Search Results for "python data analysis"
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Showing 29 open source projects for "python data analysis"
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DB-GPT
Revolutionizing Database Interactions with Private LLM Technology
DB-GPT is an experimental open-source project that uses localized GPT large models to interact with your data and environment. With this solution, you can be assured that there is no risk of data leakage, and your data is 100% private and secure. -
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MedicalGPT
MedicalGPT: Training Your Own Medical GPT Model with ChatGPT Training
MedicalGPT training medical GPT model with ChatGPT training pipeline, implementation of Pretraining, Supervised Finetuning, Reward Modeling and Reinforcement Learning. MedicalGPT trains large medical models, including secondary pre-training, supervised fine-tuning, reward modeling, and reinforcement learning training. -
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Clay Foundation Model
The Clay Foundation Model - An open source AI model and interface
The Clay Foundation Model is an open-source AI model and interface designed to provide comprehensive data and insights about Earth. It aims to serve as a foundational tool for environmental monitoring, research, and decision-making by integrating various data sources and offering an accessible platform for analysis. -
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MiniCPM-o
A GPT-4o Level MLLM for Vision, Speech and Multimodal Live Streaming
MiniCPM-o 2.6 is a cutting-edge multimodal large language model (MLLM) designed for high-performance tasks across vision, speech, and video. Capable of running on end-side devices such as smartphones and tablets, it provides powerful features like real-time speech conversation, video understanding, and multimodal live streaming. With 8 billion parameters, MiniCPM-o 2.6 surpasses its predecessors in versatility and efficiency, making it one of the most robust models available. It supports... -
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Chinese-LLaMA-Alpaca 2
Chinese LLaMA-2 & Alpaca-2 Large Model Phase II Project
This project is developed based on the commercially available large model Llama-2 released by Meta. It is the second phase of the Chinese LLaMA&Alpaca large model project. The Chinese LLaMA-2 base model and the Alpaca-2 instruction fine-tuning large model are open-sourced. These models expand and optimize the Chinese vocabulary on the basis of the original Llama-2, use large-scale Chinese data for incremental pre-training, and further improve the basic semantics and command understanding... -
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Chinese-LLaMA-Alpaca-2 v2.0
Chinese LLaMA & Alpaca large language model + local CPU/GPU training
This project has open-sourced the Chinese LLaMA model and the Alpaca large model with instruction fine-tuning to further promote the open research of large models in the Chinese NLP community. Based on the original LLaMA , these models expand the Chinese vocabulary and use Chinese data for secondary pre-training, which further improves the basic semantic understanding of Chinese. At the same time, the Chinese Alpaca model further uses Chinese instruction data for fine-tuning, which... -
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VALL-E
PyTorch implementation of VALL-E (Zero-Shot Text-To-Speech)
We introduce a language modeling approach for text to speech synthesis (TTS). Specifically, we train a neural codec language model (called VALL-E) using discrete codes derived from an off-the-shelf neural audio codec model, and regard TTS as a conditional language modeling task rather than continuous signal regression as in previous work. During the pre-training stage, we scale up the TTS training data to 60K hours of English speech which is hundreds of times larger than existing systems. VALL... -
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FinGPT
Open-Source Financial Large Language Models!
FinGPT is an open-source large language model tailored specifically for financial tasks. Developed by AI4Finance Foundation, it is designed to assist with various financial applications, such as forecasting, financial sentiment analysis, and portfolio management. FinGPT has been trained on a diverse range of financial datasets, making it a powerful tool for finance professionals looking to leverage AI for data-driven decision-making. The model is freely available on platforms like Hugging Face... -
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GLM-4-32B-0414
Open Multilingual Multimodal Chat LMs
GLM-4-32B-0414 is a powerful open-source large language model featuring 32 billion parameters, designed to deliver performance comparable to leading models like OpenAI’s GPT series. It supports multilingual and multimodal chat capabilities with an extensive 32K token context length, making it ideal for dialogue, reasoning, and complex task completion. The model is pre-trained on 15 trillion tokens of high-quality data, including substantial synthetic reasoning datasets, and further enhanced... -
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Janus-Pro
Janus-Series: Unified Multimodal Understanding and Generation Models
.... Its latest iteration, Janus-Pro, improves on this with a more optimized training strategy, expanded data, and larger model scaling, leading to significant advancements in both multimodal understanding and text-to-image generation. -
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Universal Sentence Encoder
Encoder of greater-than-word length text trained on a variety of data
The Universal Sentence Encoder (USE) is a pre-trained deep learning model designed to encode sentences into fixed-length embeddings for use in various natural language processing (NLP) tasks. It leverages Transformer and Deep Averaging Network (DAN) architectures to generate embeddings that capture the semantic meaning of sentences. The model is designed for tasks like sentiment analysis, semantic textual similarity, and clustering, and provides high-quality sentence representations... -
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GPT Neo
An implementation of model parallel GPT-2 and GPT-3-style models
An implementation of model & data parallel GPT3-like models using the mesh-tensorflow library. If you're just here to play with our pre-trained models, we strongly recommend you try out the HuggingFace Transformer integration. Training and inference is officially supported on TPU and should work on GPU as well. This repository will be (mostly) archived as we move focus to our GPU-specific repo, GPT-NeoX. NB, while neo can technically run a training step at 200B+ parameters, it is very... -
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segmentation-3.0
Speaker segmentation model for 10s audio chunks with powerset labels
... (VAD), overlapped speech detection, and speaker diarization when combined with additional models. While it doesn't process full recordings directly, it powers pipelines for detailed segmentation and analysis of speech data. Its MIT license ensures it's openly accessible, though users must agree to usage conditions for access. The model showcases state-of-the-art segmentation performance and is used in both academic and production-oriented pipelines. -
