WizardMath: Empowering Mathematical Reasoning for Large Language Models via Reinforced Evol-Instruct (RLEIF)
? HF Repo •? Github 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 | GSM8k | HumanEval | License |
|---|---|---|---|---|---|---|---|
| WizardLM-70B-V1.0 | ? HF Link | ?Coming Soon | 7.78 | 92.91% | 77.6% | 50.6 pass@1 | Llama 2 License |
| WizardLM-13B-V1.2 | ? HF Link | 7.06 | 89.17% | 55.3% | 36.6 pass@1 | Llama 2 License | |
| WizardLM-13B-V1.1 | ? HF Link | 6.76 | 86.32% | 25.0 pass@1 | Non-commercial | ||
| WizardLM-30B-V1.0 | ? HF Link | 7.01 | 37.8 pass@1 | Non-commercial | |||
| WizardLM-13B-V1.0 | ? HF Link | 6.35 | 75.31% | 24.0 pass@1 | Non-commercial | ||
| WizardLM-7B-V1.0 | ? HF Link | ? [WizardLM] | 19.1 pass@1 | Non-commercial | |||
Github Repo: https://github.com/nlpxucan/WizardLM/tree/main/WizardMath
Twitter: https://twitter.com/WizardLM_AI/status/1689998428200112128
Discord: https://discord.gg/VZjjHtWrKs
❗Note for model system prompts usage:
Please use the same systems prompts strictly with us, and we do not guarantee the accuracy of the quantified versions.
Default version:
"Below is an instruction that describes a task. Write a response that appropriately completes the request.\n\n### Instruction:\n{instruction}\n\n### Response:"
CoT Version: （❗For the simple math questions, we do NOT recommend to use the CoT prompt.）
"Below is an instruction that describes a task. Write a response that appropriately completes the request.\n\n### Instruction:\n{instruction}\n\n### Response: Let's think step by step."
Example code
import torch
from modelscope import AutoModelForCausalLM, AutoTokenizer
model = AutoModelForCausalLM.from_pretrained("AI-ModelScope/WizardMath-7B-V1.0", revision='v1.0.0', device_map='auto', torch_dtype=torch.float16)
tokenizer = AutoTokenizer.from_pretrained("AI-ModelScope/WizardMath-7B-V1.0", revision='v1.0.0')
prompt = """"Below is an instruction that describes a task. Write a response that appropriately completes the request.\n\n### Instruction:\nJames decides to run 3 sprints 3 times a week. He runs 60 meters each sprint. How many total meters does he run a week?\n\n### Response:"""
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])
❗To commen concern about dataset:
Recently, there have been clear changes in the open-source policy and regulations of our overall organization's code, data, and models. Despite this, we have still worked hard to obtain opening the weights of the model first, but the data involves stricter auditing and is in review with our legal team . Our researchers have no authority to publicly release them without authorization. Thank you for your understanding.
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