well done ! well explained, I am a data scientist as well and love your videos, a lot of work behind the scenes to bring the koncepts in such simple yet interactive way!! many thanks Shawhin !!
@@ShawhinTalebi somehow I feel like the most appropriate response you should have gave to comments in this thread is with using the ShawGPT LOL, but yeah mad respect for ya, this was great to watch!
I am from math, and a lot of times I felt the videos I saw are too vague. Your videos are very clear, and at the same time easy to understand. This is quite amazing. Appreciate your work!
First I thought omg this video is horrible but its actually excellent! (I wanted a practical fast way to get my LLM finetuned using my own data, but found it really isnt that easy). After this I understood a lot better what is going on in the background.
dear Shaw, i listen to the video so many times, and aside that is extremely well done and i learn so much, you should emphasize (or even do an ad hoc video) the fact that key for the finetuning with "one" gpu is the usage of the "quantifized" model of mistral, overall i m sure that many users, wodul like to know more about this models, i m sure that not many knows how to use the most important LMM (quantized) on their own colab or even pc.....like base of their own application.... :)
I believe the 264M parameters is because you are only targeting one layer ('q_proj'). I don't know what other linear layers Mistral 7B has, but they should be included for best training performance. Correct me if I'm wrong and apologies if this has been answered already. Great video!
Thank you for that video. I was wondering about the fine-tuning process - does the model learn to predict all tokens (include the instructions)? or does it learn to predict only non-instructions tokens?
Good question. The instruction tokens are fed into the model, so it does not predict them. However, the model does learn to identify via fine-tuning so it's important to use a consist structure for the training examples.
I have paid but I used the T4 GPU which is part of the free version. The only difference is the free is subject to access restrictions during peak usage times.
Nice tutorial, but I'm confused - 33:35, why do we pass in the tokenized data there? If I try that, it fails because there's not "test" or "train" in the results. The docs say that's expecting the dataset, not the tokenized data. The results of the tokenize function return input_ids & an attention_mask arrays.
These are the data we will use for training the model. It needs to be tokenized because the base model is expecting the text to be encoded in a specific way. We're you getting an error when running this example on Colab?
Why is it needed to fine-tune the model if we can use prompt engineering to make sure the response of the model fits the conditions we are looking for?
Great question! If prompt engineering provides the needed performance than it may not be needed. However, the key benefit in such cases is lowering inference costs since it can lead to shorter prompts.
While I haven’t tried this, you can definitely do it. The key is to have at least 16GB of RAM (and some patience). Not sure how long it would take. This post might be helpful: www.reddit.com/r/LocalLLaMA/s/uqTTi6CLnS
Thank you for the video! Just a small question. At the end, how would you run inference with your fine-tuned model? Do you save it first to the hub and then load it again? I'm not really sure how to correctly apply the lora adapter to the original model after fine-tuning.
Yes, that's how I do it here! There's example code for this in the Colab under "Load Fine-tuned Model" colab.research.google.com/drive/1AErkPgDderPW0dgE230OOjEysd0QV1sR?usp=sharing
Beautifully explained, thanks!!! When you said, for PEFT "we augment the model with additional parameters that are trainable", how do we add these parameters exactly? Do we add a new layer? Also, when we say "%trainable parameters out of total parameters", doesn't that mean that we are updating a certain % of original parameters?
I explain how LoRA works here: ua-cam.com/video/eC6Hd1hFvos/v-deo.htmlsi=_3PK3Kj4Zxs844qg&t=6 Good question. We do not touch any of the original parameters. This just done to give a sense of the relative computational savings of PEFT.
Thank you for sharing this! Have you tried Fine-tune Mixture of Experts like Mixtral 8x7B. Is the process really different? I want to do some testing of my own in the next week. Do you think this requires the same amount of vram as a 7b model or more? I have a macbook m3 pro max with 128gb of shared memory and a mac studio with 196gb of shared memory.
Thank you SO much for covering this, Sir! Small question: If I want to fine-tune a model to understand a new coding language whose syntax is similar to C++, any loose ideas or direction I would go about it in?
There are many ways you can go about this. While I haven't done anything like that, I'd try taking an existing programming model like CodeLlama and doing self-supervised fine-tuning on example code with docstring-like comments.
@@ShawhinTalebi Thank you so much! Whether QLora would be used there or should I not use any PEFT fine tuning and go for a full fine-tune would depend on experiments I guess.