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xlm-roberta-base
Multilingual RoBERTa trained on 100 languages for NLP tasks
xlm-roberta-base is a multilingual transformer model trained by Facebook AI on 2.5TB of filtered CommonCrawl data spanning 100 languages. It is based on the RoBERTa architecture and pre-trained using a masked language modeling (MLM) objective. Unlike models like GPT, which predict the next word, this model learns bidirectional context by predicting masked tokens, enabling robust sentence-level representations. xlm-roberta-base is particularly suited for cross-lingual understanding... -
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twitter-roberta-base-sentiment-latest
RoBERTa model for English sentiment analysis on Twitter data
twitter-roberta-base-sentiment-latest is a RoBERTa-based transformer model fine-tuned on over 124 million tweets collected between 2018 and 2021. Designed for sentiment analysis in English, it categorizes tweets as Negative, Neutral, or Positive. The model is optimized using the TweetEval benchmark and integrated with the TweetNLP ecosystem for seamless deployment. Its training emphasizes real-world, social media content, making it highly effective for analyzing informal or noisy text... -
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mms-300m-1130-forced-aligner
CTC-based forced aligner for audio-text in 158 languages
... to the TorchAudio forced alignment API. Users can integrate it easily through the Python package ctc-forced-aligner, and it supports GPU acceleration via PyTorch. The alignment pipeline includes audio processing, emission generation, tokenization, and span detection, making it suitable for speech analysis, transcription syncing, and dataset creation. This model is especially useful for researchers and developers working with low-resource languages or building multilingual speech systems. -
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Meta-Llama-3-8B-Instruct
Instruction-tuned 8B LLM by Meta for helpful, safe English dialogue
... available data and more than 10 million human-annotated examples, it excludes any Meta user data. The model is released under the Meta Llama 3 Community License, which allows commercial use for organizations with fewer than 700 million MAUs, and imposes clear use, attribution, and redistribution rules. Meta provides safety tools like Llama Guard 2 and Code Shield to help developers implement system-level safety in applications. -
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grok-1
Grok-1 is a 314B-parameter open-weight language model by xAI
Grok-1 is a large-scale language model released by xAI, featuring 314 billion parameters and made available under the Apache 2.0 license. It is designed for text generation and was trained for advanced language understanding and reasoning capabilities. Grok-1 is currently distributed as open weights, with inference support requiring multi-GPU hardware due to its size. The model can be downloaded from Hugging Face and run using the accompanying Python code in the official GitHub repository... -
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bert-base-chinese
BERT-based Chinese language model for fill-mask and NLP tasks
... applications, including text classification, named entity recognition, and sentiment analysis in Chinese. It uses the same structure as the BERT base uncased English model, but it is trained entirely on Chinese data. While robust, like other large language models, it may reflect or amplify existing biases present in its training data. Due to limited transparency around the dataset and evaluation metrics, users should test it thoroughly before deployment in sensitive contexts. -
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Llama-2-7b
7B-parameter foundational LLM by Meta for text generation tasks
Llama-2-7B is a foundational large language model developed by Meta as part of the Llama 2 family, designed for general-purpose text generation in English. It has 7 billion parameters and uses an optimized transformer-based, autoregressive architecture. Trained on 2 trillion tokens of publicly available data, it serves as the base for fine-tuned models like Llama-2-Chat. The model is pretrained only, meaning it is not optimized for dialogue but can be adapted for various natural language... -
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GPT-2
GPT-2 is a 124M parameter English language model for text generation
GPT-2 is a pretrained transformer-based language model developed by OpenAI for generating natural language text. Trained on 40GB of internet data from outbound Reddit links (excluding Wikipedia), it uses causal language modeling to predict the next token in a sequence. The model was trained without human labels and learns representations of English that support text generation, feature extraction, and fine-tuning. GPT-2 uses a byte-level BPE tokenizer with a vocabulary of 50,257 and handles... -
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starcoder
Code generation model trained on 80+ languages with FIM support
... natural language. While it is not an instruction-tuned model, it can act as a capable technical assistant when prompted appropriately. Developers can use it for general-purpose code generation, with fine control over prefix/middle/suffix tokens. The model has some limitations: generated code may contain bugs or licensing constraints, and attribution must be observed when output resembles training data. StarCoder is licensed under the BigCode OpenRAIL-M license. -
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Nanonets-OCR-s
State-of-the-art image-to-markdown OCR model
Nanonets-OCR-s is an advanced image-to-markdown OCR model that transforms documents into structured and semantically rich markdown. It goes beyond basic text extraction by intelligently recognizing content types and applying meaningful tags, making the output ideal for Large Language Models (LLMs) and automated workflows. The model expertly converts mathematical equations into LaTeX syntax, distinguishing between inline and display modes for accuracy. It also generates descriptive <img> tags... -
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Llama-3.1-8B-Instruct
Multilingual 8B-parameter chat-optimized LLM fine-tuned by Meta
...), and high-quality human and synthetic safety data. It excels at conversational AI, tool use, coding, and multilingual reasoning, achieving strong performance across a wide range of academic and applied benchmarks. The model is released under the Llama 3.1 Community License, which permits commercial use for organizations with fewer than 700 million monthly active users, provided they comply with Meta’s Acceptable Use Policy. -
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phi-2
Small, high-performing language model for QA, chat, and code tasks
Phi-2 is a 2.7 billion parameter Transformer model developed by Microsoft, designed for natural language processing and code generation tasks. It was trained on a filtered dataset of high-quality web content and synthetic NLP texts created by GPT-3.5, totaling 1.4 trillion tokens. Phi-2 excels in benchmarks for common sense, language understanding, and logical reasoning, outperforming most models under 13B parameters despite not being instruction-tuned or aligned via RLHF. It performs best...
