Download the CPU quantized gpt4all model checkpoint: [gpt4all-lora-quantized.bin](https://the-eye.eu/public/AI/models/nomic-ai/gpt4all/gpt4all-lora-quantized.bin).
Here's how to get started with the CPU quantized GPT4All model checkpoint:
1. Download the `gpt4all-lora-quantized.bin` file from [Direct Link](https://the-eye.eu/public/AI/models/nomic-ai/gpt4all/gpt4all-lora-quantized.bin) or [[Torrent-Magnet]](https://tinyurl.com/gpt4all-lora-quantized).
2. Clone this repository, navigate to `chat`, and place the downloaded file there.
Note: the full model on GPU (16GB of RAM required) performs much better in our qualitative evaluations.
# Python Client
## CPU Interface
To run GPT4All in python, see the new [official Python bindings](https://github.com/nomic-ai/pyllamacpp).
The old bindings are still available but now deprecated. They will not work in a notebook environment.
To get running using the python client with the CPU interface, first install the [nomic client](https://github.com/nomic-ai/nomic) using `pip install nomic`
Then, you can use the following script to interact with GPT4All:
```
from nomic.gpt4all import GPT4All
m = GPT4All()
m.open()
m.prompt('write me a story about a lonely computer')
```
## GPU Interface
There are two ways to get up and running with this model on GPU.
The setup here is slightly more involved than the CPU model.
1. clone the nomic client [repo](https://github.com/nomic-ai/nomic) and run `pip install .[GPT4All]` in the home dir.
2. run `pip install nomic` and install the additional deps from the wheels built [here](https://github.com/nomic-ai/nomic/tree/main/bin)
Once this is done, you can run the model on GPU with a script like the following:
```
from nomic.gpt4all import GPT4AllGPU
m = GPT4AllGPU(LLAMA_PATH)
config = {'num_beams': 2,
'min_new_tokens': 10,
'max_length': 100,
'repetition_penalty': 2.0}
out = m.generate('write me a story about a lonely computer', config)
print(out)
```
Where LLAMA_PATH is the path to a Huggingface Automodel compliant LLAMA model.
Nomic is unable to distribute this file at this time.
We are working on a GPT4All that does not have this limitation right now.
You can pass any of the [huggingface generation config params](https://huggingface.co/docs/transformers/main_classes/text_generation#transformers.GenerationConfig) in the config.
# GPT4All Compatibility Ecosystem
Edge models in the GPT4All Ecosystem. Please PR as the [community grows](https://huggingface.co/models?sort=modified&search=4bit).
Feel free to convert this to a more structured table.
- <spanstyle="color:green">(Done)</span> [Create a good conversational chat interface for the model.](https://github.com/nomic-ai/gpt4all-ui)
- <spanstyle="color:green">(Done)</span> [Allow users to opt in and submit their chats for subsequent training runs](https://github.com/nomic-ai/gpt4all-ui)
## Medium Term
- <spanstyle="color:red">(NOT STARTED)</span> Integrate GPT4All with [Atlas](https://atlas.nomic.ai) to allow for document retrieval.
- BLOCKED by GPT4All based on GPTJ
- <spanstyle="color:red">(NOT STARTED)</span> Integrate GPT4All with Langchain.
- <spanstyle="color:green">(IN PROGRESS)</span> Build easy custom training scripts to allow users to fine tune models.
## Long Term
- <spanstyle="color:red">(NOT STARTED)</span> Allow anyone to curate training data for subsequent GPT4All releases using Atlas.
### Come up with an interesting idea for a new movie plot. Your plot should be described with a title and a summary.
>1. The title of your movie plot should be "The Last Stand". 2. The summary of your movie plot should be: A group of mercenaries are hired to protect a small town from a dangerous criminal who is on the run. However, the town's sheriff is not willing to let them take over and decides to take matters into his own hands.
> 1. The title of your movie plot should be "The Last Stand".
> 2. The summary of your movie plot should be: A group of mercenaries are hired to protect a small town from a dangerous criminal who is on the run. However, the town's sheriff is not willing to let them take over and decides to take matters into his own hands.
### Reverse a string in python.
> In Python, you can reverse a list or tuple by using the reversed() function on it. Here's an example of how to use this method with strings:
@ -160,7 +160,7 @@ We realized that we had two bugs however:
- We accidentally duplicated data and effectively trained for 2 epochs instead of 1
- We added an eos token to every sequence, even those that we truncated (e.g. long code that exceeds the 1024).
## Conditonal EOS and 1 Epoch
## Conditional EOS and 1 Epoch
Using the same parameters, we then trained a model using a "conditional" eos token where we only add an `eos` when the inputs are less than the maximum sequence length for one epoch.
Zach Nussbaum
1 year ago