--- license: apache-2.0 --- ![An eagle soaring above a transformer robot](https://substackcdn.com/image/fetch/w_1456,c_limit,f_webp,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F10cf7fd1-6c72-4a99-84c2-794fb7bc52b3_2432x1664.png) ### Huggingface RWKV EagleX 7B v2 Model > **! Important Note !** > > The following is the HF transformers implementation of the EagleX 7B 1.7T model. This is meant to be used with the huggingface transformers > For the full model weights on its own, to use with other RWKV libraries, refer to (here)[https://huggingface.co/RWKV/v5-EagleX-v2-7B-pth] > > > This is not an instruct tune model! (soon...) ## Evaluation The following shows the progression of the model from 1.1T trained to 2.25T trained. |Model |Eagle-7B-HF|EagleX-7B-HF-v1|EagleX-7B-HF-v2| |----------------------|-----------|---------------|---------------| |Param Count |7.52 B |7.52 B |7.52 B | |Tokens Trained |1.1 T |1.7 T |2.25 T | |avg_acc |0.4822 |0.5391 |0.5495 | |glue (acc) |0.5752 |0.7463 |0.7439 | |anli (acc) |0.3594 |0.4847 |0.5097 | |mnli (acc) |0.3802 |0.7928 |0.7884 | |mnli_mismatch (acc) |0.3687 |0.7985 |0.784 | |swag (acc) |0.568 |0.5814 |0.5905 | |lambada_standard (acc)|0.685 |0.686 |0.7004 | |lambada_openai (acc) |0.7425 |0.7522 |0.7502 | |mmlu (acc) |0.3321 |0.4014 |0.438 | |winogrande (acc) |0.674 |0.7206 |0.7332 | |wnli (acc) |0.4225 |0.4648 |0.493 | |truthfulqa (acc) |0.3303 |0.3268 |0.3401 | |logiqa (acc) |0.2458 |0.2458 |0.2458 | |logiqa2 (acc) |0.2494 |0.2595 |0.2621 | |sciq (acc) |0.955 |0.96 |0.93 | |piqa (acc) |0.7704 |0.7758 |0.7764 | |arc_easy (acc) |0.7382 |0.7555 |0.7445 | |arc_challenge (acc) |0.3951 |0.4087 |0.4155 | |hellaswag (acc) |0.5264 |0.5411 |0.56 | |openbookqa (acc) |0.302 |0.296 |0.304 | |mathqa (acc) |0.26 |0.26 |0.2593 | |arithmetic (acc) |0.245 |0.0634 |0.1703 | Compared against other top performing models in the same weight class. |Model |OLMo-7B |falcon-7b |Llama-2-7b-hf|EagleX-7B-HF-v2|Mistral-7B-v0.1| |----------------------|---------------|----------------|-------------|---------------|---------------| |Param Count |6.89 B |6.92 B |6.74 B |7.52 B |7.24 B | |Tokens Trained |2.5 T |1.5 T |2 T |2.25 T |2 - 7 T? | |avg_acc |0.4578 |0.4775 |0.5045 |0.5495 |0.5676 | |glue (acc) |0.474 |0.4578 |0.4289 |0.7439 |0.515 | |anli (acc) |0.3478 |0.3541 |0.3697 |0.5097 |0.3803 | |mnli (acc) |0.3294 |0.3893 |0.4269 |0.7884 |0.4542 | |mnli_mismatch (acc) |0.3348 |0.404 |0.4395 |0.784 |0.4632 | |swag (acc) |0.5512 |0.5685 |0.5658 |0.5905 |0.5756 | |lambada_standard (acc)|0.6396 |0.6868 |0.6808 |0.7004 |0.6944 | |lambada_openai (acc) |0.6872 |0.746 |0.7353 |0.7502 |0.7553 | |mmlu (acc) |0.2812 |0.2512 |0.4077 |0.438 |0.5964 | |winogrande (acc) |0.6725 |0.6709 |0.6914 |0.7332 |0.7364 | |wnli (acc) |0.5775 |0.4789 |0.4648 |0.493 |0.5775 | |truthfulqa (acc) |0.3015 |0.2826 |0.3205 |0.3401 |0.3537 | |logiqa (acc) |0.2335 |0.2151 |0.2535 |0.2458 |0.2427 | |logiqa2 (acc) |0.2506 |0.2252 |0.2564 |0.2621 |0.3022 | |sciq (acc) |0.927 |0.944 |0.939 |0.93 |0.959 | |piqa (acc) |0.7878 |0.7949 |0.7807 |0.7764 |0.8052 | |arc_easy (acc) |0.7353 |0.7479 |0.7643 |0.7445 |0.8081 | |arc_challenge (acc) |0.3677 |0.4027 |0.4309 |0.4155 |0.5009 | |hellaswag (acc) |0.5572 |0.5772 |0.5713 |0.56 |0.6131 | |openbookqa (acc) |0.292 |0.306 |0.316 |0.304 |0.33 | |mathqa (acc) |0.26 |0.2884 |0.2801 |0.2593 |0.3554 | |arithmetic (acc) |0.0069 |0.2367 |0.4703 |0.1703 |0.9004 | See the following, for the full details on this model: [https://blog.rwkv.com/p/336f47bf-d8e9-4174-ac1d-02c6c8a99bc0](https://blog.rwkv.com/p/336f47bf-d8e9-4174-ac1d-02c6c8a99bc0) ## Links - [HF Demo](###) - [Our wiki](https://wiki.rwkv.com) - [Full eval data](https://docs.google.com/spreadsheets/d/1CBLU6yKkW-8FMvGD4INO3qjeHZ0qkKnZFcM6n6lWNOs/edit#gid=912381775) #### Running on CPU via HF transformers ```python import torch from transformers import AutoModelForCausalLM, AutoTokenizer def generate_prompt(instruction, input=""): instruction = instruction.strip().replace('\r\n','\n').replace('\n\n','\n') input = input.strip().replace('\r\n','\n').replace('\n\n','\n') if input: return f"""Instruction: {instruction} Input: {input} Response:""" else: return f"""User: hi Assistant: Hi. I am your assistant and I will provide expert full response in full details. Please feel free to ask any question and I will always answer it. User: {instruction} Assistant:""" model = AutoModelForCausalLM.from_pretrained("RWKV/v5-Eagle-7B-HF", trust_remote_code=True).to(torch.float32) tokenizer = AutoTokenizer.from_pretrained("RWKV/v5-Eagle-7B-HF", trust_remote_code=True) text = "请介绍北京的旅游景点" prompt = generate_prompt(text) inputs = tokenizer(prompt, return_tensors="pt") output = model.generate(inputs["input_ids"], max_new_tokens=333, do_sample=True, temperature=1.0, top_p=0.3, top_k=0, ) print(tokenizer.decode(output[0].tolist(), skip_special_tokens=True)) ``` output: ```shell User: hi Assistant: Hi. I am your assistant and I will provide expert full response in full details. Please feel free to ask any question and I will always answer it. User: 请介绍北京的旅游景点 Assistant: 北京是中国的首都，拥有众多的旅游景点，以下是其中一些著名的景点： 1. 故宫：位于北京市中心，是明清两代的皇宫，内有大量的文物和艺术品。 2. 天安门广场：是中国最著名的广场之一，是中国人民政治协商会议的旧址，也是中国人民政治协商会议的中心。 3. 颐和园：是中国古代皇家园林之一，有着悠久的历史和丰富的文化内涵。 4. 长城：是中国古代的一道长城，全长约万里，是中国最著名的旅游景点之一。 5. 北京大学：是中国著名的高等教育机构之一，有着悠久的历史和丰富的文化内涵。 6. 北京动物园：是中国最大的动物园之一，有着丰富的动物资源和丰富的文化内涵。 7. 故宫博物院：是中国最著名的博物馆之一，收藏了大量的文物和艺术品，是中国最重要的文化遗产之一。 8. 天坛：是中国古代皇家 ``` #### Running on GPU via HF transformers ```python import torch from transformers import AutoModelForCausalLM, AutoTokenizer def generate_prompt(instruction, input=""): instruction = instruction.strip().replace('\r\n','\n').replace('\n\n','\n') input = input.strip().replace('\r\n','\n').replace('\n\n','\n') if input: return f"""Instruction: {instruction} Input: {input} Response:""" else: return f"""User: hi Assistant: Hi. I am your assistant and I will provide expert full response in full details. Please feel free to ask any question and I will always answer it. User: {instruction} Assistant:""" model = AutoModelForCausalLM.from_pretrained("RWKV/v5-Eagle-7B-HF", trust_remote_code=True, torch_dtype=torch.float16).to(0) tokenizer = AutoTokenizer.from_pretrained("RWKV/v5-Eagle-7B-HF", trust_remote_code=True) text = "介绍一下大熊猫" prompt = generate_prompt(text) inputs = tokenizer(prompt, return_tensors="pt").to(0) output = model.generate(inputs["input_ids"], max_new_tokens=128, do_sample=True, temperature=1.0, top_p=0.3, top_k=0, ) print(tokenizer.decode(output[0].tolist(), skip_special_tokens=True)) ``` output: ```shell User: hi Assistant: Hi. I am your assistant and I will provide expert full response in full details. Please feel free to ask any question and I will always answer it. User: 介绍一下大熊猫 Assistant: 大熊猫是一种中国特有的哺乳动物，也是中国的国宝之一。