# TauBench V2 eval: MiniMax-M2.5 (Ollama, UD-TQ1_0 GGUF ~55 GB, GPUs 2-3)
# Multi-turn customer service benchmark -- airline + retail splits
# Pull model: ollama pull hf.co/unsloth/MiniMax-M2.5-GGUF:latest
# NOTE: Q4_K_M and higher quants are sharded GGUFs which Ollama does not
# support yet (see github.com/ollama/ollama/issues/5245). The "latest" tag
# pulls the largest non-sharded quant: UD-TQ1_0 (1-bit, ~55 GB).

[meta]
name = "taubench-minimax-m25"
description = "TauBench V2 on MiniMax-M2.5 UD-TQ1_0 via Ollama"

[defaults]
temperature = 0.7
max_tokens = 4096

[judge]
model = "gpt-5-mini-2025-08-07"
temperature = 0.0
engine = "cloud"

[run]
max_workers = 1
output_dir = "results/neurips-2026/baselines/minimax-m25-ollama/taubench/"
seed = 42

[[models]]
name = "hf.co/unsloth/MiniMax-M2.5-GGUF:latest"
engine = "ollama"
param_count_b = 229.0
active_params_b = 45.6
num_gpus = 2

[[benchmarks]]
name = "taubench"
backend = "jarvis-direct"
max_samples = 20
split = "airline,retail"
