IMO, local LLMs lack the capabilities or depth of understanding to be useful for most practical tasks (e.g. writing code, automation, language analysis). This will heavily skew any local LLM "usage statistics" further towards RP/storytelling (a significant proportion of which will always be NSFW in nature).

Stable Diffusion 2 base model is trained using what we would today refer to as a "censored" dataset. Stable Diffusion 1 dataset included NSFW images, the base model doesn't seem particularly biased towards or away from them and can be further trained in either direction as it has the foundational understanding of what those things are.

This sounds like a timing issue to me. The thread bunching up may be due to the hook not grabbing the thread or the take-up lever not taking up the slack at the correct time. If it's missing stitches in zig-zag mode then that would also be due to either hook timing or possibly needle bar alignment.

Simple things to check:

• Make sure that the needle is installed correctly, especially that it is oriented the right way and inserted all the way in

• Make sure that the take-up lever is threaded correctly

Assuming these are both correct, you can try the following:

• If possible, insert a fresh needle (at least, you will need a needle that is undamaged and not bent from the shank up to the eye)

• Remove the plate, leave the machine unthreaded

• On the straight stitch setting, turn the hand wheel slowly and check that the eye of the needle is exactly level with the hook as they pass each other (this should happen close to the bottom of the needle's stroke but may not be exactly at the bottom)

• On the widest zig-zag stitch setting, again turn the hand wheel slowly and check that the eye of the needle passes closely to the hook (it won't be exact because the needle has moved, but it should be just slightly early on one side and just slightly late on the other, not noticeably early or late on one side) and also check that the needle is not colliding with any solid parts of the machine on either side

If the eye and the hook are not aligned as they pass each other, then you have either a timing or a needle height alignment issue. If they pass correctly on the straight stitch but the needle is noticeably early or late on one side of the zig-zag stitch (and fine on the other side) then you have an issue with the horizontal alignment of the zig-zag stitch.

I actually like stuff that is simple and repairable. My sewing machine is electromechanical and has no computerised operation. These machines are also a lot cheaper to buy second-hand, of course.

The only stitch types that you actually need are a straight stitch and either a zigzag stitch or a stretch stitch. The zigzag stitch is what it sounds like, and can be used for seams that need to stretch. The stretch stitch involves making 2 forward stitches and 1 backwards stitch in turn, this also allows the seam to stretch and it looks neater because visually the stitch is in a straight line.

The other stitch types that you typically find on a home sewing machine are really only used for decoration and are not essential.

There are only a few popular LLM models. A few more if you count variations such as "uncensored" etc. Most of the others tend to not perform well or don't have much difference from the more popular ones.

I would think that the difference is likely for two reasons:

• LLMs require more effort in curating the dataset for training. Whereas a Stable Diffusion model can be trained by grabbing a bunch of pictures of a particular subject or style and throwing them in a directory, an LLM requires careful gathering and reformatting of text. If you want an LLM to write dialog for a particular character, for example, you would need to try to find or write a lot of existing dialog for that character, which is generally harder than just searching for images on the internet.

• LLMs are already more versatile. For example, most of the popular LLMs will already write dialog for a particular character (or at least attempt to) just by being given a description of the character and possibly a short snippet of sample dialog. Fine-tuning doesn't give any significant performance improvement in that regard. If you want the LLM to write in a specific style, such as Old English, it is usually sufficient to just instruct it to do so and perhaps prime the conversation with a sentence or two written in that style.

WizardLM 13B (I didn't notice any significant improvement with the 30B version), tends to be a bit confined to a standard output format at the expense of accuracy (e.g. it will always try to give both sides of an argument even if there isn't another side or the question isn't an argument at all) but is good for simple questions

LLaMa 2 13B (not the chat tuned version), this one takes some practice with prompting as it doesn't really understand conversation and won't know what it's supposed to do unless you make it clear from contextual clues, but it feels refreshing to use as the model is (as far as is practical) unbiased/uncensored so you don't get all the annoying lectures and stuff

TBH my experience with SillyTavern was that it merely added another layer of complexity/confusion to the prompt formatting/template experience, as it runs on top of text-generation-webui anyway. It was easy for me to end up with configurations where e.g. the SillyTavern turn template would be wrapped inside the text-generation-webui one, and it is very difficult to verify what the prompt actually looks like by the time it reaches the model as this is not displayed in any UI or logs anywhere.