Great video and explanation! Thanks a lot. For the code, have you tried to use: from transformers import BitsAndBytesConfig nf4_config = BitsAndBytesConfig( load_in_4bit=True, bnb_4bit_quant_type="nf4", bnb_4bit_use_double_quant=True, bnb_4bit_compute_dtype=torch.bfloat16 ) and then add that as quantization configs when loading the model? This would include the other aspects from the QLoRA paper, no?
4-bit is referring to the number of binary digits used to represent the number. 0101 is a random example of a number express in 4-bits which is indeed 5 in the decimal system.
Is it like the base model is stored in 4bit and as the data (X vector) passes through the layer that layer is first dequantized and then the matrix multiplication is done (X*W)? And the same thing for LoRA as well? and after we get Y (by adding output of lora and base layer) the W and LoRA layers are again quantized back to 4bit? and Y is passed on to next layer? Also, if the LoRA is at the base of the model, does that mean to update the parameters of this LoRA we need to calculate the gradients of loss wrt all the W and LoRA matrices above it?
That's a great question. Honestly I'm not entirely sure, but what you said makes sense. For inference weights are dequantized layer by layer so that multiplication is possible with FP16 inputs, and no need to dequant LoRa weights since these are already FP16. No need to compute gradients for original parameters because those are frozen i.e. we treat those as constants.
Glad you liked it! When it comes to fine-tuning, the closest thing would be semi-supervised learning. This could make sense if trying to further train a model on a knowledge base (e.g. sklearn documentation). However, empirically it seems fine-tuning tends to be a less effective way to endow a model with specialized knowledge compared to a RAG system.
Hi everyone, I have a question about data preparation and fine tuning of LLMs. What should the data format look like in the fine tuning process? On the one hand, it can be pure text to add special knowledge to the LLM. On the other hand, the data set can be structured in question and answer / prompt and answer format. What do you think? Do you have any recommendations for me? Thank you and best regards!
Great question. This comes down to the use case. If you want to instill knowledge into the model the former is suitable, while if you want the model to response in a particular format that latter might work better.
How can I fine-tune the LLAMA 3 8B model for free on my local hardware, specifically a ThinkStation P620 Tower Workstation with an AMD Ryzen Threadripper PRO 5945WX processor, 128 GB DDR4 RAM, and two NVIDIA RTX A4000 16GB GPUs in SLI? I am new to this and have prepared a dataset for training. Is this feasible?
That's a lot of firepower! You should be able to do full fine-tuning with that set up. Perhaps you can try using the example code as a jumping off point.
Your explanation on this topic is amazing bro. Can you make a video on how to train model on our custom data (to be specific excel/csv data) using open source model like llama3. many are explaining on how to train datasets that have only input and output but no one is explaining about how to train on excel/csv data. It ill be helpful to my project if can make a video of it.
@@ShawhinTalebi For example i have an excel data of different companies and their investments , yearly profit and sales growth etc.. now i want to train my own model such that if i ask any question from the data it should give me answer. (ex. what is the current price of the company xyz. output : $123M)
@A_Aphid there are some depreciation warnings. Plus, I was hoping for some information about any newer models to use. Could you suggest any newer model?
@@anmolshah Yea,, your right about them warnings.. And no I don't really have many suggestions on models. I'm just starting to get into AI so I'm not the best.. Sorry man
I haven't made any updates yet. However, feel free to take the code and hack it to try it with different models. I plan to do future videos using with Llama3 and other fine-tuning approaches.
@@ShawhinTalebi Will you make an videos on how to finetune models without requiring a NVIDIA GPU (since auto-gptq requires CUDA) so that they can run locally? Or is that just not recommended for AI? (Also great video it taught me a lot👍)
dear Shaw, i m passionated old guy (i m 54 :)) in AI, is amazing how u can explain in simple words concepts , that even an old Mammoth like me can understand , of course , being a total "artisan" in this field , my job is totally different, i m facing problems that will look very simple at your eyes, usually i ask support to chat gpt 4 to learn , understand and correct, but this argument and some of the python libreries are too recent and not yet in the last version of chat gpt 4 , so i need your help, i m not using COLAB becosue i have already similar set on my machine, (16 + 16 like in your example) and i dowload both the model and the data set in my machine , but i m getting this error : ImportError: Found an incompatible version of auto-gptq. Found version 0.3.1, but only version above 0.4.99 are supported i tried to upgrade my version but seems no :ERROR: No matching distribution found for auto-gptq== (any higher then 0.3.1) how can solve the problem?
