What is your favorite offline LLM for technical utility, and have you noticed anything unexpected about certain models?

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 <summary>" (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.</summary>