WizardLM-7B-V1.0/README.md

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	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	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	Llama 2
WizardMath-13B-V1.0	🤗 HF Link	📃 [WizardMath]	63.9	14.0	Demo	Llama 2
WizardMath-7B-V1.0	🤗 HF Link	📃 [WizardMath]	54.9	10.7	Demo	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

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])