what the recent Gemma 4 GGUF fix targets. The weights themselves did not change.
The Chat Template Bug, Explained
On May 4, 2026, Reddit user u/jacek2023 posted on r/LocalLLaMA: "it's time to update your Gemma 4 GGUFs — Chat Template was fixed a few days ago." The thread climbed to 395 upvotes and 115 comments within 24 hours, signaling that a meaningful slice of the local-inference community was running broken templates without knowing it.
The bug, as discussed across the comment section, sits in how the Jinja template emits Gemma 4's chat control tokens. Gemma's instruction-tuned variants expect a strict pattern:
<start_of_turn>user
{prompt}<end_of_turn>
<start_of_turn>model
Earlier Gemma 4 GGUF builds shipped with a template that, in some edge cases (system messages, tool calls, or multi-turn replays), inserted whitespace or omitted <end_of_turn> markers. Symptoms users reported: the Gemma 4 GGUF continuing past its turn, hallucinating user replies, or echoing literal template tokens back as text.
The top comment from u/interAathma (91 upvotes) — "Can anyone tell, what was broken and what was improved in this new gguf?" — captures how silent the bug was. Most users only noticed degraded Gemma 4 GGUF output quality after switching to the patched version.
What Got Fixed in the New Gemma 4 GGUF
The fix is template-only. Both Bartowski's and Unsloth's re-uploaded Gemma 4 GGUFs keep the underlying weights identical to the original Google release; what changed is the JSON metadata block inside the GGUF file that holds the Jinja chat template.
For practical purposes:
- Output quality on single-turn instructions: minimal change.
- Output quality on multi-turn dialog, system prompts, or tool use: substantially cleaner. No more leaked turn markers.
- Token efficiency: minor improvement — the old Gemma 4 GGUF template occasionally emitted redundant tokens that ate into the context budget.
If your Gemma 4 GGUF only ever fielded single-question prompts, the practical impact is small. If the Gemma 4 GGUF backed chat-style assistants, agent frameworks, or anything with a system prompt, the new build is worth the re-download.
Where to Re-download (Bartowski vs Unsloth)
The original Reddit post lists six canonical re-upload paths, covering all four Gemma 4 variants from both major community quantizers:
| Model variant | Bartowski | Unsloth |
|---|---|---|
| Gemma 4 31B-it (dense, flagship) | bartowski/google_gemma-4-31B-it-GGUF | unsloth/gemma-4-31B-it-GGUF |
| Gemma 4 26B-A4B-it (MoE, 4B active) | bartowski/google_gemma-4-26B-A4B-it-GGUF | unsloth/gemma-4-26B-A4B-it-GGUF |
| Gemma 4 E4B-it (efficient 4B) | bartowski/google_gemma-4-E4B-it-GGUF | (not yet re-uploaded as of May 5) |
| Gemma 4 E2B-it (efficient 2B) | bartowski/google_gemma-4-E2B-it-GGUF | (not yet re-uploaded as of May 5) |
Practical differences between the two providers:
Bartowski tends to ship a wider quant ladder (everything from IQ2_XXS up to Q8_0 plus the imatrix variants), so users squeezing a 31B model into 16 GB VRAM usually find a better fit there. Unsloth's GGUFs are calibrated against their own fine-tuning datasets and historically score marginally better on instruction-following benchmarks, at the cost of fewer quant options. Both teams patched on roughly the same timeline.
How to Tell If Your Gemma 4 GGUF Is the New Version
Three quick checks, in increasing thoroughness:
- Hugging Face page timestamp. On the model page, the file list shows a "last modified" column. Anything dated before May 1, 2026 for the Gemma 4 repos is pre-fix.
- Local file mtime. On Linux/macOS:
stat -c '%y' your-gemma-4-31b-Q4_K_M.gguf. On Windows PowerShell:(Get-Item your-gemma-4-31b-Q4_K_M.gguf).LastWriteTime. Compare to the upload date. - Behavioral test. Load the GGUF in LM Studio (or any runner), send: "Hi" → "Tell me a joke" → "Now repeat it". A broken template will sometimes leak
<start_of_turn>or<end_of_turn>literals into the third response, or skip the turn entirely.
