Fine-tuning Llama 2 on Your Own Dataset | Train an LLM for Your Use Case with QLoRA on a Single GPU
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- Опубліковано 18 тра 2024
- Full text tutorial (requires MLExpert Pro): www.mlexpert.io/prompt-engine...
Learn how to fine-tune the Llama 2 7B base model on a custom dataset (using a single T4 GPU). We'll use the QLoRa technique to train an LLM for text summarization of conversations between support agents and customers over Twitter.
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00:00 - When to Fine-tune an LLM?
00:30 - Fine-tune vs Retrieval Augmented Generation (Custom Knowledge Base)
03:38 - Text Summarization (our example)
04:14 - Text Tutorial on MLExpert.io
04:47 - Dataset Selection
05:36 - Choose a Model (Llama 2)
06:22 - Google Colab Setup
07:26 - Process data
10:08 - Load Llama 2 Model & Tokenizer
11:18 - Training
14:49 - Compare Base Model with Fine-tuned Model
18:08 - Conclusion
#llama2 #llm #promptengineering #chatgpt #chatbot #langchain #gpt4 #summarization
Full text tutorial (requires MLExpert Pro): www.mlexpert.io/prompt-engineering/fine-tuning-llama-2-on-custom-dataset
Can you send me your email pls I have a question can’t ask in public
I keep having problems with the model.merge_and_unload()...
It seems to be a bit different from the documentation on Hugging Face...is there something I am missing here?
The error says that that 'LlamaForCausalLM' object has no attribute 'merge_and_unload'....
Any ideas?
@@williamfussell1956 Did you fix that?
@@williamfussell1956 merged_model = trained_model.merge_and_unload()
Can you provide the Google Collab notebook?
This is great. A version for question answering would be helpful too.
Awesome work! Thanks a ton!
Good stuff coming, thank you in advance ❤
Excellent work! You are the hero!
Awesome tutorial!
Do you have or plan to make a tutorial for something like bellow?
Tutorial for the plane text fine-tuning and then tuning that model to make it an instruct tuned one?
very helpful. Thanks for the videos.
Super excited
Excellent video! What changes in the input we need to make to use 8 bit quantization instead of 4 bit. Thanks.
Thank you for this! Is finetuning a good approach for a private/proprietary documentation Q&A?
very good video
will you be able to add a tutorial for llama2-chat model
Fantastic video! It will be nice to see a full tutorial on how to do it with pdf locally...
Any idea how can we deploy llama-2 on huggingface api? just like the falcon one, has some issue with the handler.
Incredible video!! Thank you very much, I have a question: isn't it mandatory to put characters like EOS at the end of the summary? for the LLM to finish the instruction?
Super🎉
Great!! Do some videos regarding RLHF.
Do you have an idea how GPT4 is so good with its responses from its base model when I upload documents to it?
Could it be the parameter. size only or do you think other technologies are what determine the quality difference?
🔥
can you train the model on german data?
I still don't get it i have my data locally , how should start finetuning it please tell
Great video!
Is there anyway to build my instruction dataset for instruct fine-tuning from classical text books?
@@user-xt6tu3xt3t but then how to convert in question & answer format?
@@ikurious the best way is manualyl by a human
Thanks for sharing, really helpful. Waiting for my Llama model access to follow it step by step. Can I use any other model in place of this?
Did you get the access? And how long did it take?
Hi there, I am just reading through the repo and Im pretty sure this is the answer...i just wanted to make sure...
The actual input to the model is only from the [text] field, is that correct? As the [text] field contains the prompt, the conversation and the summary...
can i download the finetuned model after finetuning?
is it in format .bin or .safetensor or else?
cuz im current trying to do finetuning on textgen, but having troubles. with dataset (format) i guess.
do you already know how you can download the finetuned model?
Thanks for the insight, is it possible to perform training locally, with 8 GB VRAM?
No
Hola, For me the validation log show No log with mistral instruct model. Please help anyone.
default_factory=lambda: ["q_proj", "v_proj"] Why did you not add this? Is it because HF does under the hood?
I totally forgot about the `target_modules`. I retrained and updated the notebook/tutorial with those. The results are better!
Here's the list:
lora_target_modules = [
"q_proj",
"up_proj",
"o_proj",
"k_proj",
"down_proj",
"gate_proj",
"v_proj",
]
I composed it from here: github.com/huggingface/transformers/blob/f6301b9a13b8467d1f88a6f419d76aefa15bd9b8/src/transformers/models/llama/convert_llama_weights_to_hf.py#L144
Thank you!
Is there a good resource for understanding 'target modules' for different models? @@venelin_valkov
When you say you are tracking loss, what loss is that and how is that loss calculated for the task (summarization) at hand?
I have the same question. @karimbaig8573 were you able to figure out the answer?
Nope.
I need help please. I just want to be pointed in the right direction since I'm new to this and since I couldn't really find any proper guide to summarize the steps for what I want to accomplish.
I want to integrate a LLama 2 70B chatbot into my website. I have no idea where to start. I looked into setting up the environment on one of my cloud servers(Has to be private). Now I'm looking into training/fine-tuneing the chat model using our data from our DBs(It's not clear for me here but I assume it involves two steps, first I have to have the data in a CSV format since it's easier for me, second I will need to format it in Alpaca or Openassistant formats). After that, the result should be a deployment-ready model ?
Just bullet points I'd highly appreciate that.
@nty3929 Oh :/ I’m still lost about this but thank you for your effort nevertheless!
@nty3929 Yeah, bots are ruthless here and youtube is having none of it, even at that cost. Guess they expect to see more technical conversations elsewhere
should it be merged_model = trained_model.merge_and_unload()?
cannot run, it is killed
I have this problem as well😢
Were you able to solve this?
merged_model = trained_model.merge_and_unload()
Why do you use that kind of prompt for the training like `### Instruction`? When in fact Llama 2 prompts are like `[INST] `...
I think it's a LLaMA2-CHAT prompt. The base model was not finetuned.
This looks like a great notebook, however, I always get a "CUDA out of memory" error when it executes the SFTTrainer function. It's fine up until then according to nvidia-smi but then memory just instantly maxes out. Does anyone know a way around this?
try reducing the sequence length
I reduced per_device_train_batch_size=1,
omg @ 15:06 😂😂😂
ALL of these tutorials require more dependencies. Can't somebody post how to do this in pycharm with your own GPU? I can't make any of the tutorials I've found work and it's just an endless troubleshooting process as to why everything is different in all of them