It seems like an issue setting up the environment. You can try manually setting the package versions when installing them on your machine based on the Google Colab code.
@@ShawhinTalebi I believe that @edsonjr6972 is right and that the trainable parameters is reduced significantly, because you are not _just_ targeting only certain layers, but also you are using LoRA decomposition to smaller low-rank matrices, and so the 264M is the probably the number of all of the parameters in the `q_proj` layers and then the 2M is the ~1% of those parameters that you are actually training due to LoRA
When I tried this, I got this Exception: Cannot copy out of meta tensor; no data! This happens in this step: NotImplementedError Traceback (most recent call last) Cell In[45], line 2 1 # configure trainer ----> 2 trainer = transformers.Trainer( 3 model=model, 4 train_dataset=tokenized_data["train"], 5 eval_dataset=tokenized_data["test"], 6 args=training_args, 7 data_collator=data_collator 8 ) 10 # train model 11 model.config.use_cache = False # silence the warnings. Please re-enable for inference! Do you have any Idea?
@@ShawhinTalebi Thank you. For others with the same problem: This solved it for me: import sys sys.argv.append("--disable-model-loading-ram-optimization")
Enlightening journey through the intricacies of Large Language Model (LLM) optimization! 🌌🖥 Your adept presentation not only demystifies the process but also serves as a beacon of inspiration for both burgeoning and seasoned developers navigating the vast seas of AI technology. The elegance with which you delineated the nuances of QLoRA and its transformative approach to fine-tuning LLMs on a singular GPU setup is nothing short of revelatory. 📘✨ It's a masterclass in making advanced AI technologies accessible and practical for a wider audience, empowering individuals to harness the full potential of LLMs without the necessity for extensive computational resources.
fp16=true causes training to fail with error "No inf checks were recorded for this optimizer.". Set fp16=false and training successfully completed but loss and eval loss are the same for every epoch.
I am trying to fine tune on my own dataset with 20000 messages in format: msg_id, sender_id, content, reply_to, interval (between this and previous message) to generate similar messages with similar format.
This video goes pretty deep into the technical details. Watching some of the previous video in the series might help give more context. I also do office hours if you have any specific questions: calendly.com/shawhintalebi/office-hours
i get this error OSError: TheBloke/dolphin-2.6-mistral-7B-dpo-laser-GGUF does not appear to have a file named pytorch_model.bin, tf_model.h5, model.ckpt or flax_model.msgpack. ¿que hago? :c
@@ShawhinTalebi I solved it, you must put the correct model in the colab that is similar to the one you have, I still don't know how to make a meta for hugging face :c
👉More on LLMs: ua-cam.com/play/PLz-ep5RbHosU2hnz5ejezwaYpdMutMVB0.html
--
References
[1] Fine-tuning LLMs: ua-cam.com/video/eC6Hd1hFvos/v-deo.html
[2] ZeRO paper: arxiv.org/abs/1910.02054
[3] QLoRA paper: arxiv.org/abs/2305.14314
[4] Phi-1 paper: arxiv.org/abs/2306.11644
[5] LoRA paper: arxiv.org/abs/2106.09685
well done ! well explained, I am a data scientist as well and love your videos, a lot of work behind the scenes to bring the koncepts in such simple yet interactive way!! many thanks Shawhin !!
@@mouadkrikbou4596 Thanks! This one took longer than usual to put together, so glad you enjoyed it :)
@@ShawhinTalebi somehow I feel like the most appropriate response you should have gave to comments in this thread is with using the ShawGPT LOL, but yeah mad respect for ya, this was great to watch!
I am from math, and a lot of times I felt the videos I saw are too vague. Your videos are very clear, and at the same time easy to understand. This is quite amazing. Appreciate your work!
wow, you are the genius of explaining super hard math concept into layman understandable terms with good visual representation. Keep it coming.
So far the best explanation on UA-cam about this topic
Funny how this comment is under every LLM video :D
Your explanations are amazing and the content is great. This is the best playlist on LLMs on UA-cam.
