NEW TASK ALERT AutoTrain now supports fine-tuning of sentence transformer models Now, you can improve and customize your RAG or retrieval models without writing a single line of code Supports multiple types of sentence transformers training and finetuning CSV /
OPEN SOURCE
-
Mistral AI Releases Codestral-22B Model Weights on HuggingFace
By
–
We are also releasing the weights on HuggingFace! https://
huggingface.co/mistralai/Code
stral-22B-v0.1
… More details on our blogpost: -
Codestral-22B: New Code Model Outperforms Larger Models
By
–
Today we are releasing Codestral-22B, our first code model! Codestral is trained on more than 80 programming languages and outperforms the performance of previous code models, including the largest ones. It is available on our API platform, through instruct and
-
Mistral AI Launches Codestral with Non-Production License
By
–
With Codestral, our newest state-of-the-art code model, we are introducing the Mistral AI non-production license (MNPL). It allows developers to use our technology for non-commercial use and research. It ensures that every actor on the value chain builds successful businesses.
-
Pandas AI: Data Cleaning, Feature Engineering and Visualization
By
–
With Pandas AI you can: → Clean the Data → Impute missing values → Generate New features → Analyze and manipulate the data Not just that you can also ask it to plot the charts: Here is plotting Histogram with Pandas AI:
-
PandasAI: Generative AI for Natural Language Data Analysis
By
–
Python library that adds Generative AI capabilities to Pandas Introducing PandasAI, now you can analyze complex data frames and plot visualizations just by using natural language:
-
FineWeb Extended Report and Blog Post Coming Soon
By
–
We're finishing a pretty awesome extended blogpost/report on FineWeb in which you'll find all the info – stay tuned!
-
Keras Compiles to JAX, TensorFlow, PyTorch with JAX Recommended
By
–
Keras code compiles to JAX, TensorFlow, or PyTorch code, so anything you can do with one of these frameworks you can do with Keras. I recommend using JAX as your backend, as it is suitable for very-large scale computation and can be easily parallelized across many devices.
-
Keras ops API: NumPy syntax across JAX PyTorch TensorFlow
By
–
A neat feature of Keras is that its `keras.ops` API follows the NumPy API syntax exactly. And since it runs on JAX, PyTorch, TensorFlow, and NumPy itself, you can use Keras to write GPU-accelerated NumPy code that runs with any framework.
-
Reproduce GPT-2 124M in llm.c for $20 in 90 Minutes
By
–
# Reproduce GPT-2 (124M) in llm.c in 90 minutes for $20 The GPT-2 (124M) is the smallest model in the GPT-2 series released by OpenAI in 2019, and is actually quite accessible today, even for the GPU poor. For example, with llm.c you can now reproduce this model on one 8X