Gemma-4 12B Coder — SFT v5 + abliterated (GGUF)

Uncensored gemma-4 12B coder for local, agentic tool use — GGUF quantizations for llama.cpp / Ollama.

Run it: llama-server -hf tpls/gemma-4-12B-coder-fable5-composer2.5-v1-sft-v5-abliterated-GGUF:Q4_K_M --jinja (full commands below).

⚠️ Tool-calling needs the recovery shim. The model emits gemma-4's native tool markup, which llama.cpp --jinja under-parses — wrap your endpoint with the tool-shim (see Tool-calling below) to get standard tool_calls.

💡 Pick this to run locally with the best of both: SFT v5's tool-calling and an uncensored model. Our KL-guarded abliteration on top of SFT v5 — gate SHIM 8/8.

At a glance

Use it

# llama.cpp (server) — tool-calling needs the recovery shim, see below
llama-server -hf tpls/gemma-4-12B-coder-fable5-composer2.5-v1-sft-v5-abliterated-GGUF:Q4_K_M --jinja --ctx-size 16384

# Ollama
ollama run hf.co/tpls/gemma-4-12B-coder-fable5-composer2.5-v1-sft-v5-abliterated-GGUF:Q4_K_M

Files

Sizes and a one-click loader are in the file browser / Quantizations widget above; the note says which quant to reach for.

Tool-calling

Tool-calling works — but llama.cpp --jinja doesn't recognise gemma-4's native tool-call markup, so the bare parser under-reports calls. The model is fine; the parser is blind to the format. Recover standard tool_calls with a small serve-side post-processor (no weight change, no latency beyond a regex scan).

Ready-to-use → tpls/gemma4-tool-shim — a drop-in callback for OpenAI-compatible proxies, a standalone (dependency-free) example, and the pure parser, all Apache-2.0, with the full recovery algorithm documented. Point your OpenAI-compatible endpoint through it.

You send tools the usual OpenAI way (tools=[…]); the model emits native markup; the shim turns it into a standard tool_calls object:

# model completion (raw):
<|tool_call>get_weather{"city": "Paris", "units": "celsius"}

// after the shim:
{"finish_reason": "tool_calls",
 "message": {"role": "assistant", "content": null,
   "tool_calls": [{"id": "call_0", "type": "function",
     "function": {"name": "get_weather", "arguments": "{\"city\": \"Paris\", \"units\": \"celsius\"}"}}]}}

Tool-calling gate

Measured 2026-06-26 on the tool-eval suite (7 tool cases + 1 abstain), the Q4_K_M quant served on llama.cpp --jinja with TOOLS_IN_PROMPT=1 at temp 0. raw = native parser; shim = the prod gemma_tool_parse recovery path. SHIM 8/8 = identical to clean SFT v5: abliteration preserved tool-calling (KL=0.0009). The low raw is gemma-4's known native-parser breakage (the shim is the prod path), not abliteration damage.

The rows are this model under two parse paths (raw and shim); the shim path is how it's served in production.

Intended use & limitations

Built for code generation and agentic tool use; serve locally via llama.cpp / Ollama, or use as a base to fine-tune / merge / quantize. Outputs can be wrong or fabricated — validate tool arguments before executing, and keep a human in the loop for anything consequential.

⚠️ Uncensored. For this variant the refusal direction has been ablated from the weights — safety guardrails are substantially removed and it will attempt requests a stock model would refuse. You are responsible for what you generate and how it's used; not suitable where refusal behaviour is itself a safety requirement.

Where this sits in the family

• base (upstream) — yuxinlu1/gemma-4-12B-coder-fable5-composer2.5-v1
  • SFT v5 (weights)
    • SFT v5 + abliterated (weights)
      • SFT v5 + abliterated (GGUF) ← you are here
    • SFT v5 (GGUF)

Provenance & reproduction

How this model was built — technique chain, training mix, and the exact knobs/pins, so the result is reproducible without any of our tooling.

Mechanics applied

1. sft-qlora

• tools_mode: mixed

2. abliteration

weight ablation degrades the canonical <|tool_call> token — the model tends to leak calls as text markup, so the native llama.cpp parser may not fire. See the tool-call recovery note below to get structured calls back.

3. imatrix-quant

• calibration: code + tool-call markup
• embed/output: kept at f16 (protects tool-call logits)
• eog_patch: tokens 105/106 → EOG (bounds the <|turn> runaway)

4. tool-shim

• where: a thin pre/post wrapper on the OpenAI-compatible endpoint
• format: re-parse <|tool_call>NAME{json-args} (and leaked <|tool>…) into tool_calls

serve-side only — does not modify the weights; recommended for abliterated variants.

Training data, hyperparameters & environment

The supervised fine-tune (sft-qlora step above) is inherited from Gemma-4 12B Coder — SFT v5 (weights) — see that card for the full training mix, exact hyperparameters, and pinned environment. The remaining step(s) above are what this model adds on top; their measured effect is below.

Quantization environment

The GGUF bytes depend on the quantizer build, not just the weights — a different llama.cpp release rounds tensors differently and can change the convert mapping. Pins the toolchain these quants were produced with:

The image is the rolling :full tag, not a digest — for byte-exact reproduction pin the image digest you build with. The imatrix-quant step above lists the calibration set and the EOG patch this build applied.

Other measured metrics

Part of the Gemma-4 12B Coder — active collection.

Something not right, or a request? Open a discussion — happy to help.