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
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 |
Model Details
Model Sources [optional]
- Repository: [More Information Needed]
- Paper [optional]: [More Information Needed]
- Demo [optional]: [More Information Needed]
Uses
Direct Use
[More Information Needed]
Downstream Use [optional]
[More Information Needed]
Out-of-Scope Use
[More Information Needed]
Bias, Risks, and Limitations
[More Information Needed]
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.
[More Information Needed]
Training Details
Training Data
[More Information Needed]
Training Procedure
Preprocessing [optional]
[More Information Needed]
Training Hyperparameters
- Training regime: [More Information Needed]
Speeds, Sizes, Times [optional]
[More Information Needed]
Evaluation
Testing Data, Factors & Metrics
Testing Data
[More Information Needed]
Factors
[More Information Needed]
Metrics
[More Information Needed]
Results
[More Information Needed]
Summary
Model Examination [optional]
[More Information Needed]
Environmental Impact
Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
- Hardware Type: [More Information Needed]
- Hours used: [More Information Needed]
- Cloud Provider: [More Information Needed]
- Compute Region: [More Information Needed]
- Carbon Emitted: [More Information Needed]
Technical Specifications [optional]
Model Architecture and Objective
[More Information Needed]
Compute Infrastructure
[More Information Needed]
Hardware
[More Information Needed]
Software
[More Information Needed]
Citation [optional]
BibTeX:
[More Information Needed]
APA:
[More Information Needed]
Glossary [optional]
[More Information Needed]
More Information [optional]
[More Information Needed]
Model Card Authors [optional]
[More Information Needed]
Model Card Contact
[More Information Needed]