---
license: llama3.1
license_name: llama3.1
license_link: https://huggingface.co/meta-llama/Llama-3.1-8B/blob/main/LICENSE
base_model: meta-llama/Llama-3.1-8B
tags:
  - quantization
  - ternary
  - balanced-ternary
  - tritllm
  - llama
  - llama-3.1
library_name: transformers
extra_gated_description: This model is a quantized derivative of Meta Llama 3.1. By accessing this model you agree to the Llama 3.1 Community License and the Meta Acceptable Use Policy.
---

# Llama-3.1-8B-trit-uniform-d4

**Built with Llama.** Balanced ternary quantization of [`meta-llama/Llama-3.1-8B`](https://huggingface.co/meta-llama/Llama-3.1-8B) at depth **d=4** (81 levels per weight, **6.64 bits per weight**). Distributed under the [Llama 3.1 Community License Agreement](https://huggingface.co/meta-llama/Llama-3.1-8B/blob/main/LICENSE) and subject to Meta's [Acceptable Use Policy](https://www.llama.com/llama3_1/use-policy).

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

## Quick load

```python
from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("Entrit/Llama-3.1-8B-trit-uniform-d4")
tokenizer = AutoTokenizer.from_pretrained("Entrit/Llama-3.1-8B-trit-uniform-d4")
```

The weights are dequantized to FP16 for stock-`transformers` compatibility. The on-disk size is therefore the same as the FP16 source. The 6.64-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 | [`meta-llama/Llama-3.1-8B`](https://huggingface.co/meta-llama/Llama-3.1-8B) |
| Depth | d=4 (81 levels) |
| Bits per weight | 6.64 |
| 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 |

## License and use

This is a research artifact. The underlying weights remain governed by the Llama 3.1 Community License Agreement; commercial use is restricted to the terms of that license. By using this model you agree to:

1. Comply with the [Llama 3.1 Community License](https://huggingface.co/meta-llama/Llama-3.1-8B/blob/main/LICENSE).
2. Comply with Meta's [Acceptable Use Policy](https://www.llama.com/llama3_1/use-policy).
3. Display "Built with Llama" attribution if you redistribute or publicly demo derivatives of this model.

## 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 meta-llama/Llama-3.1-8B --configs uniform-d4 --out ./out
```