Test 3 is the only one that proves the fix is active end-to-end, because the runner could still override the embedded template with its own default if you launched it with custom flags.
Don't Want to Re-download? Override the Template
If you've already pulled a 30 GB Q5_K_M and don't fancy doing it again, you don't have to. As u/dampflokfreund pointed out (65 upvotes on the same Reddit thread):
"Or just use the current model with the updated chat template. In llama.cpp use
--chat-template-file 'path to your updated jinja', in koboldcpp there is also a feature that allows this now (under loaded files → jinja template)."
Concretely:
- Grab the updated Jinja from any of the patched Hugging Face repos. The file is
tokenizer_config.json→ look for thechat_templatefield, or some repos ship it as a standalonetemplate.jinja. - Save it locally as
gemma-4-fixed.jinja. - Launch llama.cpp with
--chat-template-file gemma-4-fixed.jinja. - For LM Studio, it auto-applies the embedded template — manual override requires editing the model's preset JSON. KoboldCpp has the GUI option mentioned above.
This saves the bandwidth but also means every time you switch machines you carry the override file with you. For most users, re-downloading once is cleaner.
FAQ
Does this fix change Gemma 4's actual capabilities? No. Weights are identical. The fix only affects how the runner formats user/assistant turns before tokenization. Single-turn instruction quality is essentially unchanged.
Is the bug present in the official Google releases on Hugging Face?
The community GGUFs (Bartowski, Unsloth) are converted from Google's safetensors. The template error originated upstream and was caught by community testing first. Google's instruction templates in the original google/gemma-4-* repos have since been updated.
Will my old conversation history still work with the new GGUF? Yes. Conversation logs are plain text. You can swap GGUFs without losing history. Newly generated turns under the fixed template will simply be cleaner.
Which Gemma 4 GGUF should I run on a 24 GB VRAM card (like an RTX 4090)?
The Gemma 4 GGUF in 31B-it at Q4_K_M (18 GB) leaves headroom for context. For longer chats, drop to Q4_K_S (16 GB). The 26B-A4B-it MoE Gemma 4 GGUF runs at Q4_K_M in roughly 13 GB but spikes higher under expert activation.
Can I use Ollama directly?
Ollama pulls Gemma 4 GGUF builds from its own model registry. As of May 5, 2026, the Ollama-tagged Gemma 4 entries are still being refreshed — ollama pull gemma4:31b-instruct may or may not yet point to the patched Gemma 4 GGUF. Cross-check the digest against bartowski's repo.
What about fine-tuning the Gemma 4 GGUF locally? GGUF is an inference format, not a training format. To fine-tune, start from the safetensors release and convert to a Gemma 4 GGUF afterward — Unsloth's training notebooks document the round-trip.
How We Wrote This
This article was assembled from public sources between May 4 and May 5, 2026:
- The originating r/LocalLLaMA thread by u/jacek2023 (395 upvotes, 115 comments at time of writing).
- Bartowski's and Unsloth's Hugging Face model cards.
- llama.cpp
--chat-template-filedocumentation. - Community comments providing the override workaround.
We did not run benchmarks on a clean test bench for this piece. The functional differences described above (turn-marker leakage, context efficiency) are summarized from user reports across the linked Reddit thread and the Hugging Face discussion tabs of the patched repos. If your results differ materially, please tell us.
About the Author
Jim Liu runs OpenAI Tools Hub, a developer-focused review and tutorial site covering AI coding tools, agent frameworks, and local LLM tooling. The site has published 130+ reviews since 2024 and tracks model releases through Hugging Face, GitHub, and the r/LocalLLaMA community. Editorial inquiries: see the About page.
This article does not constitute investment, legal, or professional advice. Run your own benchmarks before relying on a quantized model for production work. Local LLM accuracy varies with hardware, quant level, and prompt structure.