tripplet-research/suzhou3.2

Suzhou 3.2

A 12 billion parameter instruction-tuned language model by Triplet Research. Suzhou 3.2 is a weighted merge of Suzhou 3.1 and Qwen2.5-3B, designed to improve reasoning and math capabilities.

Merge Details

• Method: Weighted blending (70% Suzhou 3.1 + 30% Qwen2.5-3B)
• Model A: Suzhou 3.1 - strong agent/tool-use, reasoning
• Model B: Qwen2.5-3B-Instruct - math reasoning, general knowledge
• Target: 12B parameters

Key Features

• 12B parameters
• 262K context window
• Strong reasoning and chain-of-thought capabilities
• Tool calling and agent support
• Multilingual support (29+ languages)
• Mixed attention architecture (linear + full attention layers)

Architecture

• Type: Causal Language Model
• Architecture: Qwen3.5 Text
• Layers: 32
• Parameters: 12B

Safetensors

• 12B parameters

Quickstart

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("Triplet-Research/suzhou-3.2")
tokenizer = AutoTokenizer.from_pretrained("Triplet-Research/suzhou-3.2")