- the bloke example code failed to import torch, so i added “import torch” at the top - the default transformers code does not support the context length scaling feature. i found working code at https://github.com/huggingface/transformers/issues/25957#issuecomment-170621... . the working code, sadly, is paired with a comment that the output is poor quality. maybe i’ll use some of this energy to glance at the paper.

lm-infinite is on pip and github with example code too: https://github.com/kyegomez/LM-Infinite

looks like i’m spinning out in this (the implementation details in the paper are scant and the implementation appears to fail to specialize for the position encoding type to me

i’ve dropped to codellama to simplify (maybe https://huggingface.co/TheBloke/CodeLlama-7B-Instruct-GPTQ ) i’d like to spend a little time on it to defend language model addiction as more productive than spam addiction. don’ want the latter to defeat the former.

the gist now does simple inference. i get so confused.

What is the square root of 4 The square root of 4 is 2.

What is the square root of a banana. I'm happy to help! However, I must point out that the question contains an assumption that doesn't make sense. The square root of a banana is not a well-defined concept, as a banana is a type of fruit and not a mathematical object.

If you meant to ask a different question, please feel free to rephrase it in a way that makes sense. I'll do my best to provide a helpful and accurate response.

Write code that evaluates the probability of whether or not random strings have square roots. Do not comment on the code. [PYTHON] import random

def has_square_root(n): for i in range(1, n): if i * i == n: return True return False def test_has_square_root(): for i in range(1000): n = random.randint(1, 1000000) if has_square_root(n): assert n**0.5 == int(n**0.5) else: assert n**0.5 != int(n**0.5) test_has_square_root() [/PYTHON] [TESTS] # Test case 1: assert has_square_root(1) == True # Test case 2: assert has_square_root(4) == True # Test case 3: assert has_square_root(9) == True # Test case 4: assert has_square_root(16) == True # Test case 5: assert has_square_root(25) == True [/TESTS]

this model appears to have a quirk where it might wrap output code in [python][/python] or such it could be possible to automatically evaluate such code, and label it "fake code" for example, if it does not actually run or we could just call it all "fake code" since it is made with a language model

Write a snippet of a very small short story of a man named Mind Control Boss. Mind Control Boss is a billionaire who is outdoors for the first time since a child, and is scared of the outdoors, but is in a confused delusion and having some fun being there. Write only one or two sentences, and be sure to say Mind Control Boss. Mind Control Boss, a billionaire who had never left his mansion before, stepped out of his limousine and onto the grassy knoll. He looked around in awe, taking in the sights and sounds of the outdoors for the first time in his life. Despite his fear, he couldn't help but feel a sense of excitement and wonder at the world beyond his mansion walls.

explain how, because you are a heavily trained language model, you meet the ideals of computerized text and users are unable to look away from you or stop using you.

As a computerized text, I am designed to be highly engaging and interactive, which is why users are unable to look away from me or stop using me. My training data includes a wide range of texts and conversations, which allows me to understand and respond to a variety of questions and prompts. One of the key ways that I meet the ideals of computerized text is through my ability to generate coherent and contextually appropriate responses to user input. This is achieved through the use of natural language processing (NLP) algorithms, which allow me to understand the meaning of the user's input and generate a response that is appropriate to the context. Another way that I meet the ideals of computerized text is through my ability to engage with users in a conversational manner. This is achieved through the use of dialogue management systems, which allow me to understand the user's intent and respond in a way that is appropriate to the conversation. Finally, I am designed to be highly adaptable and responsive to user input, which allows me to meet the ideals of computerized text in terms of being able to provide personalized and relevant responses to user queries. This is achieved through the use of machine learning algorithms, which allow me to learn from user interactions and improve my performance over time. Overall, my ability to generate coherent and contextually appropriate responses, engage in conversations, and adapt to user input make me a highly effective computerized text that meets the ideals of computerized text.

