cog-llama-template VS marsha

Compare cog-llama-template vs marsha and see what are their differences.

marsha

Marsha is a functional, higher-level, English-based programming language that gets compiled into tested Python software by an LLM (by alantech)
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cog-llama-template marsha
1 12
303 460
1.7% 0.7%
9.2 8.4
4 months ago 7 months ago
Python Python
Apache License 2.0 MIT License
The number of mentions indicates the total number of mentions that we've tracked plus the number of user suggested alternatives.
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.

cog-llama-template

Posts with mentions or reviews of cog-llama-template. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2023-07-18.
  • Llama 2 – Meta AI
    16 projects | news.ycombinator.com | 18 Jul 2023
    * or even deploy your own LLaMA v2 fine tune with Cog (https://github.com/a16z-infra/cog-llama-template)

    Please let us know what you use this for or if you have feedback! And thanks to all contributors to this model, Meta, Replicate, the Open Source community!

marsha

Posts with mentions or reviews of marsha. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2023-07-31.
  • LLMs as compilers
    2 projects | dev.to | 31 Jul 2023
    There is already a lot of hay to mow with the current state of affairs in generative AI. LLMs as proper compilers, compiLLMers if you will, can produce correct code reliably enough today given enough guidance. Getting an LLM to generate correct code requires providing various examples and descriptive instructions. The UX of a chat interface to an LLM inherently leads people to write prompts that do not meet these criteria. We need to make it easy for people to give LLMs precise descriptions and numerous examples as concisely as possible via syntaxes that are similar to English so they remain easy to learn and use. Coq is a great example of a functional programming syntax that is verbose and distant from English, but example-driven via assertions. David Ellis, Alejandro Guillen and I recently introduced Marsha as a proposal for what a syntax that meets the requirements outlined can look like. It is still early, but LLMs will increasingly give us the power to create more accessible representations of computer programs that look close to English. These representations will be distilled by LLMs into the complexities of the current high-level languages. Knowing Java or Python will become a rare skill, akin to individuals specializing in low-level optimizations using C or assembly language these days. Instead, the focus of developer experience will shift to the higher-level abstractions that are built on top of LLMs and composing these abstractions for different tasks. Compillmers will make programming more accessible in the near future such that writing software becomes part of the resume of most knowledge workers.
  • Show HN: Marsha – An LLM-Based Programming Language
    1 project | /r/hypeurls | 27 Jul 2023
    1 project | /r/hackernews | 27 Jul 2023
    7 projects | news.ycombinator.com | 25 Jul 2023
    > You're a bit too black-and-white on this situation.

    While I agree with your other points, I feel this argument doesn't really hold water.

    The output of the c compiler is deterministic.

    I struggle very hard to believe that the floating point rounding errors when you compile C will cause it to occasionally emit a binary that is not byte-identical multiple sequential runs in a row.

    What any program does at runtime is essentially non-deterministic, and that's 100% not what we're talking about here.

    If you consider https://github.com/alantech/marsha/blob/main/examples/web/we... ...

    The generated output of this file is a probability distribution with a sweet spot where the code does what you want; there are multiple outputs of code that sit in the sweet spot. You want one of these.

    The actual output of this file is a probability distribution that includes the examples, but may or may not overlap the sweet spot of 'actually does the right thing'.

    ...in fact, and there's no specific reason to expect that, regardless of the number of examples you provide, the distribution that includes those examples also includes the sweet spot.

    For common examples it will, but I'd argue that it's actually provable that there are times (eg. where the output length of a valid solution would be > the possible out of the model), that regardless of the examples / tests, it's not actually possible to generate a valid solution from. Just like how constraint solvers will sometimes tell you there's no solution that matches all the constraints.

    So, that would be like a compiler error. "You've asked for something impossible".

    ...but I imagine it would be very very difficult to tell the difference between inputs that overlap the sweet spot and those that don't; the ones that don't will have solutions that look right, but actually only cover the examples; and there's literally no way of telling the difference between that and a correct solution without HFRL.

    It seem like an intractable problem to me.

    > Different tools for different scenarios, so if that is a huge problem, don't use Marsha as it currently is.

    As you say~

  • Marsha, a ChatGPT-based programming language
    1 project | /r/ChatGPTCoding | 27 Jul 2023
  • Marsha is a functional, higher-level, English-based programming language that gets compiled into tested Python software more reliably by ChatGPT
    1 project | /r/programming | 27 Jul 2023
  • Llama 2 – Meta AI
    16 projects | news.ycombinator.com | 18 Jul 2023
    So this comment inspired me to write a Roman Numeral to Integer function in out LLM-based programming language, Marsha: https://github.com/alantech/marsha/blob/main/examples/genera...

What are some alternatives?

When comparing cog-llama-template and marsha you can also consider the following projects:

llama2-chatbot - LLaMA v2 Chatbot

maccarone - AI-managed code blocks in Python ⏪⏩

ollama - Get up and running with Llama 3, Mistral, Gemma, and other large language models.

OpenPipe - Turn expensive prompts into cheap fine-tuned models

llama - Inference code for LLaMA models on CPU and Mac M1/M2 GPU

cog - Containers for machine learning

llama - Inference code for Llama models

programming-languages-genealogical-tree - Programming languages genealogical tree