tpls/gemma-4-12B-coder-fable5-composer2.5-v1-sft-v5-GGUF

Gemma-4 12B Coder — SFT v5 (GGUF)

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-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 for the best tool-calling (our gate winner). For an uncensored model, use SFT v5 + abliterated GGUF.

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-GGUF:Q4_K_M --jinja --ctx-size 16384

# Ollama
ollama run hf.co/tpls/gemma-4-12B-coder-fable5-composer2.5-v1-sft-v5-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`](https://huggingface.co/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

Served the GGUF on llama.cpp (llama-server --jinja), prompted 7 tool-use cases + 1 no-tool abstain, scored whether a structured tool call was emitted. raw = llama.cpp native parse; shim = same outputs re-parsed for gemma-4 native markup. Tools folded into the prompt at eval time, matching training.

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.

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)
• —SFT v5 (GGUF) ← you are here

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. 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)

3. 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 & mix

Public sources; weights/row-caps are the exact balance.

Pinned revisions (byte-exact reproduction):

• —Agent-Ark/Toucan-1.5M (Kimi-K2) @ 0df3cf37f2abefb380370cfb02eabea2a35ae782
• —Nanbeige/ToolMind (graphsyndatasets/graphsyn.jsonl) @ 8020ed1c03c367e4eb720ac3828ab4b0b95d8baf
• —NousResearch/hermes-function-calling-v1 (func-calling.json) @ dae3e1d28cfbcf4b915c04ea1e072030529b4bda
• —NousResearch/hermes-function-calling-v1 (func-calling-singleturn.json) @ dae3e1d28cfbcf4b915c04ea1e072030529b4bda
• —Salesforce/xlam-function-calling-60k (toolsmode=mixed, toolsratio=0.5 (schemas folded into ~half the prompts)) @ 26d14ebfe18b1f7b524bd39b404b50af5dc97866

Training hyperparameters

Training environment

Exact pins the run trained against (the base arch needs a recent transformers).

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.

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

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