它们的外貌特征是圆形的黑白相间的身体，有着黑色的毛发和白色的耳朵。大熊猫的食物主要是竹子，它们会在竹林中寻找竹子，并且会将竹子放在竹笼中进行储存。大熊猫的寿命约为20至30年，但由于栖息地的丧失和人类活动的 ``` #### Batch Inference ```python import torch from transformers import AutoModelForCausalLM, AutoTokenizer def generate_prompt(instruction, input=""): instruction = instruction.strip().replace('\r\n', '\n').replace('\n\n', '\n') input = input.strip().replace('\r\n', '\n').replace('\n\n', '\n') if input: return f"""Instruction: {instruction} Input: {input} Response:""" else: return f"""User: hi Assistant: Hi. I am your assistant and I will provide expert full response in full details. Please feel free to ask any question and I will always answer it. User: {instruction} Assistant:""" model = AutoModelForCausalLM.from_pretrained("RWKV/v5-Eagle-7B-HF", trust_remote_code=True).to(torch.float32) tokenizer = AutoTokenizer.from_pretrained("RWKV/v5-Eagle-7B-HF", trust_remote_code=True) texts = ["请介绍北京的旅游景点", "介绍一下大熊猫", "乌兰察布"] prompts = [generate_prompt(text) for text in texts] inputs = tokenizer(prompts, return_tensors="pt", padding=True) outputs = model.generate(inputs["input_ids"], max_new_tokens=128, do_sample=True, temperature=1.0, top_p=0.3, top_k=0, ) for output in outputs: print(tokenizer.decode(output.tolist(), skip_special_tokens=True)) ``` output: ```shell User: hi Assistant: Hi. I am your assistant and I will provide expert full response in full details. Please feel free to ask any question and I will always answer it. User: 请介绍北京的旅游景点 Assistant: 北京是中国的首都，拥有丰富的旅游资源和历史文化遗产。以下是一些北京的旅游景点： 1. 故宫：位于北京市中心，是明清两代的皇宫，是中国最大的古代宫殿建筑群之一。 2. 天安门广场：位于北京市中心，是中国最著名的城市广场之一，也是中国最大的城市广场。 3. 颐和 User: hi Assistant: Hi. I am your assistant and I will provide expert full response in full details. Please feel free to ask any question and I will always answer it. User: 介绍一下大熊猫 Assistant: 大熊猫是一种生活在中国中部地区的哺乳动物，也是中国的国宝之一。它们的外貌特征是圆形的黑白相间的身体，有着黑色的毛发和圆圆的眼睛。大熊猫是一种濒危物种，目前只有在野外的几个保护区才能看到它们的身影。大熊猫的食物主要是竹子，它们会在竹子上寻找食物，并且可以通 User: hi Assistant: Hi. I am your assistant and I will provide expert full response in full details. Please feel free to ask any question and I will always answer it. User: 乌兰察布 Assistant: 乌兰察布是中国新疆维吾尔自治区的一个县级市，位于新疆维吾尔自治区中部，是新疆的第二大城市。乌兰察布市是新疆的第一大城市，也是新疆的重要城市之一。乌兰察布市是新疆的经济中心，也是新疆的重要交通枢纽之一。乌兰察布市的人口约为2.5万人，其中汉族占绝大多数。乌 ``` ## Links - [Our wiki](https://wiki.rwkv.com) - [Full eval data](https://docs.google.com/spreadsheets/d/1CBLU6yKkW-8FMvGD4INO3qjeHZ0qkKnZFcM6n6lWNOs/edit#gid=912381775) - [RecursalAI Online Platform](https://recursal.ai) - [HF Demo (todo)](###) ## Acknowledgement We are grateful for the help and support from the following key groups: - [Recursal.ai](https://recursal.ai) team for financing the GPU resources, and managing the training of this foundation model - you can run the Eagle line of RWKV models on their cloud / on-premise platform today. - EleutherAI for their support, especially in the v5/v6 Eagle/Finch paper - Linux Foundation AI & Data group for supporting and hosting the RWKV project