Amazing work Shaw - complex concepts broken down to 'bit-sized bytes' for humans. Appreciate your time & efforts :)
thank you so much for the lecture. It really helps me understand the concept of QLoRA.
First I thought omg this video is horrible but its actually excellent! (I wanted a practical fast way to get my LLM finetuned using my own data, but found it really isnt that easy). After this I understood a lot better what is going on in the background.
Glad it was helpful :)
Thank you Shaw for yet another awesome video succinctly explaining complex topics!
Happy to help!
Great content, thank you!
Another fire video in the books!
Thanks! 🔥🔥😂
dear Shaw, i listen to the video so many times, and aside that is extremely well done and i learn so much, you should emphasize (or even do an ad hoc video) the fact that key for the finetuning with "one" gpu is the usage of the "quantifized" model of mistral, overall i m sure that many users, wodul like to know more about this models, i m sure that not many knows how to use the most important LMM (quantized) on their own colab or even pc.....like base of their own application.... :)
Thanks for the great suggestion! I'm organizing a fine-tuning series and will be sure to discuss quantization a bit more :)
This is the best explanation that i've ever heard, thanks for all the work!!
Thank you for this amazing video, great explanations, very clear and easy to understand!
Very intersting video even for more experienced watchers
Amazing video ! You are the best, man ! Thank you so much.
Exactly what I was looking for! Thanks for the video. Keep going!
Great to hear :)
Great video and your slides are very well organized!
Glad you like them!
Learned a lot. Great video and very accessible. Well Done!
Great to hear! Glad it was helpful :)
I believe the 264M parameters is because you are only targeting one layer ('q_proj'). I don't know what other linear layers Mistral 7B has, but they should be included for best training performance. Correct me if I'm wrong and apologies if this has been answered already.
Great video!
thank u for sharing this knowledge , we need more videos like this
Happy to help! More to come :)
Loved this, very informative and clear!
Thanks Aldo!
Amazing work! Thanks mate :)
Amazing explanation!!! Thank you Shaw!
Happy to help!
Thank you for that video.
I was wondering about the fine-tuning process - does the model learn to predict all tokens (include the instructions)? or does it learn to predict only non-instructions tokens?
Good question. The instruction tokens are fed into the model, so it does not predict them. However, the model does learn to identify via fine-tuning so it's important to use a consist structure for the training examples.
Man, you are amazing!
❤ really amazing work
Thank you so much, I have one doubt please, even if we set fp16 = True, still the optimization would happen in fp32 right, like you showed at 20:22
This enables mixed precision training which (to my understanding) uses FP16 to do most operations and only stores the optimizer states in FP32.
@@ShawhinTalebi got it, Thank you
This was a value bomb!
Great content, Thank you.
Great content!!
Thanks for this!!
Thanks Shaw
Awesome, thank you
Thank you so much for the video! Just one question, did you use free colab or colab pro or did you pay for the gpu? thank you so much!
I have paid but I used the T4 GPU which is part of the free version. The only difference is the free is subject to access restrictions during peak usage times.
@@ShawhinTalebi Thank you so much!!
Nice tutorial, but I'm confused - 33:35, why do we pass in the tokenized data there? If I try that, it fails because there's not "test" or "train" in the results. The docs say that's expecting the dataset, not the tokenized data. The results of the tokenize function return input_ids & an attention_mask arrays.
These are the data we will use for training the model. It needs to be tokenized because the base model is expecting the text to be encoded in a specific way.
We're you getting an error when running this example on Colab?
Why is it needed to fine-tune the model if we can use prompt engineering to make sure the response of the model fits the conditions we are looking for?
Great question! If prompt engineering provides the needed performance than it may not be needed. However, the key benefit in such cases is lowering inference costs since it can lead to shorter prompts.
Is it feasible to train 7b qlora model on cpu only?
While I haven’t tried this, you can definitely do it. The key is to have at least 16GB of RAM (and some patience). Not sure how long it would take.
This post might be helpful: www.reddit.com/r/LocalLLaMA/s/uqTTi6CLnS
Great Video!!
How much GPU memory did you need in the end to fine tune mistral 7b?
Glad you liked it!
It runs on Colab with 12.7GB system RAM and 15GB GPU RAM. I don't think it went above 10GB of GPU utilization.
Thank you for the video! Just a small question. At the end, how would you run inference with your fine-tuned model? Do you save it first to the hub and then load it again?
