51 lines
1.5 KiB
Markdown
51 lines
1.5 KiB
Markdown
---
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base_model: meta-llama/Llama-2-7b-hf
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language:
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- en
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license: llama2
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pipeline_tag: text-generation
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library_name: transformers
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tags:
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- kronq
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- quantization
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- group-quantization
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- fake-quant
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- fp16
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---
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# Llama-2-7b — KronQ W3A16 g128 (fake-quant fp16)
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**Paper:** [arXiv:2607.07964](https://arxiv.org/abs/2607.07964) · **Code:** [GitHub](https://github.com/Intelligent-Computing-Lab-Panda/KronQ)
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> ⚠️ **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.
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[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.
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## Results (WikiText-2, seqlen 2048)
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**Perplexity:** **5.773**
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**Zero-shot accuracy:**
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| PIQA | ARC-E | ARC-C | HellaSwag | WinoGrande | BoolQ | OBQA | Average |
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|---|---|---|---|---|---|---|---|
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| 77.48 | 72.01 | 43.43 | 73.32 | 67.40 | 75.11 | 40.80 | **64.22** |
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(lm-evaluation-harness 0-shot.)
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## Usage
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Loads as a **standard fp16 model** (no KronQ code):
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```python
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from transformers import AutoModelForCausalLM
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m = AutoModelForCausalLM.from_pretrained("donghyunli/Llama-2-7b-KronQ-W3A16-g128-fake", torch_dtype="float16", device_map="auto")
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```
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## Recipe
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Group-128 asymmetric W3, weight-only, `--alpha 0.25`, `--act_order`, BiIP, raw H_G.
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## License
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Derivative of Llama-2-7b — [llama2 license](https://ai.meta.com/llama/license/). |