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Model: donghyunli/Meta-Llama-3-8B-KronQ-W3A16-g128-fake
Source: Original Platform

README.md

@@ -0,0 +1,51 @@
+---
+base_model: meta-llama/Meta-Llama-3-8B
+language:
+- en
+license: llama3
+pipeline_tag: text-generation
+library_name: transformers
+tags:
+- kronq
+- quantization
+- group-quantization
+- fake-quant
+- fp16
+---
+
+# Meta-Llama-3-8B — KronQ W3A16 g128 (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.** 3-bit group-128 weights stored in **fp16** (KronQ does not pack int3) — **same size as bf16**, for PPL/accuracy reproduction. For deployable low-bit see the W4A16-g128 / W2A16-g128 (packed) repos.
+
+[Meta-Llama-3-8B](https://huggingface.co/meta-llama/Meta-Llama-3-8B) quantized to 3-bit weights (group 128) with **KronQ**, exported as a standard fp16 model.
+
+## Results (WikiText-2, seqlen 2048)
+
+**Perplexity:** **6.959**
+
+**Zero-shot accuracy:**
+
+| PIQA | ARC-E | ARC-C | HellaSwag | WinoGrande | BoolQ | OBQA | Average |
+|---|---|---|---|---|---|---|---|
+| 78.67 | 74.20 | 49.06 | 74.92 | 71.67 | 81.68 | 42.40 | **67.51** |
+
+(lm-evaluation-harness 0-shot.)
+
+## Usage
+
+Loads as a **standard fp16 model** (no KronQ code):
+
+```python
+from transformers import AutoModelForCausalLM
+m = AutoModelForCausalLM.from_pretrained("donghyunli/Meta-Llama-3-8B-KronQ-W3A16-g128-fake", torch_dtype="float16", device_map="auto")
+```
+
+## Recipe
+
+Group-128 asymmetric W3, weight-only, `--alpha 0.25`, `--act_order`, BiIP, raw H_G.
+
+## License
+
+Derivative of Meta-Llama-3-8B — [llama3 license](https://llama.meta.com/llama3/license/).