For most purposes I have given up on any UI/frontend and I just work with llama-cpp-python directly. I don't even trust text-generation-webui's "notebook" mode to use my configured sampling settings or to not insert extra end-of-text tokens or whatever.

text-generation-webui "chat" and "chat-instruct" modes are... weird and badly documented when it comes to using a specific prompt template. If you don't want to use the notepad mode, use "instruct" mode and set your turn template with the required tags and include your system prompt in the context (? I forget what it is labeled as) box.

EDIT: Actually I think text-generation-webui might use <|user|> as a special string to mean "substitute the user prefix set in the box directly above the turn template box". Why they have to have a turn template field with "macro" functionality and then separate fields for user and bot prefixes when you could just... put the prefix directly in the turn template I have no idea. It's not as though you would ever want or need to change one without the other anyway. But it's possible that as a result of this you can't actually use <|user|> itself in the turn template...

I’m slowly working my way into the codebase for Textgen, and will hopefully get to the point where I can directly use the command line for prompting.

The llama.cpp Python API is super simple to use and you don't need to dig into the text-generation-webui codebase at all. Literally just:

import llama_cpp_cuda as llama_cpp    # use llama_cpp_cuda version for support for running GGML models on the GPU

model = llama_cpp.Llama(model_path="", seed=-1, n_ctx=2048, n_gpu_layers=28, low_vram=True)    # use whatever settings here that you would set in text-generation-webui when loading the model, make sure to include n_gqa=8 when using LLaMa v2 70B model

# now you can either do things with the "all-in-one" API...
text = model.create_completion(prompt, max_tokens=200, temperature=0.8, top_p=0.95, top_k=40, repeat_penalty=1.1, frequency_penalty=0.0, presence_penalty=0.0, tfs_z=1.0, mirostat_mode=0, mirostat_tau=5.0, mirostat_eta=0.1)    # you pass your temperature, top_p, top_k, etc. settings here, these are the same as the settings in text-generation-webui, note that you don't need to pass all the parameters e.g. you can leave out mirostat parameters if you aren't using mirostat mode

# ...or the "manual" way
prompt_tokens = model.tokenize(prompt.encode('utf-8'))
model.reset()
model.eval(prompt_tokens)
generated_tokens = []
while True:
    next_token = model.sample(temp=0.8, top_p=0.95, top_k=40, repeat_penalty=1.1, frequency_penalty=0.0, presence_penalty=0.0, tfs_z=1.0, mirostat_mode=0, mirostat_tau=5.0, mirostat_eta=0.1)
    if next_token != model.token_eos():
        generated_tokens.append(next_token)
        model.eval([next_token])
    else:
        break
text = model.detokenize([generated_tokens]).decode('utf-8')

See the documentation here for more information: https://llama-cpp-python.readthedocs.io/en/latest/api-reference/ You only really need to pay attention to __init__(), tokenize(), detokenize(), reset(), eval(), sample(), and generate(). create_completion() provides an "all-in-one" wrapper around eval/sample/generate that is intended to be (loosely) compatible as a drop-in replacement for the OpenAI Python library. create_chat_completion() is likewise intended to be a replacement for OpenAI but if you want direct control over the prompt format then ignore it entirely (it's not even documented exactly how the prompt is formatted when using this function...).

Do you happen to know if the prompt processing differences in Textgen, and others like Kobold, are all arbitrary processing done before llama.cpp is called (or some similar code), or is there some other API level that more complex character prompts are tapping into?

They are not doing anything special with the model (no fancy API or anything). All they are doing is including some extra text before your input that describes the characters, scene etc. and possibly a direct instruction to roleplay as that character, and then sending that combined assembled prompt to the model/backend API as you would with any other text. Unfortunately the documentation isn't particularly transparent about how the extra text is included (with regards to the exact formatting used, what order things appear in, etc.) and neither do the logs produced by e.g. text-generation-webui include the actual raw prompt as seen by the model.

