can-ai-code VS developer

You can run a Q4 quant of a 70B model in about 40GB of RAM (+context). You're single user (batch size 1, bs=1) inference speed will be basically memory bottlenecked, so on a dual channel dedicated box you'd expect somewhere about 1 token/s. That's inference, prefill/prompt generation will take even longer (as your chat history grows) on CPU. So falls into the realm of technically possible, but not for real world use.

If you're looking specifically for CodeLlama 70B, Artificial Analysis https://artificialanalysis.ai/models/codellama-instruct-70b/... lists Perplexity, Together.ai, Deep Infra, and Fireworks as potential hosts, with Together.ai and Deepinfra at about $0.9/1M tokens, with about 30 tokens/s and about 300ms latency (time to first token).

For those looking for local coding models in specifically. I keep a list of LLM coding evals here: https://llm-tracker.info/evals/Code-Evaluation

On the EvalPlus Leaderboard, there about about 10 open models that rank higher than CodeLlama 70B, all smaller models: https://evalplus.github.io/leaderboard.html

A few other evals (worth cross-referencing to counter contamination, overfitting):

* CRUXEval Leaderboard https://crux-eval.github.io/leaderboard.html

* CanAiCode Leaderboard https://huggingface.co/spaces/mike-ravkine/can-ai-code-resul...

* Big Code Models Leaderboard https://huggingface.co/spaces/bigcode/bigcode-models-leaderb...

From the various leaderboards, deepseek-ai/deepseek-coder-33b-instruct still looks like the best performing open model (it has a very liberal ethical license), followed by ise-uiuc/Magicoder-S-DS-6.7B (a deepseek-coder-6.7b-base fine tune). The former can be run as a Q4 quant on a single 24GB GPU (a used 3090 should run you about $700 atm), and the latter, if it works for you will run 4X faster and fit on even cheaper/weaker GPUs.

There's always recent developments, but two worth pointing out:

OpenCodeInterpreter - a new system that uses execution feedback and outperforms ChatGPT4 Code Interpreter that is fine-tuned off of the DeepSeek code models: https://opencodeinterpreter.github.io/

StarCoder2-15B just dropped and also looks competitive. Announcement and relevant links: https://huggingface.co/blog/starcoder2

This is a completely fair, but open question. Not to be a typical HN user, but when you say SOTA local, the question is really what benchmarks do you really care about in order to evaluate. Size, operability, complexity, explainability etc.

Working out what copilot models perform best has been a deep exercise for myself and has really made me evaluate my own coding style on what I find important and things I look out for when investigating models and evaluating interview candidates.

I think three benchmarks & leaderboards most go to are:

https://huggingface.co/spaces/bigcode/bigcode-models-leaderb... - which is the most understood, broad language capability leaderboad that relies on well understood evaluations and benchmarks.

https://huggingface.co/spaces/mike-ravkine/can-ai-code-resul... - Also comprehensive, but primarily assesses Python and JavaScript.

https://evalplus.github.io/leaderboard.html - which I think is a better take on comparing models you intend to run locally as you can evaluate performance, operability and size in one visualisation.

Best of luck and I would love to know which models & benchmarks you choose and why.

Here is a leader board of some models

https://huggingface.co/spaces/mike-ravkine/can-ai-code-resul...

Don't know how biased this leaderboard is, but I guess you could just give some of them a try and see for yourself.

- Can AI Code? - https://huggingface.co/spaces/mike-ravkine/can-ai-code-results

Check out https://github.com/the-crypt-keeper/can-ai-code for some ideas. I'd love to see more shootouts like this. Especially if they were spread out among a few different languages.

Very cool, this looks like a combination of chatbot-ui and llama-cpp-python? A similar project I've been using is https://github.com/serge-chat/serge. Nous-Hermes-Llama2-13b is my daily driver and scores high on coding evaluations (https://huggingface.co/spaces/mike-ravkine/can-ai-code-resul...).

I have several sets of quant comparisons posted on my HF spaces, the caveat is my prompts are all "English to code": https://huggingface.co/spaces/mike-ravkine/can-ai-code-compa...

The dropdown at the top selects which comparison: Falcon compares GGML, Vicuna compares bits and bytes. I have some more comparisons planned, feel free to open an issue if you'd like to see something specific: https://github.com/the-crypt-keeper/can-ai-code

Yeah it seemed suspiciously high for HumanEval and it only ranks 14th for JS and 7th for Python on other benchmarks now: https://huggingface.co/spaces/mike-ravkine/can-ai-code-resul...

WizardCoder is a bit of a problem since it's not llama 1/2 based but is its own 15B model and as such the support for it in anything practical is near nonexistent. WizardLM v1.2 looks like it may be worth checking out.

There are at least 4 different types of quants floating around HF (bitsandbytes, GGML, GPTQ and AWQ) so I dont know if a "GGML" column makes sense vs a more abstract way of linking quants to their base models. I am doing this and its fucking awful: https://github.com/the-crypt-keeper/can-ai-code/blob/main/models/models.yaml

Ah I meant this one but I see now it's WIP.

> much of the work is repetitive, but it comes with its edge cases that we need to look out for

Then don't use AI for it.

Bluntly.

