---
base_model: meta-llama/Meta-Llama-3-8B
language:
- en
license: llama3
pipeline_tag: text-generation
library_name: transformers
tags:
- kronq
- quantization
- fake-quant
- fp16
---

# Meta-Llama-3-8B — KronQ W3A16 (fake-quant fp16)

**Paper:** [arXiv:2607.07964](https://arxiv.org/abs/2607.07964) · **Code:** [GitHub](https://github.com/Intelligent-Computing-Lab-Panda/KronQ)

> ⚠️ **Fake-quant fp16 checkpoint.** The 3-bit weights are stored in **fp16**
> (KronQ does not pack int3), so this repo is the **same size as bf16** — no
> compression or speedup, for **PPL / accuracy reproduction only**. For
> deployable low-bit, see the W4A16 (packed int4) repo.

[Meta-Llama-3-8B](https://huggingface.co/meta-llama/Meta-Llama-3-8B) quantized to 3-bit weights with **KronQ** (Kronecker-factored Hessian quantization), exported as a standard fp16 model.

## Results (WikiText-2, seqlen 2048)

**Perplexity:** **7.09**

**Zero-shot accuracy:**

| PIQA | ARC-E | ARC-C | HellaSwag | WinoGrande | BoolQ | OBQA | Average |
|---|---|---|---|---|---|---|---|
| 77.53 | 74.54 | 50.17 | 74.92 | 71.74 | 81.13 | 41.20 | **67.32** |

(lm-evaluation-harness, 0-shot. `acc_norm` for PIQA/HellaSwag/ARC/OBQA, `acc` for WinoGrande/BoolQ.)

## Usage

Loads as a **standard fp16 model** — no KronQ code needed:

```python
from transformers import AutoModelForCausalLM, AutoTokenizer
m = AutoModelForCausalLM.from_pretrained("donghyunli/Meta-Llama-3-8B-KronQ-W3A16-fake", torch_dtype="float16").cuda()
tok = AutoTokenizer.from_pretrained("donghyunli/Meta-Llama-3-8B-KronQ-W3A16-fake")
```

## Recipe

Per-channel asymmetric W3, weight-only (a_bits=16), `--alpha 0.25`, bidirectional incoherence processing (BiIP), `act_order`, raw H_G. Calibrated on 128 WikiText-2 sequences.

## License

Derivative of Meta-Llama-3-8B — subject to the [Llama 3 Community License](https://llama.meta.com/llama3/license/).