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

Gemma-4 12B Coder — SFT v5 (weights)

gemma-4 12B coder weights (safetensors) — for fine-tuning, merging, or quantizing.

Ready-to-serve GGUF quants: tpls/gemma-4-12B-coder-fable5-composer2.5-v1-sft-v5-GGUF.

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

At a glance

Type	Model weights (safetensors)
Techniques	sft-qlora
Tool-calling	✅ 100% gate pass (recovery-shim path)
Status	✅ Active / supported
Use	GGUF quants: tpls/gemma-4-12B-coder-fable5-composer2.5-v1-sft-v5-GGUF

Use it — GGUF quantizations

Ready-to-serve GGUF quants live at tpls/gemma-4-12B-coder-fable5-composer2.5-v1-sft-v5-GGUF (llama.cpp / Ollama one-liners on that card). These are the safetensors weights, for fine-tuning / merging / quantizing.

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) ← you are here
    • SFT v5 + abliterated (weights)
      • SFT v5 + abliterated (GGUF)
    • 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

Step	Technique	What it does	Provenance
1	sft-qlora	QLoRA supervised fine-tune to keep + improve native tool-calling	—

1. sft-qlora

• tools_mode: mixed (xLAM schemas folded; conditional taught)

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)

---

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

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