Search Results for "python programming language" - Page 6
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139 programs for "python programming language" with 2 filters applied:
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chatglm-6b
Bilingual 6.2B parameter chatbot optimized for Chinese and English
ChatGLM-6B is a 6.2 billion parameter bilingual language model developed by THUDM, based on the General Language Model (GLM) framework. It is optimized for natural and fluent dialogue in both Chinese and English, supporting applications in conversational AI, question answering, and assistance. Trained on approximately 1 trillion tokens, the model benefits from supervised fine-tuning, feedback self-training, and reinforcement learning with human feedback to align its outputs with human... -
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ERNIE-4.5-0.3B-Base-PT
Compact 360M text model with high efficiency and fine-tuning support
ERNIE-4.5-0.3B-Base-PT is a compact, fully dense transformer model with 360 million parameters, optimized for general-purpose text generation tasks. It belongs to the ERNIE 4.5 series by Baidu and leverages advanced pretraining techniques without relying on a Mixture-of-Experts (MoE) structure. The model features 18 transformer layers, 16 attention heads, and a maximum context length of 131,072 tokens, offering strong language understanding for its size. It can be fine-tuned using ERNIEKit... -
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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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whisper-large-v3
High-accuracy multilingual speech recognition and translation model
... input and better support for Cantonese, achieving up to 20% error reduction over Whisper-large-v2. It handles zero-shot transcription and translation, performs language detection automatically, and supports features like word-level timestamps and long-form audio processing. The model integrates well with Hugging Face Transformers and supports optimizations such as batching, SDPA, and Flash Attention 2. -
Picsart Enterprise Background Removal API for Stunning eCommerce VisualsWith our Remove Background API tool, you can access the transformative capabilities of automation , which will allow you to turn any photo asset into compelling product imagery. With elevated visuals quality on your digital platforms, you can captivate your audience, and therefore achieve higher engagement and sales.
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Llama-3.1-8B-Instruct
Multilingual 8B-parameter chat-optimized LLM fine-tuned by Meta
Llama-3.1-8B-Instruct is a multilingual, instruction-tuned language model developed by Meta, designed for high-quality dialogue generation across eight languages, including English, Spanish, French, German, Italian, Portuguese, Hindi, and Thai. It uses a transformer-based, autoregressive architecture with Grouped-Query Attention and supports a 128k token context window. The model was fine-tuned using a combination of supervised fine-tuning (SFT), reinforcement learning with human feedback (RLHF... -
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Meta-Llama-3-8B-Instruct
Instruction-tuned 8B LLM by Meta for helpful, safe English dialogue
Meta-Llama-3-8B-Instruct is an instruction-tuned large language model from Meta’s Llama 3 family, optimized for safe and helpful English dialogue. It uses an autoregressive transformer architecture with Grouped-Query Attention (GQA) and supports an 8k token context length. Fine-tuned using supervised learning and reinforcement learning with human feedback (RLHF), the model achieves strong results on benchmarks like MMLU, GSM8K, and HumanEval. Trained on over 15 trillion tokens of publicly... -
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Mistral-7B-Instruct-v0.2
Instruction-tuned 7B model for chat and task-oriented text generation
Mistral-7B-Instruct-v0.2 is a fine-tuned version of the Mistral-7B-v0.2 language model, designed specifically for following instructions in a conversational format. It supports a 32k token context window, enabling more detailed and longer interactions compared to its predecessor. The model is trained to respond to user prompts formatted with [INST] and [/INST] tags, and it performs well in instruction-following tasks like Q&A, summarization, and explanations. It can be used via the official... -
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ⓍTTS-v2
Multilingual voice cloning TTS model with 6-second sample support
ⓍTTS-v2 (XTTS-v2) by Coqui is a powerful multilingual text-to-speech model capable of cloning voices from a short 6-second audio sample. It supports 17 languages and enables high-quality voice generation with emotion, style transfer, and cross-language synthesis. The model introduces major improvements over ⓍTTS-v1, including better prosody, stability, and support for Hungarian and Korean. ⓍTTS-v2 allows interpolation between multiple voice references and generates speech at a 24kHz sampling... -
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whisper-large-v3-turbo
Whisper-large-v3-turbo delivers fast, multilingual speech recognition
Whisper-large-v3-turbo is a high-performance automatic speech recognition (ASR) and translation model developed by OpenAI, based on a pruned version of Whisper large-v3. It reduces decoding layers from 32 to 4, offering significantly faster inference with only minor degradation in accuracy. Trained on over 5 million hours of multilingual data, it handles speech transcription, translation, and language identification across 99 languages. It supports advanced decoding strategies like beam search... -
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Llama-3.3-70B-Instruct
Llama-3.3-70B-Instruct is a multilingual AI optimized for helpful chat
Llama-3.3-70B-Instruct is Meta's large, instruction-tuned language model designed for safe, multilingual, assistant-style conversations and text generation. With 70 billion parameters, it supports English, Spanish, French, German, Italian, Portuguese, Hindi, and Thai, offering state-of-the-art performance across a wide range of benchmarks including MMLU, HumanEval, and GPQA. The model is built on a transformer architecture with grouped-query attention, trained on over 15 trillion tokens... -
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Llama-2-70b-chat-hf
Llama-2-70B-Chat is Meta’s largest fine-tuned open-source chat LLM
Llama-2-70B-Chat is Meta’s largest fine-tuned large language model, optimized for dialogue and aligned using supervised fine-tuning (SFT) and reinforcement learning with human feedback (RLHF). It features 70 billion parameters and uses a transformer architecture with grouped-query attention (GQA) to improve inference scalability. Trained on 2 trillion tokens from publicly available sources and over a million human-annotated examples, the model outperforms most open-source chat models and rivals... -
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Llama-2-7b-hf
Llama-2-7B is a 7B-parameter transformer model for text generation
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 tasks. It is a 7 billion parameter auto-regressive transformer trained on 2 trillion tokens from publicly available sources, using an optimized architecture without Grouped-Query Attention (GQA). This model is the pretrained version, intended for research and commercial use in English, and can be adapted for downstream applications such as summarization... -
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chronos-t5-small
Time series forecasting model using T5 architecture with 46M params
chronos-t5-small is part of Amazon’s Chronos family of time series forecasting models built on transformer-based language model architectures. It repurposes the T5 encoder-decoder design for time series data by transforming time series into discrete tokens via scaling and quantization. With 46 million parameters and a reduced vocabulary of 4096 tokens, this small variant balances performance with efficiency. Trained on both real-world and synthetic time series datasets, it supports... -
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ERNIE-4.5-VL-28B-A3B-Paddle
Multimodal ERNIE 4.5 MoE model for image-text reasoning and chat
..., including Reinforcement Learning with Verifiable Rewards (RLVR), to enhance alignment and performance. Fine-tuned for real-world applications, it integrates language and vision through supervised learning, DPO, and UPO techniques. It supports long contexts up to 131,072 tokens and can be deployed using FastDeploy or the Hugging Face Transformers library. This version is ideal for developers needing high-performance, scalable multimodal capabilities in chat or image-based reasoning systems.
