AtlaAI/Selene-1-Mini-Llama-3.1-8B-GPTQ-W8A8
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初始化项目，由ModelHub XC社区提供模型

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Model: AtlaAI/Selene-1-Mini-Llama-3.1-8B-GPTQ-W8A8
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---
base_model: AtlaAI/Selene-1-Mini-Llama-3.1-8B
library_name: transformers
language:
- en
- de
- fr
- it
- pt
- es
pipeline_tag: text-generation
tags:
- llama
- atla
- evaluation
- llm-as-a-judge
- meta
- conversational
- lm-judge
- llama-cpp
- gptq
license: llama3.1
---
<p align="center">
<picture>
<source
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<img
src="https://atla-ai.notion.site/image/attachment%3A230448e8-921f-45df-b2af-a3158b6c04cd%3Aatla_black2x.png?table=block&id=188309d1-7745-805c-87e4-c39ca54d598d&spaceId=f08e6e70-73af-4363-9621-90e906b92ebc&width=2000&userId=&cache=v2"
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</picture>
</p>
<p align="center">
📄 <a href="https://huggingface.co/spaces/AtlaAI/selene-1-mini-tech-report">Technical report</a> |
💻 <a href="https://github.com/atla-ai/selene-mini">GitHub</a> |
👀 <a href="https://www.atla-ai.com/" style="background-image: linear-gradient(to right, red, orange, yellow, green, blue, indigo, violet); -webkit-background-clip: text; color: transparent; animation: rainbow 5s ease infinite; text-decoration: underline; text-decoration-color: currentColor;">Atla agent evals</a>
</p>
<style>
@keyframes rainbow {
0% { background-position: 0% 50%; }
50% { background-position: 100% 50%; }
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}
</style>
<style>
@keyframes rainbow {
0% { background-position: 0% 50%; }
50% { background-position: 100% 50%; }
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}
</style>
# AtlaAI/Selene-1-Mini-Llama-3.1-8B-GPTQ-W8A8
This model was quantised into an **8-bit** (W8A8) format using GPTQ and SmoothQuant from [`AtlaAI/Selene-1-Mini-Llama-3.1-8B`](https://huggingface.co/AtlaAI/Selene-1-Mini-Llama-3.1-8B).
This was done using vLLM's llm-compressor library (https://docs.vllm.ai/en/stable/features/quantization/int8.html)
Refer to the [original model card](https://huggingface.co/AtlaAI/Selene-1-Mini-Llama-3.1-8B) for more details on the model.
This quantisation was calibrated using a sample of 512 datapoints from the data used to train Selene-1-Mini.
As a result, our quantised models show minimal performance degradation, losing <0.5% overall across benchmarks!
For reference, a GPTQ quantized 8-bit [Llama-3.1-8B](neuralmagic/Meta-Llama-3.1-8B-quantized.w8a8) shows ~1.5% degradation across benchmarks.
![image/png](https://cdn-uploads.huggingface.co/production/uploads/633c4fb100732349209f2aad/K-455q4bMkd11-0P4XSdc.png)