LLM-As-Chatbot
mesh-transformer-jax
| LLM-As-Chatbot | mesh-transformer-jax | |
|---|---|---|
| 3 | 52 | |
| 3,242 | 6,213 | |
| - | - | |
| 9.0 | 0.0 | |
| 6 months ago | over 1 year 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.
LLM-As-Chatbot
-
OpenAI's GPT-4 Red Teamer Nathan Labenz: the GPT-4 base model recommends assassinating humans, naming specific targets
The first one is from https://github.com/deep-diver/Alpaca-LoRA-Serve
-
Show HN: ChatLLaMA – A ChatGPT style chatbot for Facebook's LLaMA
this is useless because it doesn't handle context:
Q: Name five genres of music.
A: Jazz, country, hip-hop, blues, classical.
Q: Name a famous artist from the third genre.
A: Salvador Dalí.
Whereas this one actually supports context: https://github.com/deep-diver/Alpaca-LoRA-Serve
- Show HN: Finetune LLaMA-7B on commodity GPUs using your own text
mesh-transformer-jax
-
Large Language Models: Compairing Gen2/Gen3 Models (GPT-3, GPT-J, MT5 and More)
GPT-J is a LLM case study with two goals: Training a LLM with a data source containing unique material, and using the training frameworkMesh Transformer JAX to achieve a high training efficiency through parallelization. There is no research paper about GPT-J, but on its GitHub pages, the model, different checkpoints, and the complete source code for training is given.
-
[R] Parallel Attention and Feed-Forward Net Design for Pre-training and Inference on Transformers
This idea has already been proposed in ViT-22B and GPT-J-6B.
- Show HN: Finetune LLaMA-7B on commodity GPUs using your own text
-
[D] An Instruct Version Of GPT-J Using Stanford Alpaca's Dataset
Sure. Here's the repo I used for the fine-tuning: https://github.com/kingoflolz/mesh-transformer-jax. I used 5 epochs, and appart from that I kept the default parameters in the repo.
- Boss wants me to use ChatGPT for work, but I refuse to input my personal phone number. Any advice?
-
Let's build GPT: from scratch, in code, spelled out by Andrej Karpathy
You can skip to step 4 using something like GPT-J as far as I understand: https://github.com/kingoflolz/mesh-transformer-jax#links
The pretrained model is already available.
-
Best coding model?
The Github repo suggests it's possible you can change the number of checkpoints to make it run on a GPU.
- Ask HN: What language models can I fine-tune at home?
-
selfhosted/ open-source ChatGPT alternative?
GPT-J, which uses mesh-transformer-jax: https://github.com/kingoflolz/mesh-transformer-jax
-
GPT-J, an open-source alternative to GPT-3
They hinted at it in the screenshot, but the goods are linked from the https://6b.eleuther.ai page: https://github.com/kingoflolz/mesh-transformer-jax#gpt-j-6b (Apache 2)
What are some alternatives?
alpaca-lora - Instruct-tune LLaMA on consumer hardware
DeepSpeed - DeepSpeed is a deep learning optimization library that makes distributed training and inference easy, efficient, and effective.
simple-llm-finetuner - Simple UI for LLM Model Finetuning
tensorflow - An Open Source Machine Learning Framework for Everyone
peft - 🤗 PEFT: State-of-the-art Parameter-Efficient Fine-Tuning.
gpt-neo - An implementation of model parallel GPT-2 and GPT-3-style models using the mesh-tensorflow library.
hh-rlhf - Human preference data for "Training a Helpful and Harmless Assistant with Reinforcement Learning from Human Feedback"
jax - Composable transformations of Python+NumPy programs: differentiate, vectorize, JIT to GPU/TPU, and more
alpaca-7b-truss
KoboldAI-Client
stanford_alpaca - Code and documentation to train Stanford's Alpaca models, and generate the data.