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

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

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| 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}| ## Example code ```python import torch from modelscope import AutoModelForCausalLM, AutoTokenizer model = AutoModelForCausalLM.from_pretrained("AI-ModelScope/WizardLM-7B-V1.0", revision='v1.0.1', device_map='auto', torch_dtype=torch.float16) tokenizer = AutoTokenizer.from_pretrained("AI-ModelScope/WizardLM-7B-V1.0", revision='v1.0.1') prompt = """A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: Who are you? ASSISTANT: """ inputs = tokenizer(prompt, padding=False, add_special_tokens=False, return_tensors="pt") # Generate generate_ids = model.generate( inputs.input_ids.to(model.device), attention_mask=inputs['attention_mask'].to(model.device), do_sample=True, top_k=10, temperature=0.1, top_p=0.95, num_return_sequences=1, eos_token_id=tokenizer.eos_token_id, max_length=200) print(tokenizer.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]) ```