ARWKV R1 7B

🚀 ARWKV🪿

このモデルは、RWKV-7の時間混合とTransformerのMLPを備えたテキスト生成モデルです。RWKV-7の効率的な再帰メカニズムを活用し、VRAM使用量が一定で、シングルGPUでのトレーニングが可能です。

🚀 クイックスタート

AMD Radeon GPUでの推論

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 

safetensorモデルをggufに変換

python ./convert_hf_to_gguf.py [model_dir]

モデルの量子化

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

llama-serverを使用したWebUIでのモデル推論

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

Radeon 7000シリーズはgfx1100を、Radeon 6000シリーズはgfx1030を使用します。

Nvidia GPUでの推論

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 

safetensorモデルをggufに変換

python ./convert_hf_to_gguf.py [model_dir]

モデルの量子化

./llama-quantize [model_dir] [Quantization_accuracy]

llama-serverを使用したWebUIでのモデル推論

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

✨ 主な機能

• 効率的な再帰メカニズム：RWKV-7の時間混合を使用し、効率的な再帰処理が可能です。
• 低VRAM使用量：VRAM使用量が一定で、シングルGPUでのトレーニングが可能です。
• 高速推論：自注意力機構を使用せず、完全にO(n)の計算量です。

📦 インストール

pip3 install --upgrade transformers rwkv-fla

トレーニング前に、以下のコマンドを実行します。

export WKV_MODE=chunk

💻 使用例

基本的な使用法

from transformers import AutoModelForCausalLM, AutoTokenizer


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()

出力例は以下の通りです。

<｜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.

📚 ドキュメント

ベンチマーク

主要機能の詳細

アーキテクチャのハイライト

コア変更フロー

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

📄 ライセンス

このプロジェクトは、Apache-2.0ライセンスの下で公開されています。