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GPT4All Python SDK


To get started, pip-install the gpt4all package into your python environment.

pip install gpt4all

We recommend installing gpt4all into its own virtual environment using venv or conda

Load LLM

Models are loaded by name via the GPT4All class. If it's your first time loading a model, it will be downloaded to your device and saved so it can be quickly reloaded next time you create a GPT4All model with the same name.

Load LLM

from gpt4all import GPT4All
model = GPT4All("Meta-Llama-3-8B-Instruct.Q4_0.gguf") # downloads / loads a 4.66GB LLM
with model.chat_session():
    print(model.generate("How can I run LLMs efficiently on my laptop?", max_tokens=1024))
GPT4All model name Filesize RAM Required Parameters Quantization Developer License MD5 Sum (Unique Hash)
Meta-Llama-3-8B-Instruct.Q4_0.gguf 4.66 GB 8 GB 8 Billion q4_0 Meta Llama 3 License c87ad09e1e4c8f9c35a5fcef52b6f1c9
Nous-Hermes-2-Mistral-7B-DPO.Q4_0.gguf 4.11 GB 8 GB 7 Billion q4_0 Mistral & Nous Research Apache 2.0 Coa5f6b4eabd3992da4d7fb7f020f921eb
Phi-3-mini-4k-instruct.Q4_0.gguf 2.18 GB 4 GB 3.8 billion q4_0 Microsoft MIT f8347badde9bfc2efbe89124d78ddaf5
orca-mini-3b-gguf2-q4_0.gguf 1.98 GB 4 GB 3 billion q4_0 Microsoft CC-BY-NC-SA-4.0 0e769317b90ac30d6e09486d61fefa26
gpt4all-13b-snoozy-q4_0.gguf 7.37 GB 16 GB 13 billion q4_0 Nomic AI GPL 40388eb2f8d16bb5d08c96fdfaac6b2c

Chat Session Generation

Most of the language models you will be able to access from HuggingFace have been trained as assistants. This guides language models to not just answer with relevant text, but helpful text.

If you want your LLM's responses to be helpful in the typical sense, we recommend you apply the chat templates the models were finetuned with. Information about specific prompt templates is typically available on the official HuggingFace page for the model.

Example LLM Chat Session Generation

Load Llama 3 and enter the following prompt in a chat session:

from gpt4all import GPT4All
model = GPT4All("Meta-Llama-3-8B-Instruct.Q4_0.gguf")
with model.chat_session():
    print(model.generate("quadratic formula"))

With the default sampling settings, you should see something resembling the following:

The quadratic formula!

The quadratic formula is a mathematical formula that provides the solutions to a quadratic equation of the form:

ax^2 + bx + c = 0

where a, b, and c are constants. The formula is:

x = (-b ± √(b^2 - 4ac)) / 2a

Let's break it down:

* x is the variable we're trying to solve for.
* a, b, and c are the coefficients of the quadratic equation.
* ± means "plus or minus".
* √ denotes the square root.

To use the formula, simply plug in the values of a, b, and c into the expression above. The resulting value(s) will be the solutions to the original quadratic equation!

For example, let's say we have the quadratic equation:

x^2 + 5x + 6 = 0

We can plug these values into the formula as follows:

a = 1
b = 5

Direct Generation

Directly calling model.generate() prompts the model without applying any templates.

Note: this can result in responses that are less like helpful responses and more like mirroring the tone of your prompt. In general, a language model outside of a chat session is less of a helpful assistant and more of a lens into the distribution of the model's training data.

As an example, see how the model's response changes when we give the same prompt as above without applying a chat session:

Example LLM Direct Generation

Load Llama 3 and enter the following prompt:

from gpt4all import GPT4All
model = GPT4All("Meta-Llama-3-8B-Instruct.Q4_0.gguf")
print(model.generate("quadratic formula"))

With the default sampling settings, you should see something resembling the following:

. The equation is in the form of a + bx = c, where a and b are constants.
The solution to this problem involves using the quadratic formula which states that for any quadratic equation ax^2+bx+c=0, its solutions can be found by:
x = (-b ± √(b^2-4ac)) / 2a
In your case, since you have a + bx = c, we need to rewrite it in the form of ax^2+bx+c=0. To do this, subtract both sides from c, so that:
c - (a + bx) = 0
Now, combine like terms on the left side and simplify:
ax^2 + (-b)x + (c-a) = 0\n\nSo now we have a quadratic equation in standard form: ax^2+bx+c=0. We can use this to find its solutions using the quadratic formula:

x = ((-b ± √((-b)^2

Why did it respond differently? Because language models, before being fine-tuned as assistants, are trained to be more like a data mimic than a helpful assistant. Therefore our responses ends up more like a typical continuation of math-style text rather than a helpful answer in dialog.


Nomic trains and open-sources free embedding models that will run very fast on your hardware.

The easiest way to run the text embedding model locally uses the nomic python library to interface with our fast C/C++ implementations.

Example Embeddings Generation

Importing embed from the nomic library, you can call embed.text() with inference_mode="local". This downloads an embedding model and saves it for later.

from nomic import embed
embeddings = embed.text(["String 1", "String 2"], inference_mode="local")['embeddings']
print("Number of embeddings created:", len(embeddings))
print("Number of dimensions per embedding:", len(embeddings[0]))
Number of embeddings created: 2
Number of dimensions per embedding: 768

Nomic embed text local inference

To learn more about making embeddings locally with nomic, visit our embeddings guide.

The following embedding models can be used within the application and with the Embed4All class from the gpt4all Python library. The default context length as GGUF files is 2048 but can be extended.

Name Using with nomic Embed4All model name Context Length # Embedding Dimensions File Size
Nomic Embed v1 embed.text(strings, model="nomic-embed-text-v1", inference_mode="local") Embed4All("nomic-embed-text-v1.f16.gguf") 2048 768 262 MiB
Nomic Embed v1.5 embed.text(strings, model="nomic-embed-text-v1.5", inference_mode="local") Embed4All("nomic-embed-text-v1.5.f16.gguf") 2048 64-768 262 MiB
SBert n/a Embed4All("all-MiniLM-L6-v2.gguf2.f16.gguf") 512 384 44 MiB