What are Open Source and Open Weight Models?

With the release of DeepSeek models, the Chinese AI lab has embraced an “open” approach to AI model development with exceptional reasoning capabilities. Also, Google entered the fray last month with the launch of Gemma 3, a lightweight, open weight model built on its Gemini 2.0 framework. Similarly, to take a step in this ecosystem and to support the “Open” in OpenAI, the tech giant OpenAI is planning to release a new ‘open-weight’ AI model soon. This will be OpenAI’s first open-weight model since the release of GPT 2 in 2019.

As more people use large language models (LLMs), terms like “open weights” and “open source models” become common. But what exactly is this open weights and open source model? This article explains model weights, the difference between open weights and open source models, and why this matters to developers and researchers.

What are Weights in LLMs?

In machine learning, weights are numbers the model learns during training. These numbers control how the model turns input data into predictions. In LLMs, weights store the knowledge learned from the training data. Models with more weights can often learn more complex language patterns.

During training, the model adjusts its weights using the data it sees. It tries to make its predictions more accurate. After training, developers can save these weights. Others can then use the trained model without needing to train it again, which saves time and resources.

Here are the types of LLM in terms of their parameters:

An open-weights model refers to a type of Large Language Model (LLM) where the model’s parameters aka its “weights”—are publicly available. That means anyone can download, inspect, use, or fine-tune the model without hitting any licensing walls or proprietary restrictions.

Unlike closed or proprietary models, which are tightly guarded by the companies that built them, open-weights models are typically released to encourage research, experimentation, and community-driven innovation. They’re often used in academic settings, by startups, or by independent developers who want to push the boundaries of what these models can do—or just get under the hood and tinker.

A big plus? They help promote transparency and reproducibility in AI research. When weights are open, other researchers can validate results, stress-test capabilities, or use them as a baseline for new work. It’s like science with better version control.

Now, here’s an important nuance: open-weights ≠ open-source. While open-source models give you access to everything—the architecture, training code, datasets (sometimes), and the weights—open-weights models only release the final trained parameters. You don’t get the full recipe, just the final dish.

Also read: 7 LLM Parameters to Enhance Model Performance (With Practical Implementation)

What are Open Weight Models?

“Open weights” means the trained weights of a model are available to the public. Anyone can download these weights. They can use the weights in their own applications if they have the right computer systems. Open weights let developers use powerful pre-trained models for tasks like writing text or understanding sentiment. This avoids the high cost and time of training a model from the beginning.

The main benefit of open weights is access. Developers can quickly use advanced models in their projects. This helps drive new ideas. However, open weights don’t always mean the model’s design or training data are also public. Users might get the model’s abilities but not know exactly how it was built or trained.

Lets see some examples

• Llama 3 (from Meta): Meta released the weights for various Llama 3 model sizes. While the weights are available, they come with a specific license that includes usage restrictions, particularly for very large companies commercializing services based on it. The training process details are partially shared, but it’s not fully open source in the traditional software sense.
• Mistral 7B (from Mistral AI): This model gained popularity for its strong performance relative to its size. The weights were released under the Apache 2.0 license, making them widely usable. While more open than Llama’s license, the focus is primarily on providing the weights and basic usage code.

What are Open Source Models?

“Open source models” is a broader idea. An open source model usually includes the weights, the complete source code, guides, and often the training data. This openness lets developers see how the model works. They can change it or even retrain it with new data.

The open source approach supports teamwork and community development. Developers can help improve the model, find bugs, and share changes. This group effort can lead to stronger, more useful models. Using and changing open source models might need more technical knowledge, which can be a challenge for some people.

Lets see some examples

• BLOOM (BigScience Large Open-science Open-access Multilingual Language Model): This was a large collaborative effort involving many researchers. The model’s weights, code, and details about its extensive multilingual training data were released with an open RAIL license aimed at responsible use.
• GPT-2 (from OpenAI): Although older now, GPT-2 was a landmark release where OpenAI made both the model code and the weights publicly available, fostering much research and development in the community.
• Falcon Models (from TII): These models (like Falcon-40B, Falcon-180B) were released with their weights and code under the Apache 2.0 license, allowing broad commercial and research use.

Key Differences

• Transparency: Open source models show everything, including design and often training data. Open weights models might only share the trained numbers.
• Modification: You can change and retrain open source models for specific needs. You usually cannot change open weights models unless the base model is also open source.
• Community: Open source models involve the community in development. Open weights models usually do not have this level of group work.
• Ease of Use: Open weights can be easier for developers who just want to use a model quickly without studying its design.