I’m aware I’m blindly walking into this space with my arms out trying to find the walls; aware, but unworried about giant potential holes in the floor.

The key point to understand here is that all current LLMs (this may change in the future) work only with raw text. They take in some text and then generate other text that goes after it. Any more complex applications such as conversation are just layers built on top of this. The conversation is turned into a plain-text transcript that is sent to the model. The model generates the next part of the conversation transcript, which is then parsed back out and appended to the list of conversation messages. From the model's perspective, it's all just one continuous stream of raw text. You can always achieve exactly the same results by manually constructing the same prompt yourself and passing it directly to the model.

For example, if I pass the following string as the prompt into model.create_completion() from above

"### User:\nPlease can you write a program in Python that will split a file into 19200-byte blocks and calculate the SHA256 hash of each block.\n\n### Response:\n"

I will get exactly the same result as if I used instruct mode in text-generation-webui with ### User: as the user string, ### Response: as the bot string, and <|user|>\n<|user-message|>\n\n<|bot|>\n<|bot-message|>\n\n as the turn template, and then sent the message "Please can you write a program in Python that will split a file into 19200-byte blocks and calculate the SHA256 hash of each block." in the chat box.

(Although imo doing it the manual way is less error-prone and guaranteed to give me exactly the prompt that I think I should be getting, noting that text-generation-webui doesn't give me any way at all to actually verify that the prompt seen by the model is actually the way I intended it to be and it's not as though I haven't encountered UI bugs before where the produced formatting doesn't match what I entered...)

Like if the reply changes writing perspective context arbitrarily, I need to recall the last question, alter it, and regenerate.

You don't necessarily need to alter your question in that case, often just regenerating is enough to "fix" this. This is, as I have said, particularly an issue with the LLaMa 2 non-chat models as they aren't specifically trained to follow a conversation, so sometimes they will arbitrarily decide to provide a commentary or reaction to the conversation or they see the conversation as part of a webpage and try to generate a heading for the next part of an article or some other such seemingly-"random" behavior instead of continuing the conversation itself. If that happens just regenerate the response until the RNG works out in your favor and the model starts writing in the correct role. Once it starts writing a particular "type" of output it will generally keep writing in the same role until it has finished.

Sometimes it is also helpful to write the first part of the response yourself. For example, you could write "Sure! Here is a program that does " (try to copy the particular style used by a particular model) and then let the model continue from there (there's an option in text-generation-webui labeled "Start reply with" that does this, or if you're constructing the prompt yourself then this is trivial to accomplish - make sure to not include a space or newline after the part that you've written). This will make it more likely to write a program for you instead of providing a commentary like "The user has asked the assistant to write a program. It is possible that someone may respond to such a request by ...".

If the reply is the same, I know the context tokens are ruined.

This seems to be (sort of) a known issue with LLaMa 2 specifically, where it will keep regenerating the previous response even though you continue the conversation. It's not exactly clear what causes this, it's not a software bug in the traditional sense. The model is receiving your follow-up message but it's just deciding to repeat whatever it said last time instead of saying something different. This is believed to possibly be an issue with how the training data was formatted.

This might make more sense if you think of this in terms of what the model is seeing. The model is seeing something such as the following:

### User:
Please can you write a program in Python that will split a file into 19200-byte blocks and calculate the SHA256 hash of each block. The hash should be written to a file with the name ".blockhashes." (index is padded to 5 digits).

### Response:
Certainly! Here's an example program that does what you described:

[33-line code snippet removed]

This program takes two arguments: the input file and the output directory. It first calculates the number of blocks needed to store the entire file, and then loops over each block, reading it from the input file and calculating its SHA256 hash. The hash is written to a separate file with the format `.blockhashes.`.

I hope this helps! Let me know if you have any questions or need further clarification.

### User:
Please can you fix the following two issues with your program:

* The output filename must have the block index padded to 5 digits.

* The output file must contain only the SHA256 hash in hex form and no other text/contents.

Please write out only the parts of the program that you have changed.