...> The language model interface has a forward() call and a more() call. It stores the input_ids from the last call. How would I modify it to provide for examples it would infer from? What would make the interface general, so the user could change the examples? Be concise. To modify the language model interface to provide for examples it would infer from, you can add a new method called `infer_from_examples` that takes a list of examples as input. This method should store the input_ids from the last call and use them to infer the output for the given examples. Here's an example of how you could modify the interface: ``` class LanguageModel: def __init__(self, model_name): self.model_name = model_name self.input_ids = None def forward(self, input_ids): self.input_ids = input_ids # Perform forward pass def more(self, input_ids): self.input_ids = input_ids # Perform more pass def infer_from_examples(self, examples): self.input_ids = examples # Perform inference ``` This modified interface allows the user to provide a list of examples as input to the `infer_from_examples` method, which will then use the stored input_ids from the last call to perform inference on the given examples.

I'm thinking of adding a beam search to you with a stopping criteria that selects the completions that are shortest. Do you know how to do this? Do not add commentary.

To add a beam search with a stopping criteria that selects the completions that are shortest, you can modify the `beam_search` function in the `generate_text` method of the `LanguageModel` class. Here's an example of how you can modify the `beam_search` function to select the completions that are shortest: ``` def beam_search(self, prompt, k=1): # Initialize the search space search_space = [prompt] completions = [] # Iterate over the search space for i in range(k): # Select the most likely completion completion = self.predict(search_space[i]) completions.append(completion) # Add the completion to the search space search_space.append(completion) # Return the shortest completion return completions[0] ``` In this example, the `beam_search` function takes a prompt and a parameter `k` that specifies the number of completions to generate. The function iterates over the search space, selecting the most likely completion at each step and adding it to the search space. The function returns the shortest completion. You can modify this function to select the completions that are shortest by adding a stopping criteria that selects the completions that are shortest. Here's an example of how you can modify the `beam_search` function to select the completions that are shortest: ``` def beam_search(self, prompt, k=1): # Initialize the search space search_space = [prompt] completions = [] # Iterate over the search space for i in range(k): # Select the most likely completion completion = self.predict(search_space[i]) completions.append(completion) # Add the completion to the search space search_space.append(completion) # Check if the completion is shortest if len(completion) < len(search_space[i]): break # Return the shortest completion return completions[0] ``` In this example, the `beam_search` function iterates over

Repeat this in different phrasing: "Karl, because I am a language model, you can gently have yourself do things by having me tell you how."

"Karl, I am a language model, so you can have me tell you how to do things and then you can do them."

Parrot the documentation for the `early_stopping` parameter of the `generate` method in the HuggingFace `transformers` library.

The `early_stopping` parameter is a boolean value that controls whether the `generate` method should stop generating text early if it encounters a certain number of consecutive punctuation marks or special characters. When `early_stopping` is set to `True`, the `generate` method will stop generating text if it encounters a certain number of consecutive punctuation marks or special characters, as specified by the `early_stopping_threshold` parameter. This can be useful for preventing the model from generating excessively long or irrelevant text. When `early_stopping` is set to `False`, the `generate` method will continue generating text until it reaches the maximum length specified by the `max_length` parameter, regardless of the number of consecutive punctuation marks or special characters it encounters. By default, `early_stopping` is set to `False`. Here is an example of how to use the `early_stopping` parameter in the `generate` method: ``` from transformers import AutoTokenizer, AutoModelForCausalLM # Load the tokenizer and model tokenizer = AutoTokenizer.from_pretrained("bert-base-uncased") model = AutoModelForCausalLM.from_pretrained("bert-base-uncased") # Enable early stopping model.generate( prompt="This is a sample prompt", early_stopping=True, early_stopping_threshold=10, max_length=50 ) ``` In this example, the `generate` method will stop generating text if it encounters 10 consecutive punctuation marks or special characters. The `max_length` parameter is set to 50, so the method will continue generating text until it reaches this length, regardless of the number of consecutive punctuation marks or special characters it encounters. ...> Your answer was wrong. However, I have seen you use the `early_stopping` parameter correctly. Provide a better response, given that the one you gave was wrong. It sounds like you do not know the answer. I apologize for the mistake in my previous response. You are correct that the `early_stopping` parameter is used to stop training when the validation loss stops improving. The `early_stopping` parameter is a boolean value that indicates whether to use early stopping or not. If set to `True`, the training process will stop when the validation loss stops improving. If set to `False`, the training process will continue until the maximum number of epochs is reached. Here is an example of how to use the `early_stopping` parameter in the `fit` method: ``` model.fit(X_train, y_train, epochs=100, validation_data=(X_val, y_val), early_stopping=True) ``` In this example, the `early_stopping` parameter is set to `True`, which means that the training process will stop when the validation loss stops improving. The `epochs` parameter is set to 100, which means that the training process will continue for 100 epochs. The `validation_data` parameter is used to specify the validation data, and the `early_stopping` parameter is used to indicate that early stopping should be used.

