From b1445f45e5fd627af8ed75598c06420371ab5b70 Mon Sep 17 00:00:00 2001 From: ModelHub XC Date: Sat, 23 May 2026 13:26:12 +0800 Subject: [PATCH] =?UTF-8?q?=E5=88=9D=E5=A7=8B=E5=8C=96=E9=A1=B9=E7=9B=AE?= =?UTF-8?q?=EF=BC=8C=E7=94=B1ModelHub=20XC=E7=A4=BE=E5=8C=BA=E6=8F=90?= =?UTF-8?q?=E4=BE=9B=E6=A8=A1=E5=9E=8B?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Model: AI-ModelScope/Xwin-LM-13B-V0.1 Source: Original Platform --- .gitattributes | 35 +++ README.md | 181 ++++++++++++++ config.json | 26 ++ configuration.json | 1 + generation_config.json | 10 + pytorch_model-00001-of-00006.bin | 3 + pytorch_model-00002-of-00006.bin | 3 + pytorch_model-00003-of-00006.bin | 3 + pytorch_model-00004-of-00006.bin | 3 + pytorch_model-00005-of-00006.bin | 3 + pytorch_model-00006-of-00006.bin | 3 + pytorch_model.bin.index.json | 410 +++++++++++++++++++++++++++++++ special_tokens_map.json | 24 ++ tokenizer.model | 3 + tokenizer_config.json | 35 +++ 15 files changed, 743 insertions(+) create mode 100644 .gitattributes create mode 100644 README.md create mode 100644 config.json create mode 100644 configuration.json create mode 100644 generation_config.json create mode 100644 pytorch_model-00001-of-00006.bin create mode 100644 pytorch_model-00002-of-00006.bin create mode 100644 pytorch_model-00003-of-00006.bin create mode 100644 pytorch_model-00004-of-00006.bin create mode 100644 pytorch_model-00005-of-00006.bin create mode 100644 pytorch_model-00006-of-00006.bin create mode 100644 pytorch_model.bin.index.json create mode 100644 special_tokens_map.json create mode 100644 tokenizer.model create mode 100644 tokenizer_config.json diff --git a/.gitattributes b/.gitattributes new file mode 100644 index 0000000..a6344aa --- /dev/null +++ b/.gitattributes @@ -0,0 +1,35 @@ +*.7z filter=lfs diff=lfs merge=lfs -text +*.arrow filter=lfs diff=lfs merge=lfs -text +*.bin filter=lfs diff=lfs merge=lfs -text +*.bz2 filter=lfs diff=lfs merge=lfs -text +*.ckpt filter=lfs diff=lfs merge=lfs -text +*.ftz filter=lfs diff=lfs merge=lfs -text +*.gz filter=lfs diff=lfs merge=lfs -text +*.h5 filter=lfs diff=lfs merge=lfs -text +*.joblib filter=lfs diff=lfs merge=lfs -text +*.lfs.* filter=lfs diff=lfs merge=lfs -text +*.mlmodel filter=lfs diff=lfs merge=lfs -text +*.model filter=lfs diff=lfs merge=lfs -text +*.msgpack filter=lfs diff=lfs merge=lfs -text +*.npy filter=lfs diff=lfs merge=lfs -text +*.npz filter=lfs diff=lfs merge=lfs -text +*.onnx filter=lfs diff=lfs merge=lfs -text +*.ot filter=lfs diff=lfs merge=lfs -text +*.parquet filter=lfs diff=lfs merge=lfs -text +*.pb filter=lfs diff=lfs merge=lfs -text +*.pickle filter=lfs diff=lfs merge=lfs -text +*.pkl filter=lfs diff=lfs merge=lfs -text +*.pt filter=lfs diff=lfs merge=lfs -text +*.pth filter=lfs diff=lfs merge=lfs -text +*.rar filter=lfs diff=lfs merge=lfs -text +*.safetensors filter=lfs diff=lfs merge=lfs -text +saved_model/**/* filter=lfs diff=lfs merge=lfs -text +*.tar.* filter=lfs diff=lfs merge=lfs -text +*.tar filter=lfs diff=lfs merge=lfs -text +*.tflite filter=lfs diff=lfs merge=lfs -text +*.tgz filter=lfs diff=lfs merge=lfs -text +*.wasm filter=lfs diff=lfs merge=lfs -text +*.xz filter=lfs diff=lfs merge=lfs -text +*.zip filter=lfs diff=lfs merge=lfs -text +*.zst filter=lfs diff=lfs merge=lfs -text +*tfevents* filter=lfs diff=lfs merge=lfs -text diff --git a/README.md b/README.md new file mode 100644 index 0000000..9d8d8f2 --- /dev/null +++ b/README.md @@ -0,0 +1,181 @@ +--- +license: llama2 +--- + +

+Xwin-LM: Powerful, Stable, and Reproducible LLM Alignment +

+ +

+ + + + + + +

