---
license: llama3.2
tags:
- quantization
- 2-bit
- llama
- qat
pretty_name: Trellis vs Scalar 2-Bit QAT — Single-Layer Snapshots
---

# Single-Layer 2-Bit QAT Weight Snapshots

Per-layer 2-bit quantized weight snapshots for the controlled trellis-vs-scalar
comparison in **"Small GPU, Small Edge: A Controlled Study of Trellis vs. Scalar
2-Bit QAT."** These reproduce **Tables 1, 3, and 4** of the paper.

Built on **meta-llama/Llama-3.2-1B** and subject to the **Llama 3.2 Community
License**. Each snapshot quantizes a *single* decoder layer's 7 projections to
2 bits; all other layers stay FP16. **These are experiment artifacts, not a
deployable model.**

## Contents
| file | layer | arm | init → train |
|---|---|---|---|
| `snapshots/layer04_bcjr_seed{0,1,2}.pt` | 4 (mild) | BCJR-trellis (soft) | PTQ → 10-step BCJR |
| `snapshots/layer04_ste_trellis_seed0.pt` | 4 | STE-trellis | FP → 10-step STE |
| `snapshots/layer04_scalar_ctrl_seed0.pt` | 4 | scalar-ctrl (uniform) | FP → 10-step STE |
| `snapshots/layer01_bcjr_seed0.pt` | 1 (sensitive) | BCJR-trellis | PTQ → 10-step BCJR |
| `snapshots/layer01_ste_trellis_seed0.pt` | 1 | STE-trellis | FP → 10-step STE |
| `snapshots/layer01_scalar_ctrl_seed0.pt` | 1 | scalar-ctrl | FP → 10-step STE |

The trellis-PTQ and scalar-PTQ arms are **not** included — they carry no training
and are recomputed on the fly from the FP weights.

## Format
Each `.pt` is a `torch.save` dict `{proj_name: W_q}` (fp32/bf16, original
un-rotated basis), with keys `attn_{q,k,v,o}_proj` and `mlp_{gate,up,down}_proj`.

## Usage (reproduce the tables)
```python
import torch
from transformers import AutoModelForCausalLM
from src.qat.eval_llama_layer import install_layer_weights   # from the code repo

m = AutoModelForCausalLM.from_pretrained(
    "meta-llama/Llama-3.2-1B", torch_dtype=torch.bfloat16, device_map="cuda")
snap = torch.load("snapshots/layer04_bcjr_seed0.pt", weights_only=True)
install_layer_weights(m, 4, snap, dtype=torch.bfloat16)
# then eval WikiText-2 PPL / lm-eval 0-shot -> Tables 1, 3, 4
```

- **Code:** https://github.com/Venugopalan2610/quant-2bit
- **Paper:** *Small GPU, Small Edge* (arXiv link TBD)