v5-Eagle-7B-HF / README.md
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---
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%2F6bbd31a7-21b4-4ff6-b43f-8735d1decf25_2048x1652.png)
### Huggingface RWKV-5 Eagle 7B Model - via HF Transformers Library
> **! Important Note !**
>
> The following is the HF transformers implementation of the RWKV-5 Eagle 7B 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-Eagle-7B)
>
> This is not an instruct tune model! (soon...)
- [HF Demo](https://huggingface.co/spaces/BlinkDL/RWKV-Gradio-2)
- [Our wiki](https://wiki.rwkv.com)
- [pth model weights](https://huggingface.co/RWKV/v5-Eagle-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/HF_v5-Eagle-7B", trust_remote_code=True).to(torch.float32)
tokenizer = AutoTokenizer.from_pretrained("RWKV/HF_v5-Eagle-7B", 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/HF_v5-Eagle-7B", trust_remote_code=True, torch_dtype=torch.float16).to(0)
tokenizer = AutoTokenizer.from_pretrained("RWKV/HF_v5-Eagle-7B", 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/HF_v5-Eagle-7B", trust_remote_code=True).to(torch.float32)
tokenizer = AutoTokenizer.from_pretrained("RWKV/HF_v5-Eagle-7B", 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
```