Upload README.md

README.md

@@ -5,16 +5,9 @@ license: llama2
 model_creator: Xwin-LM
 model_name: Xwin-LM 13B V0.1
 model_type: llama
-prompt_template: 'Below is an instruction that describes a task. Write a response
-  that appropriately completes the request.
-
-
-  ### Instruction:
-
-  {prompt}
-
-
-  ### Response:
+prompt_template: '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: {prompt} ASSISTANT:
 
   '
 quantized_by: TheBloke
@@ -63,15 +56,10 @@ It is also now supported by continuous batching server [vLLM](https://github.com
 <!-- repositories-available end -->
 
 <!-- prompt-template start -->
-## Prompt template: Alpaca
+## Prompt template: Vicuna
 
 ```
-Below is an instruction that describes a task. Write a response that appropriately completes the request.
-
-### Instruction:
-{prompt}
-
-### Response:
+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: {prompt} ASSISTANT:
 
 ```
 
@@ -99,7 +87,7 @@ Documentation on installing and using vLLM [can be found here](https://vllm.read
 - When using vLLM as a server, pass the `--quantization awq` parameter, for example:
 
 ```shell
-python3 python -m vllm.entrypoints.api_server --model TheBloke/Xwin-LM-13B-V0.1-AWQ --quantization awq
+python3 python -m vllm.entrypoints.api_server --model TheBloke/Xwin-LM-13B-V0.1-AWQ --quantization awq --dtype half
 ```
 
 When using vLLM from Python code, pass the `quantization=awq` parameter, for example:
@@ -115,7 +103,7 @@ prompts = [
 ]
 sampling_params = SamplingParams(temperature=0.8, top_p=0.95)
 
-llm = LLM(model="TheBloke/Xwin-LM-13B-V0.1-AWQ", quantization="awq")
+llm = LLM(model="TheBloke/Xwin-LM-13B-V0.1-AWQ", quantization="awq", dtype="half")
 
 outputs = llm.generate(prompts, sampling_params)
 
@@ -161,12 +149,7 @@ model = AutoAWQForCausalLM.from_quantized(model_name_or_path, fuse_layers=True,
 tokenizer = AutoTokenizer.from_pretrained(model_name_or_path, trust_remote_code=False)
 
 prompt = "Tell me about AI"
-prompt_template=f'''Below is an instruction that describes a task. Write a response that appropriately completes the request.
-
-### Instruction:
-{prompt}
-
-### Response:
+prompt_template=f'''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: {prompt} ASSISTANT:
 
 '''
 
@@ -259,6 +242,9 @@ Xwin-LM: Powerful, Stable, and Reproducible LLM Alignment
 </h3>
 
 <p align="center">
+  <a href="https://github.com/Xwin-LM/Xwin-LM">
+    <img src="https://img.shields.io/badge/GitHub-yellow.svg?style=social&logo=github">
+  </a>
   <a href="https://huggingface.co/Xwin-LM">
     <img src="https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Models-blue">
   </a>
@@ -268,13 +254,14 @@ 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, reject sampling, reinforcement learning, 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.
+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
 
-- :boom: [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**.
-- :boom: [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.
-- :boom: [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.
+- 💥 [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
@@ -302,4 +289,101 @@ The table below displays the performance of Xwin-LM on [AlpacaEval](https://tats
 | **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.<br> 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.</s>USER: Who are you? ASSISTANT: I am Xwin-LM.</s>......
+```
+
+### HuggingFace Example
+
+```python
+from transformers import AutoTokenizer, AutoModelForCausalLM
+
+model = AutoModelForCausalLM.from_pretrained("Xwin-LM/Xwin-LM-7B-V0.1")
+tokenizer = AutoTokenizer.from_pretrained("Xwin-LM/Xwin-LM-7B-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-7B-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).