Finch-MoE-37B-A11B-v0.1-HF

Huggingface RWKV Flock of Finches 37B-A11B v0.1 Mixture of Experts Model

HF compatible model for Finch-MoE-37B-A11B-v0.1

Compute sponsored by TensorWave - Access MI300X today!

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! Important Note !

The following is the HF transformers implementation of the Flock of Finches Mixture of Experts 37B-A11B v0.1 model. This is meant to be used with the huggingface transformers

Quickstart with the hugging face transformer library

model = AutoModelForCausalLM.from_pretrained("RWKV/Finch-MoE-37B-A11B-v0.1-HF", trust_remote_code=True).to(torch.float32)
tokenizer = AutoTokenizer.from_pretrained("RWKV/Finch-MoE-37B-A11B-v0.1-HF", trust_remote_code=True)

Evaluation

The following demonstrates the improvements from Eagle 7B to Flock of Finches 37B-A11B v0.1

Running on CPU via HF transformers

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/Finch-MoE-37B-A11B-v0.1-HF", trust_remote_code=True).to(torch.float32)
tokenizer = AutoTokenizer.from_pretrained("RWKV/Finch-MoE-37B-A11B-v0.1-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:

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

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/Finch-MoE-37B-A11B-v0.1-HF", trust_remote_code=True, torch_dtype=torch.float16).to(0)
tokenizer = AutoTokenizer.from_pretrained("RWKV/Finch-MoE-37B-A11B-v0.1-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:

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

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/Finch-MoE-37B-A11B-v0.1-HF", trust_remote_code=True).to(torch.float32)
tokenizer = AutoTokenizer.from_pretrained("RWKV/Finch-MoE-37B-A11B-v0.1-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:

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
• TensorWave - The AMD Cloud - Access MI300X today!
• Recursal.AI Cloud Platform
• Featherless Inference

Acknowledgement

We are grateful for the help and support from the following key groups:

• TensorWave - Sponsored the compute needed to train this model. It wouldn't be possible without them.
• Recursal for their support in building and managing the training of this model architecture
• EleutherAI for their support, especially in the v5/v6 Eagle/Finch paper
• Linux Foundation AI & Data group for supporting and hosting the RWKV project