I'm not really sure how to correctly apply the lora adapter to the original model after fine-tuning.
Yes, that's how I do it here! There's example code for this in the Colab under "Load Fine-tuned Model"
colab.research.google.com/drive/1AErkPgDderPW0dgE230OOjEysd0QV1sR?usp=sharing
Beautifully explained, thanks!!!
When you said, for PEFT "we augment the model with additional parameters that are trainable", how do we add these parameters exactly? Do we add a new layer?
Also, when we say "%trainable parameters out of total parameters", doesn't that mean that we are updating a certain % of original parameters?
I explain how LoRA works here: ua-cam.com/video/eC6Hd1hFvos/v-deo.htmlsi=_3PK3Kj4Zxs844qg&t=6
Good question. We do not touch any of the original parameters. This just done to give a sense of the relative computational savings of PEFT.
Thanks for sharing. I have a question, instead of quantized model, can i load the base mistral model and follow this process ?
Yes, given that you have enough memory for the model.
Thank you for sharing this! Have you tried Fine-tune Mixture of Experts like Mixtral 8x7B. Is the process really different? I want to do some testing of my own in the next week. Do you think this requires the same amount of vram as a 7b model or more? I have a macbook m3 pro max with 128gb of shared memory and a mac studio with 196gb of shared memory.
I haven't played with Mixtral 8x7B yet, so I don't have much insight. Hope to cover this in a future video :)
I just did a similar example on Mac: ua-cam.com/video/3PIqhdRzhxE/v-deo.html
Given your specs, you have more than enough to fine-tune Mixtral 8x7B
nice video bro
Thank you SO much for covering this, Sir!
Small question: If I want to fine-tune a model to understand a new coding language whose syntax is similar to C++, any loose ideas or direction I would go about it in?
There are many ways you can go about this. While I haven't done anything like that, I'd try taking an existing programming model like CodeLlama and doing self-supervised fine-tuning on example code with docstring-like comments.
@@ShawhinTalebi Thank you so much! Whether QLora would be used there or should I not use any PEFT fine tuning and go for a full fine-tune would depend on experiments I guess.
Great video and explanation! Thanks a lot. For the code, have you tried to use:
from transformers import BitsAndBytesConfig
nf4_config = BitsAndBytesConfig(
load_in_4bit=True,
bnb_4bit_quant_type="nf4",
bnb_4bit_use_double_quant=True,
bnb_4bit_compute_dtype=torch.bfloat16
)
and then add that as quantization configs when loading the model? This would include the other aspects from the QLoRA paper, no?
Thanks for sharing! I'll need to try that out. I remember running into issues when trying this on my first pass.
at 8:30 there is written 4 bit equals to 0101 which is not true because that binary number is 5 in decimal and 4 is 0100.
4-bit is referring to the number of binary digits used to represent the number. 0101 is a random example of a number express in 4-bits which is indeed 5 in the decimal system.
Good stuff
Is it like the base model is stored in 4bit and as the data (X vector) passes through the layer that layer is first dequantized and then the matrix multiplication is done (X*W)? And the same thing for LoRA as well? and after we get Y (by adding output of lora and base layer) the W and LoRA layers are again quantized back to 4bit? and Y is passed on to next layer?
Also, if the LoRA is at the base of the model, does that mean to update the parameters of this LoRA we need to calculate the gradients of loss wrt all the W and LoRA matrices above it?
That's a great question. Honestly I'm not entirely sure, but what you said makes sense. For inference weights are dequantized layer by layer so that multiplication is possible with FP16 inputs, and no need to dequant LoRa weights since these are already FP16.
No need to compute gradients for original parameters because those are frozen i.e. we treat those as constants.
Hey would this model work if i wanted to input a DNA sequence for example ATCGTGC and the model to repond with the gene name (for example Gene X)?
I don't know honestly, but it's worth a try. LLMs have a funny way of surprising us.
Thank you for sharing this fantastic video! Would it be worthwhile to explore a similar approach using unsupervised learning?
Glad you liked it! When it comes to fine-tuning, the closest thing would be semi-supervised learning. This could make sense if trying to further train a model on a knowledge base (e.g. sklearn documentation). However, empirically it seems fine-tuning tends to be a less effective way to endow a model with specialized knowledge compared to a RAG system.
