---
license: apache-2.0
base_model: Qwen/Qwen2.5-32B
tags:
  - quantization
  - ternary
  - balanced-ternary
  - tritllm
library_name: transformers
---

# Qwen2.5-32B-trit-uniform-d2

Balanced ternary quantization of [`Qwen/Qwen2.5-32B`](https://huggingface.co/Qwen/Qwen2.5-32B) at depth **d=2** (9 levels per weight, **3.47 bits per weight**) via uniform PTQ.

Produced with the codec from **"Balanced Ternary Post-Training Quantization for Large Language Models"** (Stentzel, 2026). See the [Entrit/tritllm-codec](https://huggingface.co/Entrit/tritllm-codec) repository for the codec source and the [paper](https://huggingface.co/Entrit/tritllm-paper) for full evaluation results.

## Quick load

```python
from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("Entrit/Qwen2.5-32B-trit-uniform-d2")
tokenizer = AutoTokenizer.from_pretrained("Entrit/Qwen2.5-32B-trit-uniform-d2")
```

The weights are dequantized to FP16 for stock-`transformers` compatibility. The on-disk size is therefore the same as the FP16 source. The 3.47-bpw figure refers to the *information content* of the quantized matrices and is what matters for inference on hardware that consumes the packed trit format directly (see [Entrit/tritllm-kernel](https://huggingface.co/Entrit/tritllm-kernel)).

## Quantization details

| Field | Value |
|---|---|
| Source model | [`Qwen/Qwen2.5-32B`](https://huggingface.co/Qwen/Qwen2.5-32B) |
| Depth | d=2 (9 levels) |
| Bits per weight | 3.47 |
| Group size | 16 |
| Scale codebook | 27-entry log-spaced (scale_depth=3) |
| Method | uniform PTQ |
| Quantized layers | all 2D linear matrices |
| Kept FP16 | `lm_head`, token embeddings, all `*_norm` layers |
| Codec | tritllm v2 |

## What's quantized vs kept FP16

Following standard quantization-paper convention, only the 2D linear weight matrices are ternary-quantized. The token embedding lookup and final classifier (`lm_head`) stay in FP16. The 3.47-bpw figure is computed over the quantized matrices only, consistent with how GPTQ, AWQ, and NF4 report BPW.

## Citation

```
@article{stentzel2026ternaryptq,
  title  = {Balanced Ternary Post-Training Quantization for Large Language Models},
  author = {Stentzel, Eric},
  year   = 2026,
  note   = {Entrit Systems}
}
```

## Reproducibility

```bash
git clone https://huggingface.co/Entrit/tritllm-codec
cd tritllm-codec
python quantize_model_v2.py --model Qwen/Qwen2.5-32B --configs uniform-d2 --out ./out
```

The output at `./out/uniform-d2/model/` matches this repository.