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])