Hi everyone,
I have a question about data preparation and fine tuning of LLMs. What should the data format look like in the fine tuning process? On the one hand, it can be pure text to add special knowledge to the LLM. On the other hand, the data set can be structured in question and answer / prompt and answer format.
What do you think? Do you have any recommendations for me?
Thank you and best regards!
Great question. This comes down to the use case. If you want to instill knowledge into the model the former is suitable, while if you want the model to response in a particular format that latter might work better.
Thank you for this great video! If you find a way to get this working on Apple silicon machines, we would love to see a video about it!
Thanks for the suggestion! Once I get something working I'll be sure to share it.
What's the loss function for this NLP task? I mean, What is the quantitative measure that determines a good response from a bad one?
I believe cross entropy is used here.
@@ShawhinTalebi Cheers, I'll look it out. Amazing content Shaw!
Hi,
how long did it take you to you to fintune Mistral in this example?
Took about 10 min to run in Colab
is the dropout set to 0.05 or 0.5? I heard you say 0.5, but the codes shows 0.05.
I probably misspoke. The truth is in the code
How can I fine-tune the LLAMA 3 8B model for free on my local hardware, specifically a ThinkStation P620 Tower Workstation with an AMD Ryzen Threadripper PRO 5945WX processor, 128 GB DDR4 RAM, and two NVIDIA RTX A4000 16GB GPUs in SLI? I am new to this and have prepared a dataset for training. Is this feasible?
That's a lot of firepower! You should be able to do full fine-tuning with that set up. Perhaps you can try using the example code as a jumping off point.
Your explanation on this topic is amazing bro.
Can you make a video on how to train model on our custom data (to be specific excel/csv data) using open source model like llama3.
many are explaining on how to train datasets that have only input and output but no one is explaining about how to train on excel/csv data. It ill be helpful to my project if can make a video of it.
Great suggestion! Could you share more about the the use case you have in mind?
@@ShawhinTalebi For example i have an excel data of different companies and their investments , yearly profit and sales growth etc.. now i want to train my own model such that if i ask any question from the data it should give me answer. (ex. what is the current price of the company xyz. output : $123M)
Have you solved the mac issue? Thanks!
Lemme know as well, I was pretty bummed when I found out bitsandbytes doesn't work on M2
Not yet. However, that Llama3 is out I have an excuse to spend more time with it. Hope to revisit this in June.
Yes! ua-cam.com/video/3PIqhdRzhxE/v-deo.html
how do we evalute the model with blue score after training plz make a video or notebook
Thanks for the suggestion! I plan to do a video on LLM eval, and I'll be sure to touch on this.
Any idea if GPTQ support is coming to Mac M1 at some point?
I doubt it. There is an alternative format that works on Mac called GGUF.
@@ShawhinTalebi- thanks
I can't save this video, do you know why, can you please enable saving videos to playlist
That's strange. Are you still having this issue?
No I can save it now :), thanks a lot
the video is now 3 months old, are ther any updates to the shown code?
I just tried it and it's working perfectly!
@A_Aphid there are some depreciation warnings.
Plus, I was hoping for some information about any newer models to use.
Could you suggest any newer model?
@@anmolshah Yea,, your right about them warnings.. And no I don't really have many suggestions on models. I'm just starting to get into AI so I'm not the best.. Sorry man
I haven't made any updates yet. However, feel free to take the code and hack it to try it with different models.
I plan to do future videos using with Llama3 and other fine-tuning approaches.
@@ShawhinTalebi Will you make an videos on how to finetune models without requiring a NVIDIA GPU (since auto-gptq requires CUDA) so that they can run locally? Or is that just not recommended for AI? (Also great video it taught me a lot👍)
dear Shaw, i m passionated old guy (i m 54 :)) in AI, is amazing how u can explain in simple words concepts , that even an old Mammoth like me can understand , of course , being a total "artisan" in this field , my job is totally different, i m facing problems that will look very simple at your eyes, usually i ask support to chat gpt 4 to learn , understand and correct, but this argument and some of the python libreries are too recent and not yet in the last version of chat gpt 4 , so i need your help, i m not using COLAB becosue i have already similar set on my machine, (16 + 16 like in your example) and i dowload both the model and the data set in my machine , but i m getting this error : ImportError: Found an incompatible version of auto-gptq. Found version 0.3.1, but only version above 0.4.99 are supported i tried to upgrade my version but seems no :ERROR: No matching distribution found for auto-gptq== (any higher then 0.3.1) how can solve the problem?
