The WizardLM delta weights. ## WizardLM: Empowering Large Pre-Trained Language Models to Follow Complex Instructions

🤗 HF Repo • 🐦 Twitter • 📃 [WizardLM] • 📃 [WizardCoder] • 📃 [WizardMath]

👋 Join our Discord

| Model | Checkpoint | Paper | HumanEval | MBPP | Demo | License | | ----- |------| ---- |------|-------| ----- | ----- | | WizardCoder-Python-34B-V1.0 | 🤗 HF Link | 📃 [WizardCoder] | 73.2 | 61.2 | [Demo](http://47.103.63.15:50085/) | Llama2 | | WizardCoder-15B-V1.0 | 🤗 HF Link | 📃 [WizardCoder] | 59.8 |50.6 | -- | OpenRAIL-M | | WizardCoder-Python-13B-V1.0 | 🤗 HF Link | 📃 [WizardCoder] | 64.0 | 55.6 | -- | Llama2 | | WizardCoder-3B-V1.0 | 🤗 HF Link | 📃 [WizardCoder] | 34.8 |37.4 | [Demo](http://47.103.63.15:50086/) | OpenRAIL-M | | WizardCoder-1B-V1.0 | 🤗 HF Link | 📃 [WizardCoder] | 23.8 |28.6 | -- | OpenRAIL-M | | Model | Checkpoint | Paper | GSM8k | MATH |Online Demo| License| | ----- |------| ---- |------|-------| ----- | ----- | | WizardMath-70B-V1.0 | 🤗 HF Link | 📃 [WizardMath]| **81.6** | **22.7** |[Demo](http://47.103.63.15:50083/)| Llama 2 | | WizardMath-13B-V1.0 | 🤗 HF Link | 📃 [WizardMath]| **63.9** | **14.0** |[Demo](http://47.103.63.15:50082/)| Llama 2 | | WizardMath-7B-V1.0 | 🤗 HF Link | 📃 [WizardMath]| **54.9** | **10.7** | [Demo](http://47.103.63.15:50080/)| Llama 2 | | Model | Checkpoint | Paper |MT-Bench | AlpacaEval | WizardEval | HumanEval | License| | ----- |------| ---- |------|-------| ----- | ----- | ----- | | WizardLM-13B-V1.2 | 🤗 HF Link | | 7.06 | 89.17% | 101.4% |36.6 pass@1| Llama 2 License | | WizardLM-13B-V1.1 | 🤗 HF Link | | 6.76 |86.32% | 99.3% |25.0 pass@1| Non-commercial| | WizardLM-30B-V1.0 | 🤗 HF Link | | 7.01 | | 97.8% | 37.8 pass@1| Non-commercial | | WizardLM-13B-V1.0 | 🤗 HF Link | | 6.35 | 75.31% | 89.1% | 24.0 pass@1 | Non-commercial| | WizardLM-7B-V1.0 | 🤗 HF Link | 📃 [WizardLM] | | | 78.0% |19.1 pass@1 | Non-commercial| ## Inference WizardLM Demo Script We provide the inference WizardLM demo code [here](https://github.com/nlpxucan/WizardLM/tree/main/demo).