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---
license: apache-2.0
datasets:
- cerebras/SlimPajama-627B
- EleutherAI/pile
language:
- en
---
### Huggingface RWKV EagleX 7B v2 Model
> **! Important Note !**
>
> The following is the HF transformers implementation of the EagleX 7B 2.25T 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 `RWKV/v5-EagleX-v2-7B-pth`](https://huggingface.co/RWKV/v5-EagleX-v2-7B-pth)
>
>
> This is not an instruct tune model! (soon...)
## Quickstart with the hugging face transformer library
```
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)
```
## 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/eaglex-v2-soaring-past-llama2-7b](https://blog.rwkv.com/p/eaglex-v2-soaring-past-llama2-7b)
#### 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)
- [Recursal.AI Cloud Platform](https://recursal.ai)
- [HF Gradio Demo](https://huggingface.co/spaces/RWKV/v5-EagleX-v2-7B-gradio)
- [Blog article, detailing our model launch](https://blog.rwkv.com/p/eaglex-v2-soaring-past-llama2-7b)
## 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 |