It seems like an issue setting up the environment. You can try manually setting the package versions when installing them on your machine based on the Google Colab code.
@@ShawhinTalebi i will try and let u know tks for feedback
i did it!!! following all your steps in colab tks a lot!!!!
my guess is that the q_proj has 264M parameters, and thats why it's showing only that.
Wouldn't that make it 264M trainable parameters then?
@@ShawhinTalebi The training is for a smaller low rank matrix.
Not for this reason, you can try changing target_modules to see changes in training parameters
@@ShawhinTalebi I believe that @edsonjr6972 is right and that the trainable parameters is reduced significantly, because you are not _just_ targeting only certain layers, but also you are using LoRA decomposition to smaller low-rank matrices, and so the 264M is the probably the number of all of the parameters in the `q_proj` layers and then the 2M is the ~1% of those parameters that you are actually training due to LoRA
@@BenLewisE Thanks for the clarification!
what is the minium vram spec for this tutorial
Runs on Google Colab using 13GB of memory (6.5 CPU RAM + 6.5 VRAM).
When you say "memory" do you mean RAM or VRAM?
Both! QLoRA specifically uses Nvidia's unified memory feature.
When I tried this, I got this Exception:
Cannot copy out of meta tensor; no data!
This happens in this step:
NotImplementedError Traceback (most recent call last)
Cell In[45], line 2
1 # configure trainer
----> 2 trainer = transformers.Trainer(
3 model=model,
4 train_dataset=tokenized_data["train"],
5 eval_dataset=tokenized_data["test"],
6 args=training_args,
7 data_collator=data_collator
8 )
10 # train model
11 model.config.use_cache = False # silence the warnings. Please re-enable for inference!
Do you have any Idea?
This link might be helpful: github.com/AUTOMATIC1111/stable-diffusion-webui/issues/13087
@@ShawhinTalebi Thank you. For others with the same problem: This solved it for me:
import sys
sys.argv.append("--disable-model-loading-ram-optimization")
Does it work with GGUF models ?
I didn't try it, but I'm sure there is a way to do that.
It's 264M parameters because it's the only ones which are trainable. Rest ones are frozen.
Frozen from LoRA or something else?
@@ShawhinTalebi Like the main model parameters are frozen except LoRAs parameters. Maybe that's why
Enlightening journey through the intricacies of Large Language Model (LLM) optimization! 🌌🖥 Your adept presentation not only demystifies the process but also serves as a beacon of inspiration for both burgeoning and seasoned developers navigating the vast seas of AI technology.
The elegance with which you delineated the nuances of QLoRA and its transformative approach to fine-tuning LLMs on a singular GPU setup is nothing short of revelatory. 📘✨ It's a masterclass in making advanced AI technologies accessible and practical for a wider audience, empowering individuals to harness the full potential of LLMs without the necessity for extensive computational resources.
fp16=true causes training to fail with error "No inf checks were recorded for this optimizer.". Set fp16=false and training successfully completed but loss and eval loss are the same for every epoch.
I am trying to fine tune on my own dataset with 20000 messages in format: msg_id, sender_id, content, reply_to, interval (between this and previous message) to generate similar messages with similar format.
Are you running the provided script in Colab?
@@ShawhinTalebi no, on my own machine
Same here, but training doesn't take effect, i got the same answer after training
i changed the torch version to match colab via pip install torch==2.1.0, and it work
Getting Key Error: Mistral
I'm not able to replicate that error. Are you running the example in Colab?
I think he didn't log in with api key
im so fucking lost
This video goes pretty deep into the technical details. Watching some of the previous video in the series might help give more context. I also do office hours if you have any specific questions: calendly.com/shawhintalebi/office-hours
i get this error OSError: TheBloke/dolphin-2.6-mistral-7B-dpo-laser-GGUF does not appear to have a file named pytorch_model.bin, tf_model.h5, model.ckpt or flax_model.msgpack. ¿que hago? :c
Not sure, I haven't come across that one before
@@ShawhinTalebi I solved it, you must put the correct model in the colab that is similar to the one you have, I still don't know how to make a meta for hugging face :c
Great video, thanks !
Additionally, probably also pruning was performed beside quantization in order to get such a low amount of trainable parameters.
Thanks for the tip! I'll need to dig into that.