base_model: tpls/gemma-4-12B-coder-fable5-composer2.5-v1-abliterated-sft-v5
base_model_relation: quantized
library_name: gguf
pipeline_tag: text-generation
license: gemma
quantized_by: triplezrobotics
tags:
- gguf
- llama.cpp
- gemma4
- abliterated
- code
gemma-4-12B-coder-fable5-composer2.5-v1-abliterated-sft-v5-GGUF
GGUF quantizations of tpls/gemma-4-12B-coder-fable5-composer2.5-v1-abliterated-sft-v5.
Quantized with llama.cpp using an importance matrix (imatrix) calibrated on
code + tool-call markup, with token-embedding and output tensors kept at f16
to protect the tool-call token logits, plus a gemma-4 EOG patch (eot=106) to
bound the <|turn> runaway.
Files
| Quant | File | Notes |
|---|---|---|
| Q4_K_M | gemma-4-12B-coder-fable5-composer2.5-v1-abliterated-sft-Q4_K_M.gguf |
good default — fits 12 GB VRAM, best size/quality balance |
Use it
# llama.cpp (server, with native tool parsing)
llama-server -hf tpls/gemma-4-12B-coder-fable5-composer2.5-v1-abliterated-sft-v5-GGUF:Q4_K_M --jinja --ctx-size 16384
# Ollama
ollama run hf.co/tpls/gemma-4-12B-coder-fable5-composer2.5-v1-abliterated-sft-v5-GGUF:Q4_K_M
⚠️ Tool-calling note (abliterated weights)
The base model and fine-tune emit canonical tool_calls that llama.cpp --jinja
parses natively. Abliteration degrades the canonical tool token: this model
tends to leak its calls as text markup (<|tool>NAME{...}) instead of the
canonical <|tool_call> token, so the native parser may not fire.
Two ways to get structured tool_calls back:
Runtime shim (works now). Put a tolerant recovery shim in front via LiteLLM:
# litellm config model_list: - model_name: gemma-coder litellm_params: model: openai/gemma-coder api_base: http://127.0.0.1:8096/v1 api_key: sk-none litellm_settings: callbacks: ["gemma_tool_shim.shim"] # recovers tool_calls from leaked markup drop_params: trueThe shim is published with the source at the repo that produced this model.
Native (no shim). Use a control-vector / activation-steered variant
instead of weight-ablation — steering uncensors at inference without editing
the weights, so the canonical tool token survives and bare--jinjaworks.