+ + + +**Step up your LLM alignment with Xwin-LM!** + +Xwin-LM aims to develop and open-source alignment technologies for large language models, including supervised fine-tuning (SFT), reward models (RM), reject sampling, reinforcement learning from human feedback (RLHF), etc. Our first release, built-upon on the Llama2 base models, ranked **TOP-1** on [AlpacaEval](https://tatsu-lab.github.io/alpaca_eval/). Notably, it's **the first to surpass GPT-4** on this benchmark. The project will be continuously updated. + +## News + +- 💥 [Sep, 2023] We released [Xwin-LM-70B-V0.1](https://huggingface.co/Xwin-LM/Xwin-LM-70B-V0.1), which has achieved a win-rate against Davinci-003 of **95.57%** on [AlpacaEval](https://tatsu-lab.github.io/alpaca_eval/) benchmark, ranking as **TOP-1** on AlpacaEval. **It was the FIRST model surpassing GPT-4** on [AlpacaEval](https://tatsu-lab.github.io/alpaca_eval/). Also note its winrate v.s. GPT-4 is **60.61**. +- 🔍 [Sep, 2023] RLHF plays crucial role in the strong performance of Xwin-LM-V0.1 release! +- 💥 [Sep, 2023] We released [Xwin-LM-13B-V0.1](https://huggingface.co/Xwin-LM/Xwin-LM-13B-V0.1), which has achieved **91.76%** win-rate on [AlpacaEval](https://tatsu-lab.github.io/alpaca_eval/), ranking as **top-1** among all 13B models. +- 💥 [Sep, 2023] We released [Xwin-LM-7B-V0.1](https://huggingface.co/Xwin-LM/Xwin-LM-7B-V0.1), which has achieved **87.82%** win-rate on [AlpacaEval](https://tatsu-lab.github.io/alpaca_eval/), ranking as **top-1** among all 7B models. + + +## Model Card +| Model | Checkpoint | Report | License | +|------------|------------|-------------|------------------| +|Xwin-LM-7B-V0.1| 🤗 HF Link | 📃**Coming soon (Stay tuned)** | Llama 2 License| +|Xwin-LM-13B-V0.1| 🤗 HF Link | | Llama 2 License| +|Xwin-LM-70B-V0.1| 🤗 HF Link | | Llama 2 License| +## Benchmarks + +### Xwin-LM performance on [AlpacaEval](https://tatsu-lab.github.io/alpaca_eval/). + +The table below displays the performance of Xwin-LM on [AlpacaEval](https://tatsu-lab.github.io/alpaca_eval/), where evaluates its win-rate against Text-Davinci-003 across 805 questions. To provide a comprehensive evaluation, we present, for the first time, the win-rate against ChatGPT and GPT-4 as well. Our Xwin-LM model family establish a new state-of-the-art performance across all metrics. Notably, Xwin-LM-70B-V0.1 has eclipsed GPT-4 for the first time, achieving an impressive win-rate of **95.57%** to Text-Davinci-003 and **60.61%** to GPT-4. + +| **Model** | **AlpacaEval (winrate %)** | **AlpacaEval (winrate %)** |**AlpacaEval (winrate %)** | +|----------------------------------|------------|----------|-------------| +| | **v.s. Text-Davinci-003** | **v.s. ChatGPT** | **v.s. GPT4**| +| **Xwin-LM-70B-V0.1** | **95.57** | **87.50** | **60.61** | +| GPT-4 | 95.28 | 84.66 | 50.00 | +| WizardLM-70B-V1.0 | 92.91 | 80.19 | 46.70 | +| Llama-2-70B-Chat | 92.66 | 81.97 | 51.19 | +| **Xwin-LM-13B-V0.1** | **91.76** | **81.79** | **55.30** | +| ChatGPT | 89.37 | 50.00 | 16.60 | +| WizardLM-13B-V1.2 | 89.17 | 75.75 | 41.91 | +| **Xwin-LM-7B-V0.1** | **87.35** | **76.40** | **47.57** | +| Llama-2-13B-Chat | 81.09 | 64.22 | 30.92 | + +### Case study on [AlpacaEval](https://tatsu-lab.github.io/alpaca_eval/) + +| Prompts | GTP4 response | Xwin-LM response | +|----------------|------------|--------| +|I've put a plate on top of an apple, and take the plate from kitchen to dinning room. Where is the apple now|The apple is now in the dining room, as it was underneath the plate when you moved the plate from the kitchen to the dining room.|The apple is still in the kitchen, as you have only moved the plate. The apple is under the plate, so if you want to bring the apple to the dining room, you should either move the apple directly or put the apple on the plate and then move the plate to the dining room.| +| Answer the following question.
