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Model: Entrit/Llama-3.1-8B-trit-uniform-d4
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---
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
```

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{
"architectures": [
"LlamaForCausalLM"
],
"attention_bias": false,
"attention_dropout": 0.0,
"bos_token_id": 128000,
"dtype": "float16",
"eos_token_id": 128001,
"head_dim": 128,
"hidden_act": "silu",
"hidden_size": 4096,
"initializer_range": 0.02,
"intermediate_size": 14336,
"max_position_embeddings": 131072,
"mlp_bias": false,
"model_type": "llama",
"num_attention_heads": 32,
"num_hidden_layers": 32,
"num_key_value_heads": 8,
"pad_token_id": null,
"pretraining_tp": 1,
"rms_norm_eps": 1e-05,
"rope_parameters": {
"factor": 8.0,
"high_freq_factor": 4.0,
"low_freq_factor": 1.0,
"original_max_position_embeddings": 8192,
"rope_theta": 500000.0,
"rope_type": "llama3"
},
"tie_word_embeddings": false,
"transformers_version": "5.5.3",
"use_cache": true,
"vocab_size": 128256
}

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{
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"bos_token_id": 128000,
"do_sample": true,
"eos_token_id": 128001,
"temperature": 0.6,
"top_p": 0.9,
"transformers_version": "5.5.3"
}

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{
"backend": "tokenizers",
"bos_token": "<|begin_of_text|>",
"clean_up_tokenization_spaces": true,
"eos_token": "<|end_of_text|>",
"is_local": true,
"model_input_names": [
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"attention_mask"
],
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"pad_token": "<|end_of_text|>",
"tokenizer_class": "TokenizersBackend"
}