ClankLabs/Wrench-35B-A3B-Q4_K_M-GGUF

Wrench 35B — Purpose-Built Agentic Model

A LoRA fine-tuned version of Qwen3.5-35B-A3B (MoE, 3B active parameters), purpose-built for tool calling, error recovery, and system prompt following. Runs on 16GB VRAM.

Benchmarks

Clank Benchmark — Category Breakdown

BFCL — Independent Validation

Tested on the Berkeley Function Calling Leaderboard non_live/AST category — 1,390 test cases across 7 categories. This is an independent, standardized benchmark not designed by us.

vs. Frontier Models

Quick Start

Ollama (recommended)

Download the GGUF and Modelfile from the Files tab, then:

ollama create wrench -f Modelfile
ollama run wrench

llama.cpp

./llama-server -m wrench-35B-A3B-Q4_K_M.gguf --jinja -ngl 100 -fa on \
  --temp 0.4 --top-k 20 --top-p 0.95 --min-p 0 --presence-penalty 1.5 -c 32768

With Clank Gateway

npm install -g @clanklabs/clank
clank setup
# Set primary model to "ollama/wrench" in config

Model Details

Training Data

All training data is published and auditable: ClankLabs/wrench-training-data

1,252 examples across 15 categories including tool calling, error recovery, multi-step chains, system prompt following, safety, planning, and frontier-gap targeting (uncertainty calibration, constraint following, strategy revision, long-context multiturn).

Links

• —Wrench 9B — 114/120, runs on 8GB VRAM
• —Training Data
• —Clank Gateway — the AI agent gateway Wrench was built for
• —clanklabs.dev/wrench
• —Benchmark Methodology