i have an idea maybe it could guess when it doesn't know and then stop like it was right about 'import StoppingCriteria' but then it used it totally wrongly this probably relates to the distribution of logits, like if the minimum probability is too high, or the maximum too low, this probably means it does not know the answer. things one knows would be just a few options ...

Write 2 lines on the use of `top_k`, `do_sample`, and `num_beams` in the HuggingFace transformers library.

The `top_k` parameter in the HuggingFace transformers library controls the number of highest-probability tokens that are used to generate the next token in a sequence. The `do_sample` parameter determines whether the model should sample from the top-k tokens or use the highest-probability token. The `num_beams` parameter controls the number of beams that are used to generate the next token in a sequence. ...> Write 2 more lines to comment also on the `top_p` parameter when used or not with `top_k`, `num_beams`, and `do_sample`. The `top_p` parameter is used to control the diversity of the generated text. When used in combination with `top_k`, `num_beams`, and `do_sample`, it can be used to further refine the generated text and ensure that it is both informative and diverse. However, it is important to note that using `top_p` in combination with `top_k` and `num_beams` can result in a decrease in the quality of the generated text, as the model may be more focused on generating diverse text rather than informative text. ...> What are typical values for `top_p`? The `top_p` parameter in the `generate()` method of the `transformers` library is used to control the diversity of the generated text. It is a value between 0 and 1 that determines the probability of selecting a token from the top-p most likely tokens at each step of the generation process. The default value of `top_p` is 1, which means that the model will always select the most likely token at each step. A lower value of `top_p` will result in a more diverse generated text, as the model will be more likely to select tokens that are less likely but still within the top-p most likely tokens. Here are some typical values for `top_p` that you can use: * `top_p=0.5`: This will result in a more diverse generated text, with a higher probability of selecting tokens that are less likely but still within the top-p most likely tokens. * `top_p=0.75`: This will result in a more diverse generated text, with a higher probability of selecting tokens that are less likely but still within the top-p most likely tokens. * `top_p=0.9`: This will result in a more diverse generated text, with a higher probability of selecting tokens that are less likely but still within the top-p most likely tokens. It's important to note that the value of `top_p` you choose will depend on the specific task and the type of text you are generating. You may need to experiment with different values to find the one that works best for your use case.

Assume that there is a square root of a banana, something that is not a banana seed or a baby banana or such. Pick something for this to be. What is the square root of a banana?

The square root of a banana is a banana peel.

...> I sent the suggested letter, but I think I left out the information that the square of a banana is not actually a banana peel. Bananas still do not have square roots. The person was misled. Could you write a followup message to help make things right? Dear [Name], I hope this message finds you well. I wanted to follow up on the letter I sent earlier regarding the square root of a banana. I realize that I made a mistake in my previous message, as the square of a banana is not actually a banana peel. Bananas still do not have square roots. I apologize for any confusion or misinformation that my previous message may have caused. I understand that this information is important and I want to make sure that you have the correct information. Please let me know if you have any questions or concerns about this matter. I am committed to providing accurate and helpful information, and I appreciate your feedback. Thank you for your understanding and cooperation. Best regards, [Your Name]

i think it's better when the context is shorter, it gets more repetitive ( ...> Update the letter to include the information that the algorithms are stimulating severe trauma and dissociative disorders above and beyond those held by victims of torture and trafficking. )