Instructions to use yuxinlu1/gemma-4-12B-coder-fable5-composer2.5-v1-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use yuxinlu1/gemma-4-12B-coder-fable5-composer2.5-v1-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="yuxinlu1/gemma-4-12B-coder-fable5-composer2.5-v1-GGUF",
	filename="gemma4-coding-Q2_K.gguf",
)

llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": "What is the capital of France?"
		}
	]
)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use yuxinlu1/gemma-4-12B-coder-fable5-composer2.5-v1-GGUF with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf yuxinlu1/gemma-4-12B-coder-fable5-composer2.5-v1-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf yuxinlu1/gemma-4-12B-coder-fable5-composer2.5-v1-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf yuxinlu1/gemma-4-12B-coder-fable5-composer2.5-v1-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf yuxinlu1/gemma-4-12B-coder-fable5-composer2.5-v1-GGUF:Q4_K_M

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf yuxinlu1/gemma-4-12B-coder-fable5-composer2.5-v1-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf yuxinlu1/gemma-4-12B-coder-fable5-composer2.5-v1-GGUF:Q4_K_M

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf yuxinlu1/gemma-4-12B-coder-fable5-composer2.5-v1-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf yuxinlu1/gemma-4-12B-coder-fable5-composer2.5-v1-GGUF:Q4_K_M

Use Docker

docker model run hf.co/yuxinlu1/gemma-4-12B-coder-fable5-composer2.5-v1-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use yuxinlu1/gemma-4-12B-coder-fable5-composer2.5-v1-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "yuxinlu1/gemma-4-12B-coder-fable5-composer2.5-v1-GGUF"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "yuxinlu1/gemma-4-12B-coder-fable5-composer2.5-v1-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/yuxinlu1/gemma-4-12B-coder-fable5-composer2.5-v1-GGUF:Q4_K_M

Ollama
How to use yuxinlu1/gemma-4-12B-coder-fable5-composer2.5-v1-GGUF with Ollama:
```
ollama run hf.co/yuxinlu1/gemma-4-12B-coder-fable5-composer2.5-v1-GGUF:Q4_K_M
```

Unsloth Studio

How to use yuxinlu1/gemma-4-12B-coder-fable5-composer2.5-v1-GGUF with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for yuxinlu1/gemma-4-12B-coder-fable5-composer2.5-v1-GGUF to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for yuxinlu1/gemma-4-12B-coder-fable5-composer2.5-v1-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://hf-f1c2f441.iring.fun/spaces/unsloth/studio in your browser
# Search for yuxinlu1/gemma-4-12B-coder-fable5-composer2.5-v1-GGUF to start chatting

How to use yuxinlu1/gemma-4-12B-coder-fable5-composer2.5-v1-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf yuxinlu1/gemma-4-12B-coder-fable5-composer2.5-v1-GGUF:Q4_K_M

Configure the model in Pi

# Install Pi:
npm install -g @mariozechner/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "yuxinlu1/gemma-4-12B-coder-fable5-composer2.5-v1-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use yuxinlu1/gemma-4-12B-coder-fable5-composer2.5-v1-GGUF with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf yuxinlu1/gemma-4-12B-coder-fable5-composer2.5-v1-GGUF:Q4_K_M

Configure Hermes

# Install Hermes:
curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash
hermes setup
# Point Hermes at the local server:
hermes config set model.provider custom
hermes config set model.base_url http://127.0.0.1:8080/v1
hermes config set model.default yuxinlu1/gemma-4-12B-coder-fable5-composer2.5-v1-GGUF:Q4_K_M

Run Hermes

hermes

Atomic Chat new
Docker Model Runner
How to use yuxinlu1/gemma-4-12B-coder-fable5-composer2.5-v1-GGUF with Docker Model Runner:
```
docker model run hf.co/yuxinlu1/gemma-4-12B-coder-fable5-composer2.5-v1-GGUF:Q4_K_M
```

Lemonade

How to use yuxinlu1/gemma-4-12B-coder-fable5-composer2.5-v1-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull yuxinlu1/gemma-4-12B-coder-fable5-composer2.5-v1-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.gemma-4-12B-coder-fable5-composer2.5-v1-GGUF-Q4_K_M

List all available models

lemonade list

💻 Gemma4-12B-Coder (GGUF) — Composer 2.5 × Fable 5 ✨

🐣 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. 💚

📚 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. ✅

🗺️ Roadmap — v2 (if there's interest! 💚)

This is v1. If the likes / downloads add up, I'll ship a v2 that pushes for the benchmarks 🏁.

📢 Update on v2 & the Fable 5 situation (2026-06-14)

Quick heads-up for everyone waiting on v2:

Fable 5 access has been pulled. The Fable 5 CoT data I managed to save beforehand is honestly a pretty small set — not enough on its own to act as the primary signal for v2 without risking overfitting. So the plan is shifting:

v2 will lean more heavily on Composer 2.5 verifiable CoT as the backbone (the main, execution-verified source), and use the limited Fable 5 data carefully as a supplement rather than the core.
If Fable 5 access doesn't come back within ~a week, I'm considering bringing in GLM-5.2 as an additional teacher. I just went through the benchmarks: per BridgeMind's eval posted on X, GLM-5.2 actually edges out Fable 5 on both the BS and reasoning leaderboards. I haven't tested it hands-on myself yet — my gut says it'll land slightly below Fable 5 in practice, but likely very close.

Bottom line: v2 is still coming. I'd just rather take a little longer and ship something that generalizes than rush out an overfit model. Thanks for the patience and support 💚

⭐ Like & download if you'd like to see v2 — that's the signal I'm watching!

📦 Pick your size (GGUF quants)

Quant	Size	Vibe
🟢 Q2_K	4.5 GB	tiniest — runs almost anywhere
🔵 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 131K. "—" = won't fit, pick a smaller quant. ✂️

Your VRAM / unified mem	🟢 Q2_K (4.5G)	🔵 Q4_K_M (6.87G)	🟣 Q6_K (9.11G)	⚪ Q8_0 (11.8G)
8 GB	~16K ctx	tight (~2–4K)	—	—
12 GB	~48K	~30K	~12K	—
16 GB	~80K	~64K	~44K	~22K
24 GB	131K (max) 🎉	~128K	~110K	~88K
32 GB	131K	131K	131K	131K

💡 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):

@echo off
cd /d C:\llama.cpp
llama-server.exe ^
  -m C:\models\gemma4-coding-Q4_K_M.gguf ^
  --ctx-size 16384 ^
  --n-gpu-layers 99 ^
  --no-mmap ^
  -fa on ^
  --cache-type-k q8_0 --cache-type-v q8_0 ^
  --temp 1.0 --top-p 0.95 --top-k 64 ^
  --host 0.0.0.0 --port 18080
pause

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

Base model: google/gemma-4-12B-it. Subject to the Gemma Terms of Use (derivatives must comply).
Personal/hobby project — shared as-is, no warranty. Have fun, and happy hacking! 🐾✨

Downloads last month: 20,207

GGUF

Model size

12B params

Architecture

gemma4

Hardware compatibility

2-bit

4-bit

6-bit

8-bit

Model tree for yuxinlu1/gemma-4-12B-coder-fable5-composer2.5-v1-GGUF

Base model

google/gemma-4-12B

Finetuned

google/gemma-4-12B-it

Quantized

(147)

this model

Quantizations

2 models

yuxinlu1
/

gemma-4-12B-coder-fable5-composer2.5-v1-GGUF