🚨⚠️ I HAVE REACHED HUGGING FACE'S FREE STORAGE LIMIT ⚠️🚨
I can no longer upload new models unless I can cover the cost of additional storage. I host 70+ free models as an independent contributor and this work is unpaid. Without your support, no more new models can be uploaded.
Your help will motivate me and would go into further improving my workflow and coverings fees for storage, compute and may even help uncensoring bigger model with rental Cloud GPUs.
🐣 Tiny footprint, big brain — a local coding model for everyone
No matter your GPU. No matter your RAM. If you've got ~4.5 GB of VRAM or unified memory free,
you can run your own private, offline coding assistant right now. 🚀
This is the v1 / code edition — distilled from real chain-of-thought so it thinks through a problem
before writing the solution. 🧠💻 All local, all yours, no API, no cloud.
🎯 What it is
A focused fine-tune of Gemma 4 12B on verifiable Python coding data — every training example's reasoning leads to
code that actually passed its tests. The result reasons in the open (edge cases, complexity, approach) and then
emits a clean, runnable solution. 💚
📌 Announcements
🚀🔥 IT'S HERE — v2 is OUT NOW! v2 has shipped — the GGUF quants are live and ready to run →
grab v2 here. 🎉
The full safetensors master (build / fine-tune on top) goes up tomorrow. v2 is agentic + coding focused —
the piece v1 was missing.
Here's the result that got me most excited. When I saw v2's tau2-bench telecom result — an agentic tool-use
benchmark where the model has to diagnose → fix → verify, exactly like real terminal/debugging work — I literally got
launched out of my chair (…okay, kidding 😄). The jump in actually solving the problem is wild:
tau2-bench telecom · local, same harness, Q8_0
score
official gemma-4-12B-it (base)
~15%
🟢 v2 (this release)
~55%
The base model tends to give up early (hands the problem off to a human); v2 keeps going and works it the way a
much bigger model would. Full benchmark details are in the v2 card now. 🔧
✅ safetensors master (this v1 model) is UP. Full-precision weights are live →
yuxinlu1/gemma-4-12B-coder-fable5-composer2.5-v1
— roll your own GGUF / MLX / AWQ quants or fine-tune straight from the master. 🎉
A community member spotted that this model was reporting only a 131K context window. That turned out to be
the well-known upstream Gemma 4 metadata bug — Google's initial config.json shipped with
max_position_embeddings: 131072 instead of the real 262144 (256K), and that value got baked into a lot of
downstream finetunes and quants (including this one) before it was fixed upstream.
The weights were always fine — it was purely a metadata field. All GGUF quants have been re-patched to the
full 256K context (gemma4.context_length = 262144). Just re-download if you grabbed an earlier copy. 🙏
📚 Training data (the interesting part 🍳)
This is a distillation of two complementary chain-of-thought sources, both over verifiable Python coding tasks
(algorithmic / function-level problems that come with deterministic tests):
🥇 Main set — Composer 2.5 real CoT. Genuine, model-authored reasoning traces. The teacher solved each problem,
its code was run against the task's tests, and only the passing solutions were kept. So the reasoning you're
learning from leads to code that actually works.
🥈 Aux set — Fable 5 (released today! 🎉). A clever twist: we took the problems where Composer 2.5 got it wrong
and handed them to Fable 5 to redo — re-deriving a fresh, self-consistent chain-of-thought and a correct
solution, again gated on passing the tests. This recovers the hard cases the main teacher missed. These traces
are synthetic (rationalized CoT), and are tagged separately so the two sources stay distinguishable.
The recipe: real CoT for the bulk of solid coverage, plus synthetic "second-attempt" CoT to patch the failures —
both verified by execution before anything entered training. ✅
📦 Pick your size (GGUF quants)
Quant
Size
Vibe
🟢 Q2_K
4.5 GB
tiniest — runs almost anywhere
🟡 Q3_K_M
5.7 GB
great for 8 GB VRAM — much better than Q2
🔵 Q4_K_M
6.87 GB
the sweet spot 👌 (recommended)
🟣 Q6_K
9.11 GB
near-lossless
⚪ Q8_0
11.8 GB
basically full quality
🧮 "Will it fit?" — context length cheat-sheet
Rough estimates 🤓 (assumes q8_0 KV cache + ~1.5 GB overhead; use q4_0 KV cache for ≈2× more context!).
Max context is 256K. "—" = won't fit, pick a smaller quant. ✂️
Your VRAM / unified mem
🟢 Q2_K (4.5G)
🟡 Q3_K_M (5.7G)
🔵 Q4_K_M (6.87G)
🟣 Q6_K (9.11G)
⚪ Q8_0 (11.8G)
8 GB
~16K ctx
~10K
tight (~2–4K)
—
—
12 GB
~48K
~38K
~30K
~12K
—
16 GB
~80K
~72K
~64K
~44K
~22K
24 GB
~200K
~160K
~128K
~110K
~88K
32 GB
256K (max) 🎉
256K
256K
~230K
~190K
💡 Apple Silicon / integrated GPUs with unified memory count too — same numbers, just slower than a dGPU.
💡 Low on room? Drop a quant or switch KV cache to q4_0 and your context roughly doubles.
🚀 How to run it (super easy)
Option A — llama.cpp (recommended) 🦙
Grab a quant above (e.g. …-Q4_K_M.gguf) and llama-server from llama.cpp.
⚠️ Needs a recent llama.cpp (this is the gemma4_unified architecture — older builds won't load it).
Run a server (Windows .bat shown — tweak --port, --ctx-size to taste):
Open http://localhost:18080 and chat. 🎉 (Tip: bump --ctx-size per the table; use q4_0 KV for more.)
Option B — one-click apps 🖱️
Works in LM Studio, Jan, Ollama, etc. — just import the GGUF, pick your quant, go. 🐾
🧠 Thinking mode
This model thinks in Gemma's native thought channel before answering — exactly how it was trained. Keep
enable_thinking=true (the default chat template handles it). Recommended sampling: temp 1.0, top_p 0.95, top_k 64.
For coding you can also go greedy (temp 0) for more deterministic solutions.
⚠️ Good to know
Reduced refusals: the training data is task-focused with no safety hedging, so this refuses less than the base
model. It is not safety-aligned — add your own guardrails for production. Use responsibly. 🙏
Specialized for Python / algorithmic coding. Reasoning quality is strongest in that domain; general-knowledge
facts/numbers should still be double-checked.
English-centric.
📚 Base & License
License: Apache 2.0. Gemma 4 is released by Google under
Apache 2.0 (unlike the older Gemma 1/2/3 terms), so this fine-tune is
Apache 2.0 too — free to use, modify, and redistribute. 🎉