### Response:

At this point, the model sees the heading ### Response:. For some reason, the LLaMa 2 models have an over-tendancy to refer back in the text and see that last time the text ### Response: was followed by the text Certainly! Here's an example program that does what you described: and so they will then repeat that exact same text again because the model has concluded that ### Response: should now always be followed by Certainly! Here's an example program that does what you described: instead of seeing the higher-level view where ### User: and ### Response: are taking turns in a conversation.

If this happens, you don't always need to clear/reset the conversation. Often, you can just regenerate it a few times and once the model starts writing a different response it will continue into something else other than repeating the same text as before. As with the previous point it can also help if you write the first part of the response yourself to force it to say something different.

I haven't got any experience with the 70B version specifically but based on my experience with LLaMa 2 13B (still annoyed that there's no 30B version of v2...) it is more sensitive to promoting variations than other models as it isn't specifically trained for "chat", "instruct", or "completion" style interactions. It is capable of all three but without using a clear prompt and template it can be somewhat random as to what kind of response you will get.

For example, using

### User:
Please write an article about [subject].

### Response:

as the prompt will get results varying from a written article to "The user's response to an article about [subject] is" to "My response to this request is to ask the user about [clarifying questions]" to "One possible counterargument to an article about [subject] is" to literally the text "Generating response, please wait... [random URL]". Whereas most conversationally-fine-tuned models will understand and follow this template or other similar templates and play their side of the conversation even if it doesn't match exactly what they were trained on.

I would recommend using llama.cpp (or the Python binding) directly for more awareness of and control over the exact prompt text as seen by the model. Or using text-generation-webui in "notebook" mode (which just gives you a blank text box that both you and the LLM will type into and it's up to you to provide the prompt format). This will also avoid any formatting issues with the chat view in text-generation-webui (again I don't have any specific experience with LLaMa 2 70B but I have encountered times when models don't output the markdown code block tags and text-generation-webui will mess up the formatting).

Note that for some reason the difference between chat, instruct, and chat-instruct modes in text-generation-webui are confusingly named. instruct mode does not include an "instruction" (e.g. "Continue the conversation") before the conversation unless you include one in the conversation template (the conversation template is referred to as "Instruction template" in the UI). chat-instruct mode includes an instruction such as "Continue the conversation by writing a single response for Assistant" before the conversation, followed by the conversation template. chat and chat-instruct modes also include text that describes the character that the model will speak as (mostly used for roleplay but the default "None" character describes a generic AI assistant character - it is possible that the inclusion of this text is what is helping LLaMa 2 stay on track in your case and understand that it is participating in a conversation. I'm not sure what conversation template chat mode uses but afaik it is not the same turn template as set in instruct and chat-instruct modes and I don't see an option to configure it anywhere.

How would you ask for a follow-up change using this instruction template?

Personally I interpreted the request as "if it's been less than 2 minutes, sleep/block until it's 2 minutes since last time" rather than dropping/discarding the string immediately and continuing. Suppose this is what I had actually wanted, can you ask the model to modify its code accordingly without having to go back and edit the original prompt to start over?

I find that a lot of programming questions require multiple rounds of refinements. I tend to favor models that are able to modify existing code in a back-and-forth discussion, and that are capable of writing out just the modified parts of their code with each change to save on time and token count (seriously, so many models will insist on repeating the entire thing no matter how firmly you tell them not to - if you're lucky, they'll actually include the changes in their second reply instead of thereafter getting stuck in a loop of writing out identical code every time).

At least (as far as I can tell) they appear to be ranking the models by human evaluation rather than "benchmarks", which is closer to measuring the real-world performance.

It would be interesting to consider the types of questions that users are posing. For example there is a difference between asking:

• A surface-level fact-based question such as "what is ..."

• A creative question like "write a story/article about ..." or "give me a list of possible talking points for a presentation on ..."

• A question about reasoning/understanding like "why do you think the word ... is more popular than ... when referring to ..." or "explain why ... is considered socially acceptable while ... is not"

• Anything coding-related

Also, some models seem to do well at things that can be answered after one or two replies, but struggle to follow an argument if you try to go more in-depth or continue a conversation about a topic.