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Model: Entrit/Qwen2.5-32B-trit-uniform-d2
Source: Original Platform

README.md

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+---
+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.