How do you say "good evening" in French. | In French, "good evening" is said as "bonsoir." | In French, "good evening" is translated to "bonsoir." This greeting is used in the evening and nighttime hours, typically after 6 p.m. It is a combination of "bon," which means "good," and "soir," which means "evening." To pronounce it, say "bone-swahr." | + + +### Xwin-LM performance on NLP foundation tasks. + +The following table provides a comparison of Xwin-LMs with other LLMs on NLP foundation tasks in [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard). + +| Model | MMLU 5-shot | ARC 25-shot | TruthfulQA 0-shot | HellaSwag 10-shot | Average | +|------------------|-------------|-------------|-------------------|-------------------|------------| +| Text-davinci-003 | 56.9 | **85.2** | 59.3 | 82.2 | 70.9 | +|Vicuna-13b 1.1 | 51.3 | 53.0 | 51.8 | 80.1 | 59.1 | +|Guanaco 30B | 57.6 | 63.7 | 50.7 | 85.1 | 64.3 | +| WizardLM-7B 1.0 | 42.7 | 51.6 | 44.7 | 77.7 | 54.2 | +| WizardLM-13B 1.0 | 52.3 | 57.2 | 50.5 | 81.0 | 60.2 | +| WizardLM-30B 1.0 | 58.8 | 62.5 | 52.4 | 83.3 | 64.2| +| Llama-2-7B-Chat | 48.3 | 52.9 | 45.6 | 78.6 | 56.4 | +| Llama-2-13B-Chat | 54.6 | 59.0 | 44.1 | 81.9 | 59.9 | +| Llama-2-70B-Chat | 63.9 | 64.6 | 52.8 | 85.9 | 66.8 | +| **Xwin-LM-7B-V0.1** | 49.7 | 56.2 | 48.1 | 79.5 | 58.4 | +| **Xwin-LM-13B-V0.1** | 56.6 | 62.4 | 45.5 | 83.0 | 61.9 | +| **Xwin-LM-70B-V0.1** | **69.6** | 70.5 | **60.1** | **87.1** | **71.8** | + + +## Inference + +### Conversation templates +To obtain desired results, please strictly follow the conversation templates when utilizing our model for inference. Our model adopts the prompt format established by [Vicuna](https://github.com/lm-sys/FastChat) and is equipped to support **multi-turn** conversations. +``` +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 Xwin-LM....... +``` + +### ModelScope Example +```python +from modelscope import AutoTokenizer, AutoModelForCausalLM, snapshot_download +import torch + + +model_dir = snapshot_download('AI-ModelScope/Xwin-LM-13B-V0.1', 'v1.0.0') + +model = AutoModelForCausalLM.from_pretrained(model_dir, + torch_dtype=torch.bfloat16, + device_map='cuda') +tokenizer = AutoTokenizer.from_pretrained(model_dir) +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: Hello, can you help me? " + "ASSISTANT:" +) +inputs = tokenizer(prompt, return_tensors="pt").to('cuda') +samples = model.generate(**inputs, max_new_tokens=4096, temperature=0.7) +output = tokenizer.decode(samples[0][inputs["input_ids"].shape[1]:], skip_special_tokens=True) +print(output) +# Of course! I'm here to help. Please feel free to ask your question or describe the issue you're having, and I'll do my best to assist you. + +``` + + +### HuggingFace Example + +```python +from transformers import AutoTokenizer, AutoModelForCausalLM + +model = AutoModelForCausalLM.from_pretrained("Xwin-LM/Xwin-LM-13B-V0.1") +tokenizer = AutoTokenizer.from_pretrained("Xwin-LM/Xwin-LM-13B-V0.1") +( + 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: Hello, can you help me? " + "ASSISTANT:" +) +inputs = tokenizer(prompt, return_tensors="pt") +samples = model.generate(**inputs, max_new_tokens=4096, temperature=0.7) +output = tokenizer.decode(samples[0][inputs["input_ids"].shape[1]:], skip_special_tokens=True) +print(output) +# Of course! I'm here to help. Please feel free to ask your question or describe the issue you're having, and I'll do my best to assist you. +``` + + +### vllm Example +Because Xwin-LM is based on Llama2, it also offers support for rapid inference using [vllm](https://github.com/vllm-project/vllm). Please refer to [vllm](https://github.com/vllm-project/vllm) for detailed installation instructions. +```python +from vllm import LLM, SamplingParams +( + 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: Hello, can you help me? " + "ASSISTANT:" +) +sampling_params = SamplingParams(temperature=0.7, max_tokens=4096) +llm = LLM(model="Xwin-LM/Xwin-LM-13B-V0.1") +outputs = llm.generate([prompt,], sampling_params) + +for output in outputs: + prompt = output.prompt + generated_text = output.outputs[0].text + print(generated_text) +``` + +## TODO + +- [ ] Release the source code +- [ ] Release more capabilities, such as math, reasoning, and etc. + +## Citation +Please consider citing our work if you use the data or code in this repo. +``` +@software{xwin-lm, + title = {Xwin-LM}, + author = {Xwin-LM Team}, + url = {https://github.com/Xwin-LM/Xwin-LM}, + version = {pre-release}, + year = {2023}, + month = {9}, +} +``` + +## Acknowledgements + +Thanks to [Llama 2](https://ai.meta.com/llama/), [FastChat](https://github.com/lm-sys/FastChat), [AlpacaFarm](https://github.com/tatsu-lab/alpaca_farm), and [vllm](https://github.com/vllm-project/vllm). diff --git a/config.json b/config.json new file mode 100644 index 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