Adding Closed Source Models to the Picture

It also helps to compare these with proprietary models. Companies own these models. They do not share the model design, weights, or training data publicly. People usually use these models through specific software or tools provided by the company. These often cost money through licenses or subscriptions. Proprietary models can be easy to use and have company support. But they offer little transparency and users cannot change them.

Comparing Model Types: Open Weights, Open Source, Closed Source

This table shows the main differences:

What it Means for Developers and Researchers?

The choice between open weights, open source, or proprietary models affects work. Open weights can be fine for using a model quickly for one task. Open source models work well for projects that need changes, clear understanding, or community help. Proprietary models fit when ease of use and company support matter most, and changes are not needed.

This choice also involves thinking about responsible AI development. Using data fairly, being clear about training, and ensuring accountability are important. Developers should think about the effects of their choices.

How to Use Open Weight Models?

Here are simple examples using the Hugging Face transformers library:

Setup

First, install the needed libraries:

!pip install transformers torch

1. Using open weights

This example uses Mistral 7b. It’s a model with open weights. We use it for text generation.

Let’s quantize the model so make it run on local machine.

from transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig, pipeline

import torch

bnb_config = BitsAndBytesConfig(

   load_in_4bit=True,

   bnb_4bit_quant_type="nf4",

   bnb_4bit_use_double_quant=True,

)

2. Loading the model using HuggingFace transformers

model_name = "mistralai/Mistral-7B-Instruct-v0.2”

tokenizer = AutoTokenizer.from_pretrained(model_name)

model = AutoModelForCausalLM.from_pretrained(

       model_name,

       load_in_4bit=True,

       quantization_config=bnb_config,

       torch_dtype=torch.bfloat16,

       device_map="auto",

       trust_remote_code=True,

   )

3. Let’s define pipeline for easy inferencing

pipe = pipeline(

   "text-generation",

   model=model,

   tokenizer = tokenizer,

   torch_dtype=torch.bfloat16,

   device_map="auto"

)

4. Performing inferencing

prompt = "As a data scientist, can you explain the concept of regularization in machine learning?"

sequences = pipe(

   prompt,

   do_sample=True,

   max_new_tokens=100,

   temperature=0.7,

   top_k=50,

   top_p=0.95,

   num_return_sequences=1,

)

print(sequences[0]['generated_text'])

Output

In machine learning, regularization is the process of preventing overfitting.
 Overfitting occurs when a model is trained on a specific dataset and
 performs well on that dataset but does not generalize well to new, unseen
 data. Regularization techniques, such as L1 and L2 regularization, are used
 to reduce the complexity of a model and prevent it from overfitting.

How to Use Open Source Models?

This example uses GPT 2. It is an open source model used here for text generation. Because it’s open source, you could look at its code or change it.

from transformers import TFGPT2LMHeadModel, GPT2Tokenizer

#get large GPT2 tokenizer and GPT2 model

tokenizer = GPT2Tokenizer.from_pretrained("gpt2-large")

GPT2 = TFGPT2LMHeadModel.from_pretrained("gpt2-large", pad_token_id=tokenizer.eos_token_id)

Performing Text Generation

input_sequence = "I don't know about you, but there's only one thing I want to do after a long day of work"

# encode context the generation is conditioned on

input_ids = tokenizer.encode(input_sequence, return_tensors='tf')

# generate text until the output length (which includes the context length) reaches 50

greedy_output = GPT2.generate(input_ids, max_length = MAX_LEN)

print(tokenizer.decode(greedy_output[0], skip_special_tokens = True))

Output

I don't know about you, but there's only one thing I want to do after a long
 day of work: go to the gym.

I'm not talking about the gym that's right next to my house. I'm talking
 about the gym that's right next to my office.

Hence, we can see that GPT 2 generated text successfully.

Conclusion

Knowing the difference between open weights and open source models helps you navigate the world of LLMs. Open weights give easy access to use pre-trained models. Open source models offer transparency, the ability to make changes, and community support. Closed Source models provide ease and support but limit access and control.

Understanding these options helps developers and researchers choose the best approach for their goals, resources, and values as AI continues to grow. Also i am keenly waiting for the realease of OpenAI’s new Open Weight Model, let me know what are you expectations from the model in the comment section below.

Harsh Mishra

Harsh Mishra is an AI/ML Engineer who spends more time talking to Large Language Models than actual humans. Passionate about GenAI, NLP, and making machines smarter (so they don’t replace him just yet). When not optimizing models, he’s probably optimizing his coffee intake. 🚀☕