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1.6 KiB
1.6 KiB
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