🚀 ARWKV🪿

ARWKV是一个基于纯RNN的模型，结合了RWKV-7的时间混合机制和Transformer的MLP，具有高效的循环机制、无需自注意力机制、恒定的显存使用和单GPU可训练性等特点。本项目还提供了在AMD Radeon GPU上的推理方法，以及模型的使用示例和性能基准测试。

🚀 快速开始

环境准备

在AMD Radeon GPU上使用llama.cpp进行推理，可按以下步骤操作：

git clone https://github.com/MollySophia/llama.cpp.git -b rwkv-v7
cd llama.cpp
HIPCXX="$(hipconfig -l)/clang" HIP_PATH="$(hipconfig -R)" \
    cmake -S . -B build -DGGML_HIP=ON -DAMDGPU_TARGETS=gfx1030 -DCMAKE_BUILD_TYPE=Release \
    && cmake --build build --config Release -- -j 16
cd ./build/bin 

模型转换

将safetensor模型转换为gguf格式：

python ./convert_hf_to_gguf.py [model_dir]

模型量化

对模型进行量化：

./llama-quantize [model_dir] [Quantization accuracy]

在Webui中推理

使用llama-server在Webui中推理模型：

/llama-server -m [model_dir] -t [use_cpu_thread_number] -ngl 99 --host [host_number] --port [port_number]

注意：Radeon 7000系列使用gfx1100，Radeon 6000系列使用gfx1030。

在Nvidia GPU上推理

git clone https://github.com/MollySophia/llama.cpp.git -b rwkv-v7
cd llama.cpp
cmake -B build -DGGML_CUDA=ON
cmake --build build --config Release
cd ./build/bin 

模型转换

python ./convert_hf_to_gguf.py [model_dir]

模型量化

./llama-quantize [model_dir] [Quantization_accuracy]

在Webui中推理

/llama-server -m [model_dir] -t [use_cpu_thread_number] -ngl 99 --host [host_number] --port [port_number]

✨ 主要特性

模型特性

• 高效循环机制：采用RWKV-7的高效循环机制。
• 无自注意力机制：完全O(n)复杂度，无需自注意力机制。
• 恒定显存使用：显存使用恒定，可在单GPU上训练。

未来规划

• 长上下文能力：即将开源支持16k+上下文的增强版本。
• 数学能力提升：针对数学问题进行特定改进。
• 强化推理模型：通过强化学习提升推理能力。

📦 安装指南

pip3 install --upgrade transformers rwkv-fla

训练前需设置环境变量：

export WKV_MODE=chunk

💻 使用示例

基础用法

from transformers import AutoModelForCausalLM, AutoTokenizer
import threading

model = AutoModelForCausalLM.from_pretrained(
    "RWKV-Red-Team/ARWKV-R1-7B",
    device_map="auto",
    torch_dtype=torch.float16,
    trust_remote_code=True,
)
tokenizer = AutoTokenizer.from_pretrained(
    "RWKV-Red-Team/ARWKV-R1-7B"
)

system_prompt = "You are a world class trivia AI - provide accurate, succinct responses. "
messages = [
    {"role": "system", "content": system_prompt},
    {"role": "user", "content": prompt}]

text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
text = text + "<think>"
print(text)
model_inputs = tokenizer([text], return_tensors="pt").to(device)

streamer = TextIteratorStreamer(tokenizer, skip_prompt=False, skip_special_tokens=False)

generation_kwargs = dict(model_inputs, streamer=streamer, max_new_tokens=8192, do_sample=True,tokenizer=tokenizer,stop_strings=["<｜end▁of▁sentence｜>"])
thread = threading.Thread(target=model.generate, kwargs=generation_kwargs)
thread.start()

print("Streaming output:")
for new_text in streamer:
    print(new_text, end="", flush=True)

thread.join()

输出示例

<｜begin▁of▁sentence｜>You are a world class trivia AI - provide accurate, succinct responses. <｜User｜>The world's largest rainforest, home to approximately three million species of plants and animals, is named after which river?<｜Assistant｜><think>
Okay, so I'm trying to solve this question about the world's largest rainforest and which river it's named after. Hmm, first, I think rainforest names often have links related to the region it's in. The most famous rainforest in the world is the Amazon. I remember hearing a lot about it being called that because rainforests are connected to specific river systems. 

Now, I'm trying to recall which river is named after the Amazon. I think it's the Amazon River. But I want to be sure. Let me see... the Amazon is a major rainforest located in South America. The Amazon River flows through it, which is why it's named after it. That makes sense because it's a very important river. I recall reading somewhere that all the rainforests are named after rivers related to their regions. So if the Amazon is named after its River, then the name would naturally be related to its source.

I wonder if it's the Amazon itself that's named after it, or another river named after it. But the official name for the Amazon is the Amazon Rainforest. The most significant rainforest in the world is the Amazon, and its name probably started with river-sounding names.
</think>

The largest rainforest located in South America is the Amazon. It is named after the river named after it, which is the Amazon River. Therefore, the Amazon River is the name given to the Amazon Rain Forest.

📚 详细文档

性能基准测试

关键特性

架构亮点

核心修改流程

Transformer Decoder Layer:
- Multi-head Latent Attention(MLA)
+ RWKV-7 Time Mixing (Eq.3)
- RoPE Positional Encoding
+ State Recurrence
= Hybrid Layer Output

📄 许可证

本项目采用Apache-2.0许可证。