Zen Eco 4B Thinking

Chain-of-thought reasoning model. Part of the Zen Eco family.

Overview

Zen Eco 4B Thinking is the chain-of-thought reasoning variant of Zen Eco 4B. Trained to decompose complex problems into step-by-step reasoning chains, achieving stronger performance on math, logic, and multi-step tasks.

Property	Value
Parameters	4B
Context	32K
License	Apache 2.0

Usage

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("zenlm/zen-eco-thinking")
tokenizer = AutoTokenizer.from_pretrained("zenlm/zen-eco-thinking")

messages = [{"role": "user", "content": "Solve step by step: If a train travels 120km in 2 hours, then slows to cover 90km in 3 hours, what is the average speed for the entire trip?"}]
inputs = tokenizer.apply_chat_template(messages, return_tensors="pt")
output = model.generate(inputs, max_new_tokens=1024)
print(tokenizer.decode(output[0], skip_special_tokens=True))

Related

• zen-eco — Base 4B model
• zen-eco-instruct — Instruction-tuned variant
• zen-eco-agent — Tool-calling variant
• Zen LM — Full model family

Apache 2.0 · Zen LM · Hanzo AI