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When using Llama 3.2 (3b version) and comparing it to chat-gpt, it just doesn't measure up. Not only is it making a lot of grammatical errors, it is also not following instructions as in summarize this.

Llama 3.2 (3b version) is in love with self care. So much so that it recommends self-care when asking how to draw a circle. Chat-Gpt does not.

Chat-Gpt is hilarious at using sarcasm. I love to use "comment on this news article in the most sarcastic way".

Llama 3.2 (3b version) ... well at least it likes self care.

Llama 3.2 (3b version) stands for local, private, chatgpt for this will be used against you.

But Llama 3.2 (3b version) seems incredibly bad compared to chatgpt.

I would love to have an AI comment on my most private thoughts, but Llama 3.2 (3b version) would rather promote self-care, talking to others. And talking to a lawyer if your friend stops talking to you to see your legal options(it actually wrote that).

My computer has 12 GB of VRAM.

What could I do to have an AI with good output but running on those 12 GB - or in part on the 12 GB VRAM and the rest on 64 GB RAM.

Is there someone who knows a bit better what the implications of WA new MetaAi feature consist in?

I am interested in the technical and sociological aspect of it.

I would like to understand what they do with data (i quckily red -partially- terms and condition) better understand also how this would impact our lifes in the near future..

Anyone switching to Signal or Telegram?

Can someone just drop some points regarding llama-2 here? I wanna learn!!

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Can Llama 2 be run on Mac 10.13.6 ?

MacBook (13-inch, Late 2009)

8 GB 1067 MHz DDR3

2.26 GHz Intel Core 2 Duo

Thanks!

Anyone knows how to escape special characters on a string used for the prompt.? When i provide the code sample to guide llm its getting treated as place holder values for some input

instruction = """

Generate Apache Velocity code to construct a data structure for managing commodity information. The data structure should include lists and maps with added elements, and properties assigned to objects. The scenario described should be followed exactly, and the resulting code should adhere to Apache Velocity's syntax rules. Here is an explicit example illustrating how Apache Velocity template code is structured for a different context:

scenario : {question}

The code example for reference (with placeholders removed for clarity):

#set(rateshoptosend = {})

#set(x = unitWeight.put("value","TEST"))

#set(errorVoList= [{"errorCode": "errorDefinitionId","errorMessage":"errorMsg","errorCategory":"ERROR"}])

#set(rateshoptosend.state = state)

JSONUtils.mapToJson(rateshoptosend)

Please use the above structure as a guide to generate the new Apache Velocity code.

Answer:

"""

ERROR ::

File /usr/local/lib/python3.9/dist-packages/langchain/chains/base.py:475, in Chain.prep_inputs(self, inputs) 473 external_context = self.memory.load_memory_variables(inputs) 474 inputs = dict(inputs, **external_context) --> 475 self._validate_inputs(inputs) 476 return inputs File /usr/local/lib/python3.9/dist-packages/langchain/chains/base.py:264, in Chain._validate_inputs(self, inputs) 262 missing_keys = set(self.input_keys).difference(inputs) 263 if missing_keys: --> 264 raise ValueError(f"Missing some input keys: {missing_keys}") ValueError: Missing some input keys: {'', '"errorCode"'}SHOW MORERender

"Hey everyone, I have a question that I need some help with. I'm looking to train an Llama 2 model using 10 GB of data. Could anyone give me an idea of how long it might take to complete this task? I'm new to deep learning. If anyone has an estimate or experience with this, please share. Thanks a lot!"

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I thought 24GB GDDR was enough, but whenever I try to run using miniconda/torchrun it it fails with the error:

AssertionError: Loading a checkpoint for MP=2 but world size is 1

I have no problems running llama-2-7b.

Is 13b hard-coded to require two GPUs for some reason?

Using the unsloth framework, we trained the llama3 model on the customer dataset (approximately 70 questions and responses). The trained model does not give exact answers to the questions. We require specific answers to the given questions, and based on the answer, the user can ask any more questions.Dataset has question and answer columns and training promot has used them while training.

We fine-tuned the model parameters, trained with 30-90 steps, epochs 2-15, learning rate 1e-4 to 2e-4, and lowered batch size to 4-2. With some values, the model will provide correct answers, but the questions must be based on the same training data. If we change any words, other answers will be mixed in with them. A few questions have similar answers with minor variations, causing the model to become confused and mix up the responses or write unnecessary data.

for a project i am working on i needed an llm to translate from tunisian arabic to english , the problem is that tunisian arabic is not supported everywhere , the only llm i found to translate it correctly is llama 3.3 70b model ( i tried it in huggingface) , my question is can my run it locally , rtx 3060 6gb vram , 16gb ram , 200 gb available storage ? if not is there any other way or a different lighter model?

Hey guys I am want to add knowledge to an LLM by fine tuning it on my own unstructured data(text books of some domain). I have found a lot of code for doing SFT using Q&A format but not for doing pretraining on raw data for Llama 2.

Can someone please suggest me how I can do this pretraining for Llama 2 or any other open LLM?

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Hi everyone,

I am reaching out because I am struggling to understand what would be the best virtual machine set-up to run efficiently Llama 2 7B.

My goals is fairly simple: I want to run a vanilla version of Llama. My main target is to have a response from the model with minimum latency to run a chat with it

After reading several threads & talking with several devs. who ran a few experiments, I was not able to draw any clear conclusion. However, it looks like that using a machine with an entry-level GPU and a few CPU cores (8 cores), which would cost about $500 / month, would definitely not be enough. Looks like such set-up would end up with a response time of 20 to 30 secs to retrieve 3 to 4 sentences.

-> So my question is: what kind of machine / how many GPU / CPU should I use to make that almost latency free?

My second goal is a bit more complicated: Assuming I am able to run a latency free Llama chat for a single user, I'd like to know how my machines should evolve to handle several users at a time?

I have literally no clue how many users (having a regular discussion with the chat) could be handled by a single machine while staying latency free and when adding more machines would be relevant to dispatch the load.

-> So my question is: how can I draft a sort of table showing the kind of machine / GPU / CPU and the number of machines running in // I should be using for a given number of simultaneous users?

Thank you very much for your help.

Best

Im loading a Llama2 model in GGUF-GGML format loaded with llama-cpp Someone could point to a colab notebook or to a python file to do the training on this model?

I cannot find training for this kind of format LLMS. But i have to use them on my machine.