Smaug-2-72B / README.md
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metadata
library_name: transformers
license: other
license_name: tongyi-qianwen
license_link: https://huggingface.co/Qwen/Qwen1.5-72B/blob/main/LICENSE
base_model: Qwen/Qwen1.5-72B-Chat

Model Card for Model ID

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Introducing Smaug-2, the return of Smaug!

This version of Smaug is based on the Qwen1.5-72B-Chat model and has undergone further fine-tuning. It is specialised in the areas of reasoning and coding.

It outperforms Qwen1.5-72B-Chat on MT-Bench, as shown below.

MT-Bench

We ran MT-Bench with the Qwen conversation template.

Model First Turn Second Turn Average
Qwen1.5-72B-Chat 8.59 8.08 8.34
Smaug-2-72B 8.86 8.20 8.53

HumanEval

We ran HumanEval with pass@1 with the Qwen conversation template. Smaug-2 outperforms Qwen1.5-72B-Chat by approximately 10%:

Model pass@1 (%)
Qwen1.5-72B-Chat 56.7
Smaug-2-72B 66.5

This version of Smaug uses new techniques and new data compared to Smaug-72B, and more information will be released later on. For now, see the previous Smaug paper: https://arxiv.org/abs/2402.13228.

Model Details

Model Sources [optional]

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Uses

Direct Use

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Out-of-Scope Use

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Bias, Risks, and Limitations

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Recommendations

Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.

How to Get Started with the Model

Use the code below to get started with the model.

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Training Details

Training Data

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Training Procedure

Preprocessing [optional]

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Training Hyperparameters

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Speeds, Sizes, Times [optional]

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Evaluation

Testing Data, Factors & Metrics

Testing Data

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Factors

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Metrics

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Results

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Summary

Model Examination [optional]

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Environmental Impact

Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).

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Technical Specifications [optional]

Model Architecture and Objective

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Compute Infrastructure

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Hardware

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Software

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Citation [optional]

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