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Model: donghyunli/Llama-2-7b-KronQ-W3A16-g128-fake
Source: Original Platform

README.md

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+---
+base_model: meta-llama/Llama-2-7b-hf
+language:
+- en
+license: llama2
+pipeline_tag: text-generation
+library_name: transformers
+tags:
+- kronq
+- quantization
+- group-quantization
+- fake-quant
+- fp16
+---
+
+# Llama-2-7b — 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.
+
+[Llama-2-7b](https://huggingface.co/meta-llama/Llama-2-7b-hf) quantized to 3-bit weights (group 128) with **KronQ**, exported as a standard fp16 model.
+
+## Results (WikiText-2, seqlen 2048)
+
+**Perplexity:** **5.773**
+
+**Zero-shot accuracy:**
+
+| PIQA | ARC-E | ARC-C | HellaSwag | WinoGrande | BoolQ | OBQA | Average |
+|---|---|---|---|---|---|---|---|
+| 77.48 | 72.01 | 43.43 | 73.32 | 67.40 | 75.11 | 40.80 | **64.22** |
+
+(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/Llama-2-7b-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 Llama-2-7b — [llama2 license](https://ai.meta.com/llama/license/).