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--- |
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license: gpl-3.0 |
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datasets: |
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- JosephusCheung/GuanacoDataset |
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- yahma/alpaca-cleaned |
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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-leh-V2: A Multilingual Instruction-Following Language Model Based on LLaMA 7B |
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This model is trained with [guanaco-lora](https://github.com/KohakuBlueleaf/guanaco-lora) with lora + embed_tokens + lm_head be trained. |
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The dataset is from [alpaca-cleaned](https://huggingface.co./datasets/yahma/alpaca-cleaned) 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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## Difference between previous model |
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The main differences are: |
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* model is trained on bf16 not 8bit |
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* ctx cut off length increased to 1024 |
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* use larger dataset (latest guanaco + alpaca cleand = 540k entries) |
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* use larger batch size (64->128) |
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And since the train data has more chat-based data. |
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This model is more fit in chatbot usage. |
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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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Or using generate.py in the [guanaco-lora](https://github.com/KohakuBlueleaf/guanaco-lora), all the examples are generated by guanaco-lora. |
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If you want to use the lora model from guanaco-7b-leh-v2-adapter/ , remember to turn off the load_in_8bit, or manually merge it into 7B model! |
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### Recommend Generation parameters: |
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* temperature: 0.5~0.7 |
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* top p: 0.65~1.0 |
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* top k: 30~50 |
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* repeat penalty: 1.03~1.17 |
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## Training Setup |
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* 2x3090 with model parallel |
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* batch size = bsz 8 * grad acc 16 = 128 |
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* ctx cut off length = 1024 |
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* only train on output (with loss mask) |
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* enable group of len |
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* 538k entries, 2epoch (about 8400 step) |
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* lr 2e-4 |
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## Some Example |
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(As you can see, although guanaco can reply fluently, the content is quite confusing. So you may want to add some thing in the system part.) |
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I use guanaco with instruction to let it translate a chinese article to JP/DE/EN. |
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And use gpt-4 to scoring them and get this: |
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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. |
