This is the Full-Weight of WizardLM-13B V1.2 model, this model is trained from Llama-2 13b.
WizardLM: Empowering Large Pre-Trained Language Models to Follow Complex Instructions
? HF Repo •? Github Repo • ? Twitter • ? [WizardLM] • ? [WizardCoder] • ? [WizardMath]
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News
- ???[2023/08/26] We released WizardCoder-Python-34B-V1.0 , which achieves the 73.2 pass@1 and surpasses GPT4 (2023/03/15), ChatGPT-3.5, and Claude2 on the HumanEval Benchmarks. For more details, please refer to WizardCoder.
- [2023/06/16] We released WizardCoder-15B-V1.0 , which surpasses Claude-Plus (+6.8), Bard (+15.3) and InstructCodeT5+ (+22.3) on the HumanEval Benchmarks. For more details, please refer to WizardCoder.
| 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 |
- ? [08/11/2023] We release WizardMath Models.
- ? Our WizardMath-70B-V1.0 model slightly outperforms some closed-source LLMs on the GSM8K, including ChatGPT 3.5, Claude Instant 1 and PaLM 2 540B.
- ? Our WizardMath-70B-V1.0 model achieves 81.6 pass@1 on the GSM8k Benchmarks, which is 24.8 points higher than the SOTA open-source LLM.
- ? Our WizardMath-70B-V1.0 model achieves 22.7 pass@1 on the MATH Benchmarks, which is 9.2 points higher than the SOTA open-source LLM.
| 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 | ||
Repository: https://github.com/nlpxucan/WizardLM
Twitter:
- ??? [7/25/2023] We released WizardLM V1.2 models. The WizardLM-13B-V1.2 is here (Demo13B-V1.2, Demo13B-V1.2_bak-1, Full Model Weight). Please checkout the paper.
- ??? [7/25/2023] The WizardLM-13B-V1.2 achieves 7.06 on MT-Bench Leaderboard, 89.17% on AlpacaEval Leaderboard, and 101.4% on WizardLM Eval. (Note: MT-Bench and AlpacaEval are all self-test, will push update and request review. All tests are completed under their official settings.)
Example code
```python import torch from modelscope import AutoModelForCausalLM, AutoTokenizer
model = AutoModelForCausalLM.frompretrained("AI-ModelScope/WizardLM-13B-V1.2", revision='v1.0.0', devicemap='auto', torchdtype=torch.float16) tokenizer = AutoTokenizer.frompretrained("AI-ModelScope/WizardLM-13B-V1.2", revision='v1.0.0')
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, addspecialtokens=False, return_tensors="pt")
Generate
generateids = model.generate( inputs.inputids.to(model.device), attentionmask=inputs['attentionmask'].to(model.device), dosample=True, topk=10, temperature=0.1, topp=0.95, numreturnsequences=1, eostokenid=tokenizer.eostokenid, maxlength=200) print(tokenizer.batchdecode(generateids, skipspecialtokens=True, cleanuptokenization_spaces=False)[0])
❗<b>Note for model system prompts usage:</b>
<b>WizardLM</b> adopts the prompt format from <b>Vicuna</b> and supports **multi-turn** conversation. The prompt should be as following:
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: Hi ASSISTANT: Hello.USER: Who are you? ASSISTANT: I am WizardLM.…… ```
❗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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