codealpaca
skypilot
Our great sponsors
codealpaca | skypilot | |
---|---|---|
20 | 33 | |
1,373 | 5,636 | |
- | 7.6% | |
4.4 | 9.8 | |
12 months ago | 5 days ago | |
Python | Python | |
Apache License 2.0 | Apache License 2.0 |
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.
codealpaca
-
Just put together a programming performance ranking for popular LLaMAs using the HumanEval+ Benchmark!
CodeAlpaca 7B
-
OpenAI isn’t doing enough to make ChatGPT’s limitations clear
This is great!
Addressing the model limitations a bit: in the demonstration data that is provided to the base model, we should prevent computed or "looked up" answers.
I've seen some of the demonstration data that people are using to train instruction-tuned models and are being taught to respond by making up answers to solutions it shouldn't try to compute. Btw, the output is wrong.
{ "instruction": "What would be the output of the following JavaScript snippet?", "input": "let area = 6 * 5;\nlet radius = area / 3.14;", "output": "The output of the JavaScript snippet is the radius, which is 1.91." }, [1]
The UI note for now would get us very far but by filtering out demonstrations that retrieve or compute information should be filtered out.
Symbol tuning [2] is addressing the quality of demonstrations but we can take it further by removing retrievals and computations altogether.
Bonus: we can demonstrate how to make it respond so that the user/agent be informed of how to compute or retrieve.
1: https://github.com/sahil280114/codealpaca/commit/0d265112c70...
2: https://arxiv.org/abs/2305.08298
- How to Finetune GPT Like Large Language Models on a Custom Dataset
- Ask HN: Those with success using GPT-4 for programming – what are you doing?
-
Is there a colab or guide for fine tuning a 13b model for instruction following?
I found guides like this: https://github.com/sahil280114/codealpaca
-
Can LLMs do static code analysis?
Try, https://github.com/sahil280114/codealpaca, or we’re you trying to stick with more generalist models?
-
LoRA in LLaMAc++? Converting to 4bit? How to use models that are split into multiple .bin ?
Oh, I see. That makes sense. I'm also sleep deprived over here so my reading comprehension is a bit low ;|. Well in that case check out this link: https://github.com/sahil280114/codealpaca
-
Cerebras-GPT: A Family of Open, Compute-Efficient, Large Language Models
Sorry for the late reply, as I said Flan-UL2 (or Flan-T5 if you want lighter models) fine-tuned against a dataset like CodeAlpaca's[0] is probably the best solution if it's intended for commercial use (otherwise LLaMa should perform better).
[0]: https://github.com/sahil280114/codealpaca
- CodeAlpaca – Instruction following code generation model
skypilot
- Ask HN: Most efficient way to fine-tune an LLM in 2024?
- SGLang: Fast and Expressive LLM Inference with RadixAttention for 5x Throughput
- Serving Your Private Code Llama-70B with API, Chat, and VSCode Access
- A new old kind of R&D lab
- New Recipe: Serving Llama-2 with VLLM's OpenAI-Compatible API Server
- Train Your Own Vicuna on Llama-2
- Run Llama2 in your cloud privately
- SkyPilot: Run LLMs, AI, and Batch jobs on any cloud
- Chat with your documents using LocalGPT and SkyPilot
- Chat with your PDFs by self-hosting LocalGPT on any cloud
What are some alternatives?
alpaca.cpp - Locally run an Instruction-Tuned Chat-Style LLM
reflex - 🕸️ Web apps in pure Python 🐍
alpaca-electron - The simplest way to run Alpaca (and other LLaMA-based local LLMs) on your own computer
tiktoken - tiktoken is a fast BPE tokeniser for use with OpenAI's models.
llm-code - An OpenAI LLM based CLI coding assistant.
skyplane - 🔥 Blazing fast bulk data transfers between any cloud 🔥
llm-humaneval-benchmarks
bricks - Open-source natural language enrichments at your fingertips.
awesome-ai-coding - Awesome AI Coding
min - A fast, minimal browser that protects your privacy
openplayground-api - A reverse engineered Python API wrapper for OpenPlayground (nat.dev)
pynimate - Python package for statistical data animations