README.md

---
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

![image/png](https://cdn-uploads.huggingface.co/production/uploads/64c14f6b02e1f8f67c73bd05/pf4d6FA7DriRtVq5HCkxd.png)

![image/png](https://cdn-uploads.huggingface.co/production/uploads/64c14f6b02e1f8f67c73bd05/htflRMYp8ab8GfhP-lo-J.png)

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](https://huggingface.co/abacusai/Smaug-72B-v0.1), and more information will be released later on. For now, see the previous Smaug paper: https://arxiv.org/abs/2402.13228.

## Model Details

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## Uses

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### Direct Use

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### Out-of-Scope Use

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## Bias, Risks, and Limitations

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### Recommendations

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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 

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#### Training Hyperparameters

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## Evaluation

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### Testing Data, Factors & Metrics

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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

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Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).

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## Technical Specifications [optional]

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