This is a poor use-case; it doesn't matter what model you use, you'll get a disappointing result.

These are the domains where using AI coding currently shines:

1) You're approaching a new well established domain (eg. building an android app in kotlin), and you already know how to build things / apps, but not specifically that exact domain.

Example: How do I do X but for an android app in kotlin?

2) You're building out a generic scaffold for a project and need some tedious (but generic) work done.

Example: https://github.com/smol-ai/developer

3) You have a standard, but specific question regarding your code, and although related Q/A answers exist, nothing seems to specifically target the issue you're having.

Example: My nginx configuration is giving me [SPECIFIC ERROR] for [CONFIG FILE]. What's wrong and how can I fix it?

The domains where it does not work are:

1) You have some generic code with domain/company/whatever specific edge cases.

The edge cases, broadly speaking, no matter how well documented, will not be handled well by the model.

Edge cases are exactly that; edge cases; the common medium of 'how to x' does not cover edge cases; the edge cases will not be covered and the results will require you to review and complete them manually.

2) You have some specific piece of code you want to refactor 'to solve xxx', but the code is not covered well by tests.

LLMs struggle to refactor existing code, and the difficulty is proportional to the code length. There are technical reasons for this (mainly randomizing token weights), but tldr; it's basically a crap shot.

Might work. Might not. If you have no tests who knows? You have to manually verify both the new functionality and the old functionality, but maybe it helps a bit, at scale, for trivial problems.

3) You're doing some obscure BS or using a new library / new version of the library.

The LLM will have no context for this, and will generate rubbish / old deprecated content.

...

So. Concrete advice:

1) sigh~

> a friend of mine came and suggested that I use Retrieval-Augmented Generation (RAG), I have yet to try it, with a setup Langchain + Ollama.

Ignore this advice. RAG and langchain are not the solutions you are looking for.

2) Use a normal coding assistant like copilot.

This is the most effective way to use AI right now.

There are some frameworks that let you use open source models if you don't want to use openAI.

3) Do not attempt to bulk generate code.

AI coding isn't at that level. Right now, the tooling is primitive, and large scale coherent code generation is... not impossible, but it is difficult (see below).

You will be more effective using an existing proven path that uses 'copilot' style helpers.

However...

...if you do want to pursue code generation, here's a broad blueprint to follow:

- decompose your task into steps

- decompose you steps in functions

- generate or write tests and function definitions

- generate an api specification (eg. .d.ts file) for your function definitions

- for each function definition, generate the code for the function passing the api specification in as the context. eg. "Given functions x, y, z with the specs... ; generate an implementation of q that does ...".

- repeated generate multiple outputs for the above until you get one that passes the tests you wrote.

This approach broadly scales to reasonably complex problems, so long as you partition your problem into module sized chunks.

I personally like to put something like "you're building a library/package to do xxx" or "as a one file header" as a top level in the prompt, as it seems to link into the 'this should be isolated and a package' style of output.

check out smol-developer (https://github.com/smol-ai/developer)

4. https://github.com/smol-ai/developer

How can ChatGPT be effectively utilized for reading library source code, resolving coding issues, and serving as a dedicated coding assistant tailored for a specific programming language?

The smol developer AI tool was built by a developer called Swyx using ChatGPT. This library is designed to act like a personal, junior developer, performing a huge array of simple, routine tasks as well as some sophisticated tasks. By using a spec that you provide in a prompt, you can even use smol developer to pair program with an AI tool!

And this is where most of these tools fall short, with tools like Smol-Developer creating decent client and server code that work great on their own, but unfortunately don’t work together!

1) Show HN: Bloop – Answer questions about your code with an LLM agent (github.com/bloopai)

https://news.ycombinator.com/item?id=36260961

2) https://github.com/paul-gauthier/aider

3) Show HN: GPT Repo Loader – load entire code repos into GPT prompts (github.com/mpoon)

https://news.ycombinator.com/item?id=35191303

4) https://github.com/smol-ai/developer

5) codium

6) copilot

7) using gpt in the playground / chatgpt

8) jam.dev/jamgpt

9) magic.dev

10) https://github.com/kristoferlund/duet-gpt

Which ones am I missing?

I had this same idea and started working on something for this purpose called j-dev [0]. It started as a fork off smol-dev [1] which basically gets GPT to write your entire project from scratch. And then you would have to iterate the prompt to nuke everything and re-write everything, filling in increasingly complicated statements like "oh except in this function make sure you return a promise"

j-dev is basically a CLI where it gives a prompt similar to the one in the parent article. You start with a prompt and the CLI fills in the directory contents (excluding gitignore). Then it requests access to the files it thinks it wants. And then it can edit, delete or add files or ask for followup based on your response.

It also addresses the problem that a lot of these tools eat up way too many tokens so a single prompt to something like smol-dev would eat up a few dollars on every iterations.

It's still very much a work in progress and i'll prob do a show hn next week but I would love some feedback

[0] https://github.com/breeko/j-dev

[1] https://github.com/smol-ai/developer

Smol AI’s “Smol-Developer” gained a lot of notoriety very quickly by being one of the first such tools on the scene. It is a simple set of python scripts that allow a user to build prototype apps using natural language in an iterative approach.