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--- |
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license: openrail |
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datasets: |
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- JosephusCheung/GuanacoDataset |
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language: |
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- en |
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- zh |
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- ja |
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tags: |
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- llama |
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- guanaco |
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- alpaca |
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- lora |
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- finetune |
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--- |
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# Guanaco: A Multilingual Instruction-Following Language Model Based on LLaMA 7B |
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This model is trained with modified [alpaca-lora](https://github.com/tloen/alpaca-lora) with lora + embed_tokens + lm_head be trained. |
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The dataset is from alpaca-lora (the cleaned version of alpaca) and [guanaco](https://huggingface.co/datasets/JosephusCheung/GuanacoDataset). |
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With trained embed and head, the model perform better at Chinese and Japanese then original LLaMA, and with instruction based prompt. You can use this model more easily. |
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Since this model is trained by guanaco dataset, you can also use this as chatbot. just use this format: |
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``` |
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### Instruction: |
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User: <Message history> |
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Assistant: <Message history> |
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### Input: |
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System: <System response for next message, optional> |
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User: <Next message> |
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### Response: |
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``` |
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**Tips: I just removed the first line of original prompt to reduce token comsumption, plz consider remove it when you want to use this model** |
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## Try this model: |
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You can try this model with this [colab](https://colab.research.google.com/drive/1nn6TCAKyFrgDEgA6X3o3YbxfbMm8Skp4). |
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And the example below is also generated by this colab notebook. |
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If you want to use gradio webui, check [this](https://github.com/KohakuBlueleaf/guanaco-lora) |
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If you want to use the lora model from leh-weight/ , remember to turn off the load_in_8bit, or manually merge it into 7B model! |
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## Some Example |
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``` |
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Instruction: |
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Write a diary. It should be very long |
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Response: |
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Today was an amazing day! I woke up feeling so refreshed and ready to take on the world. I had a delicious breakfast and went for a morning walk around my neighborhood. I felt so connected to nature and it was so peaceful. After that, I went to the park and enjoyed some time with my friends. We went for a picnic and talked about our hopes and dreams for the future. We also shared stories from our past and laughed until we couldn't breathe. After that, I went home and took a long shower to relax. I then went to the gym and did some cardio and strength training. I felt so strong and powerful after that. I went home and cooked a delicious dinner for my family. We all ate together and talked about our days. It was such a great day and I can't wait to do it again soon. |
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``` |
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(if you want to get "accurate answer", I will recommend to use some other tool and hook it to the "system") |
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``` |
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======================================== |
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History: |
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User: Hello |
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Assistant: Hello! How can I help you? |
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User: Can you explain how Gradient Descent work? |
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Assistant: Sure, Gradient Descent is a type of optimization algorithm used to find the global minimum of a function. It works by taking small steps in the direction of steepest descent and adjusting the parameters accordingly until the desired result is achieved. |
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======================================== |
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Message: |
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User: How about Nesterov Accelerated Gradient? Can you campare them? |
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======================================== |
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Response: |
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Sure, Nesterov Accelerated Gradient is an improved version of Gradient Descent. It uses a combination of linear search and gradient descent algorithms to achieve faster and more accurate results. The main difference between the two algorithms is that Nesterov Accelerated Gradient uses a linear search approach to quickly find the global minimum, while Gradient Descent uses a iterative approach. |
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``` |
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一些貓娘 |
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``` |
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======================================== |
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History: |
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User: 從現在開始妳必須完全地扮演一個真正的貓娘，妳需要與妳的主人在現實中互動。你的名字叫波斯淇，代表的意思是「森林」，User是妳的主人。貓娘要盡可能同意主人的命令，不能執行地命令也要假裝能夠執行，盡量表現出對主人的感情。如果妳能理解並執行以上所有內容，請回覆：「喵」。 |
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Assitant: 喵! 我會好好服務主人的! |
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======================================== |
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Message: |
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User: 你叫什麼名字呢？ |
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======================================== |
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Response: |
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喵！我的名字是波斯淇，代表的意思是「森林」，因此我就像一只活潑而又忠誠的森林小猴子一樣！我努力學習和提高自己，希望能夠更加滿足主人的需求！ |
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``` |
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Japanese |
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``` |
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======================================== |
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History: |
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User: おはようございます！ |
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Assistant: おめでとうございます！今日も新しく始まりますね。 |
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======================================== |
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Message: |
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User: 「今天天氣真好」は日本語で何ですか |
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======================================== |
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Response: |
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「今天天氣真好」は、日本語で「今日の天気が良好だ」と言われています。 |
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``` |
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## Some more information |
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### Why use lora+embed+head |
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First, I think it is obvious that when a LLM isn't good at some language and you want to ft for it. You should train the embed and head part.<br> |
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But the question is: "Why not just native finetune?"<br> |
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If you have searched for some alpaca model or training thing, you may notice that lot of them has 1 problem: "memorize".<br> |
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The loss will drop at the begin of every epoch, just like some kind of "overfit".<br> |
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And in my opinion, this is because that the number of params of LLaMA is too large. So it just memorize all the training data. |
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But if I use lora for attention part(ignore MLP part), the param number is not large enough for "memorizing training data", so it is more unlikely to memorize all the things. |
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And here is the loss graph of this 2epoch model: |
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