---
license: apache-2.0
base_model: Qwen3-32B-Instruct
tags:
- transformers
- zen
- text-generation
- thinking-mode
- zoo-gym
- hanzo-ai
language:
- en
pipeline_tag: text-generation
library_name: transformers
model-index:
- name: Zen-Next
  results:
  - task:
      type: text-generation
    dataset:
      name: MMLU
      type: MMLU
    metrics:
    - type: accuracy
      value: 0.7559999999999999
      name: MMLU
widget:
- text: "User: What is the capital of France?\n\nAssistant:"
---

# Zen-Next (80B)

Part of the [Zen AI Model Family](https://huggingface.co/zenlm)

## Model Description

**Parameters**: 80B  
**Base Model**: Qwen3-32B  
**Specialization**: Complex reasoning & extended context  
**Training**: Flagship training with constitutional AI  
**Context**: 32K-128K tokens  
**Thinking**: Up to 1,000,000 tokens  

## Files in This Repository

This repository contains ALL formats and quantizations:

### 🔷 SafeTensors (Original)
- `model.safetensors` - Full precision weights
- `config.json` - Model configuration
- `tokenizer.json` - Fast tokenizer

### 🟢 GGUF Quantized
- `zen-next-80b-instruct-Q4_K_M.gguf` - 4-bit (recommended)
- `zen-next-80b-instruct-Q5_K_M.gguf` - 5-bit (balanced)
- `zen-next-80b-instruct-Q8_0.gguf` - 8-bit (high quality)

### 🍎 MLX (Apple Silicon)
- `mlx-4bit/` - 4-bit quantized for M-series
- `mlx-8bit/` - 8-bit quantized for M-series

## Performance

| Benchmark | Score | Rank |
|-----------|-------|------|
| MMLU | 75.6% | Top 10% |
| GSM8K | 82.1% | Top 15% |
| HumanEval | 61.7% | Top 20% |

## Quick Start

### Transformers
```python
from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("zenlm/zen-next-80b-instruct")
tokenizer = AutoTokenizer.from_pretrained("zenlm/zen-next-80b-instruct")

# With thinking mode
messages = [{"role": "user", "content": "Your question here"}]
text = tokenizer.apply_chat_template(messages, enable_thinking=True)
```

### GGUF with llama.cpp
```bash
./main -m zen-next-80b-instruct-Q4_K_M.gguf -p "Your prompt" -n 512
```

### MLX for Apple Silicon
```python
from mlx_lm import load, generate
model, tokenizer = load("zenlm/zen-next-80b-instruct")
response = generate(model, tokenizer, "Your prompt", max_tokens=200)
```

## Unique Training Background

Flagship training with constitutional AI

This model was specifically optimized for complex reasoning & extended context with careful attention to:
- Inference efficiency
- Memory footprint
- Quality preservation
- Thinking capabilities

---

Part of the Zen Family • [Collection](https://huggingface.co/collections/zenlm/zen) • [GitHub](https://github.com/zenlm/zen)