---
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
---


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<p align="center">
    🛝 <a href="https://hf.co/spaces/AtlaAI/selene">Playground</a> | 
    📄 <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/sign-up-waitlist?utm_source=huggingface&utm_medium=community&utm_campaign=WL_HF_all_communitypost_sel1minilaunch" 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;">Sign up for the API</a>
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# 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)