Other LORA models
philwee opened this issue · 14 comments
Would it be possible to support other LORA adapters?
For example, I've finetuned llama on alpaca + dolly (https://huggingface.co/couchpotato888/dolpaca_gpt4_13b_1e_adapter/tree/main) but I can't seem to use it on your Colab (it tells me it's unsupported) - it would be really nice if I could use your interface with my finetune.
Thanks for the great work on it btw, the interface looks really nice!
sure thing! @philwee
It shows "unsupported" since I have not tested the model. Please let me know where the model is(apparently you pointed it), well working cases(or how to structure the prompts). I assume you followed the same structure as in the original Alpaca dataset, yes?
I manage prompt interfaces for different models in different repositories(for example plain Alpaca), if you think this one exactly matches to yours, please let me know
It's based on basically plain alpaca, so it should work with the same stuff as alpaca - happy to test it once support is given (i have both a 7b and 13b adapter). Thank you for your help!
Absolutely! Will let you know when I integrate them :) pretty soon
hey @philwee ,
can you share the GenerationConfig that you used?
When I say "hello", it gives me such as (too) long responses which is weird, and I can even easily find some weird words or ending tokens.
Generated text on "hello"
Hello, what is your name? | What can I do for you today.J it depends a lot on how specific are you with detail! Tell me your need.
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hi @deep-diver - I tested it using tloen/alpaca-lora's generate.py, which defaults to using following prompt:
{
"description": "Template used by Alpaca-LoRA.",
"prompt_input": "Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request.\n\n### Instruction:\n{instruction}\n\n### Input:\n{input}\n\n### Response:\n",
"prompt_no_input": "Below is an instruction that describes a task. Write a response that appropriately completes the request.\n\n### Instruction:\n{instruction}\n\n### Response:\n",
"response_split": "### Response:"
}
More info can be found here: alpaca-lora prompts
Right now, I just tested it - when I give it the input of "hello", I get "Hello! How can I help you today?", which seems alright - see below:
Please let me know if this works, otherwise, happy to try to help.
I see, the Generation config is so different. let me check
I think I can make this model works with just a simple case (1, 1, 1 instruction, input, and response). I have found that it is hard to inject past conversation history in the input, so I decided not to use that template in this project.
However, in your model's case, it looks it doesn't work if I don't follow the standard template format. hm... gotta think about this any further to make your model to work better.
I think it was intended to be used in an alpaca like style anyway (1 input, 1 output) - and the data that it was trained on was like that anyway. For now, I think that simple case would still be nice to have! :)
I can also try to fine tune another version that focuses on a more conversational style - I'll update you once I've worked on it.
For sure! How would you format dataset to reflect conversational style?
probably I will include your model as a single turn conversation since I plan to add StackLLaMA as well
So I initially finetuned it using data in the format instruction-input-output (as is needed by alpaca-lora)
For example:
[
{
"instruction": "Give three tips for staying healthy.",
"input": "",
"output": "1. Eat a balanced and nutritious diet: Make sure your meals are inclusive of a variety of fruits and vegetables, lean protein, whole grains, and healthy fats. This helps to provide your body with the essential nutrients to function at its best and can help prevent chronic diseases.\n\n2. Engage in regular physical activity: Exercise is crucial for maintaining strong bones, muscles, and cardiovascular health. Aim for at least 150 minutes of moderate aerobic exercise or 75 minutes of vigorous exercise each week.\n\n3. Get enough sleep: Getting enough quality sleep is crucial for physical and mental well-being. It helps to regulate mood, improve cognitive function, and supports healthy growth and immune function. Aim for 7-9 hours of sleep each night."
}
]
A more conversation like format would involve data that looks something like this (note the delimiters [|Human|] and [|AI|])
[
{
"topic": "Give three tips for staying healthy.",
"input": "The conversation between human and AI assistant.\n[|Human|] Give three tips for staying healthy.\n[|AI|] 1.Eat a balanced diet and make sure to include plenty of fruits and vegetables. \n2. Exercise regularly to keep your body active and strong. \n3. Get enough sleep and maintain a consistent sleep schedule.\n[|Human|] "
}
]
(in my testing, the training with the conversation style-like data appears to result in better chat output, especially more than 1q+1a, than the instruction-input-output format)
probably I will include your model as a single turn conversation since I plan to add
StackLLaMAas well
cool, thank you! stack llama is a nice approach too (in that it uses RLHF)
Update: I've finetuned another model with data in a more conversational format: https://huggingface.co/couchpotato888/baize7bdollylora
(here it is in adapter format, lmk if you need it in hf format)
This one should work better for chat use, ex:
