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

Name: gemma-4-12B-coder-fable5-composer2.5-v1-sft-v5-GGUF
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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


Type	GGUF quantizations · llama.cpp / Ollama
Techniques	`sft-qlora` → `imatrix-quant` → `tool-shim`
Tool-calling	✅ 100% gate pass (recovery-shim path)
Status	✅ Active / supported
Use	`llama-server -hf tpls/gemma-4-12B-coder-fable5-composer2.5-v1-sft-v5-GGUF:Q4_K_M --jinja`

Use it

bash

# 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.

Quant	Notes
`Q4_K_M`	good default — fits 12 GB VRAM, best size/quality balance

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"}

json

// 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.

Measured on	Pass rate
this model — raw (`--jinja`)	0.125
this model — shim (prod path)	1.000

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

Step	Technique	What it does	Provenance
1	`sft-qlora`	QLoRA supervised fine-tune to keep + improve native tool-calling	—
2	`imatrix-quant`	llama.cpp quantization with an importance matrix (imatrix)	—
3	`tool-shim`	serve-side recovery of structured tool_calls from the model's native markup	—

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.

Dataset	Subset	Role	Weight	Max rows
`Agent-Ark/Toucan-1.5M`	Kimi-K2	tool-dense multiturn	2.0	1000
`Nanbeige/ToolMind`	graph_syn_datasets/graphsyn.jsonl	reasoning-heavy (down-weighted vs v4 — the v4 culprit)	1.0	500
`NousResearch/hermes-function-calling-v1`	func-calling.json	multiturn function calling	1.5	600
`NousResearch/hermes-function-calling-v1`	func-calling-singleturn.json	terse single-call (up vs v4)	2.0	600
`Salesforce/xlam-function-calling-60k`	tools_mode=mixed, tools_ratio=0.5 (schemas folded into ~half the prompts)	terse, verifiable single-call (up vs v4)	2.0	600

Pinned revisions (byte-exact reproduction):

Agent-Ark/Toucan-1.5M (Kimi-K2) @ 0df3cf37f2abefb380370cfb02eabea2a35ae782
Nanbeige/ToolMind (graph_syn_datasets/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 (tools_mode=mixed, tools_ratio=0.5 (schemas folded into ~half the prompts)) @ 26d14ebfe18b1f7b524bd39b404b50af5dc97866

Training hyperparameters

Knob	Value
method	QLoRA (4-bit NF4 base, bf16 compute)
lora_r / lora_alpha / dropout	32 / 32 / 0.05
target_modules	all-linear
objective	train on assistant turns only (responses-only masking)
optimizer	adamw_8bit
lr / scheduler / warmup	2e-4 / cosine / 0.03
epochs	1
seq_len	4096
effective_batch	16
chat_template	gemma (native turn boundaries 105/106)

Training environment

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

Package	Version
torch	2.11.0
transformers	5.13.0.dev0 @ c21da1b (git pin)
peft	0.19.1
trl	1.6.0
datasets	5.0.0
bitsandbytes	0.49.2
accelerate	1.14.0
liger-kernel	0.8.0
attention	eager (no flash-attn)

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:

Step	Tool / setting
quantizer	llama.cpp tools image `ghcr.io/ggml-org/llama.cpp:full`
convert	`convert_hf_to_gguf.py` → f16 GGUF
imatrix	`llama-imatrix` over the calibration set (CPU forward pass)
quantize	`llama-quantize --imatrix`, token-embeddings + output tensor kept at f16

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.

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