The Best 6603 Large Language Model Tools in 2025

Phi-2 is a small yet powerful language model developed by Microsoft, featuring 2.7 billion parameters, focusing on efficient inference and high-quality text generation.

A large English language model pre-trained with masked language modeling objectives, using improved BERT training methods

DistilBERT is a distilled version of the BERT base model, maintaining similar performance while being more lightweight and efficient, suitable for natural language processing tasks such as sequence classification and token classification.

Meta Llama 3.1 8B Instruct is a multilingual large language model optimized for multilingual dialogue use cases, excelling in common industry benchmarks.

XLM-RoBERTa is a multilingual model pretrained on 2.5TB of filtered CommonCrawl data across 100 languages, using masked language modeling as the training objective.

An English pre-trained model based on Transformer architecture, trained on massive text through masked language modeling objectives, supporting text feature extraction and downstream task fine-tuning

OPT is an open pre-trained Transformer language model suite released by Meta AI, with parameter sizes ranging from 125 million to 175 billion, designed to match the performance of the GPT-3 series while promoting open research in large-scale language models.

A pretrained model based on the transformers library, suitable for various NLP tasks

Llama 3.1 is Meta's multilingual large language model series, featuring 8B, 70B, and 405B parameter scales, supporting 8 languages and code generation, with optimized multilingual dialogue scenarios.

The T5 Base Version is a text-to-text Transformer model developed by Google with 220 million parameters, supporting multilingual NLP tasks.

XLM-RoBERTa is a multilingual model pretrained on 2.5TB of filtered CommonCrawl data across 100 languages, trained with a masked language modeling objective.

Zero-shot classification model based on BART-large architecture, fine-tuned on MultiNLI dataset

T5-Small is a 60-million-parameter text transformation model developed by Google, using a unified text-to-text framework to handle various NLP tasks

FLAN-T5 is a language model optimized through instruction fine-tuning based on the T5 model, supporting multilingual task processing and outperforming the original T5 model with the same parameter count.

ALBERT is a lightweight pre-trained language model based on Transformer architecture, reducing memory usage through parameter sharing mechanism, suitable for English text processing tasks.

DistilBERT is a distilled version of the BERT base multilingual model, retaining 97% of BERT's performance with fewer parameters and faster speed. It supports 104 languages and is suitable for various natural language processing tasks.

DistilGPT2 is a lightweight distilled version of GPT-2 with 82 million parameters, retaining GPT-2's core text generation capabilities while being smaller and faster.

The BLEURT model implemented based on PyTorch, used for text evaluation tasks in natural language processing.

Llama 3.2 is a multilingual large language model series developed by Meta, including 1B and 3B scale pre-trained and instruction-tuned generative models, optimized for multilingual dialogue scenarios, supporting intelligent retrieval and summarization tasks.

A 0.5B parameter instruction fine-tuned model designed for the Gensyn reinforcement learning group, supporting local fine-tuning training

A 1.5B parameter instruction fine-tuned model designed for Gensyn RL Swarm, supporting local fine-tuning via peer-to-peer reinforcement learning

Llama 3.2 is a multilingual large language model series launched by Meta, including 1B and 3B parameter pre-trained and instruction-tuned generative models, optimized for multilingual dialogue scenarios, supporting agent retrieval and summarization tasks.

BART is a Transformer model combining a bidirectional encoder and an autoregressive decoder, suitable for text generation and understanding tasks.

Bio+Clinical BERT is a clinical BERT model initialized from BioBERT, trained on all notes from MIMIC III, suitable for biomedical and clinical text processing.

DeepSeek-R1 is a 1.58-bit dynamically quantized large language model optimized by Unsloth, adopting the MoE architecture and supporting English task processing.

BiomedBERT is a biomedical domain-specific language model pretrained on PubMed abstracts and PubMedCentral full-text articles, achieving state-of-the-art performance in multiple biomedical NLP tasks.

DeepSeek-R1 is the first-generation inference model launched by DeepSeek. Through large-scale reinforcement learning training, it performs excellently in mathematics, code, and reasoning tasks.

This model is a masked language model trained on Python code based on microsoft/codebert-base-mlm, primarily used for code evaluation and generation tasks.

Cutting-edge French language model based on RoBERTa, offering 6 different versions

FireFunction V2 is a state-of-the-art function calling model developed by Fireworks AI with a commercially viable license. It is trained on Llama 3 and supports parallel function calls with strong instruction-following capabilities.

DeBERTaV3 is an improved pre-trained language model based on DeBERTa, which enhances efficiency through gradient-disentangled embedding sharing in ELECTRA-style pretraining and excels in natural language understanding tasks.

Llama 3.2 is a multilingual large language model series developed by Meta, including 1B and 3B scale pre-trained and instruction-tuned generative models, optimized for multilingual conversation scenarios.

FinBERT is a pre-trained natural language processing model specifically designed for financial text sentiment analysis

OpenELM is a set of open-source efficient language models that use a hierarchical scaling strategy to efficiently allocate parameters in each layer of the Transformer model, thereby improving model accuracy.

The GGUF quantized version of Qwen2-7B-Instruct, suitable for local deployment and inference

ByT5 is a tokenizer-free version of Google's T5 that directly processes raw UTF-8 bytes, supporting multilingual text processing with excellent performance on noisy data.

DeBERTa-V2-XXLarge is an improved BERT model based on the disentangled attention mechanism and enhanced masked decoder, excelling in multiple natural language understanding tasks.

TinyLlama is a lightweight 1.1B-parameter Llama model pre-trained on 3 trillion tokens, fine-tuned for conversation and alignment optimized, suitable for resource-constrained scenarios.

BARTpho is a pre-trained sequence-to-sequence model for Vietnamese, based on the BART architecture and specifically optimized for the Vietnamese language.

A large language model fine-tuned based on Llama2 70B, trained with Orca-style datasets, excels at executing complex instructions

A RoBERTa model pretrained on Korean, suitable for various Korean natural language processing tasks.

DistilRoBERTa is a distilled version of the RoBERTa-base model with fewer parameters but faster speed, suitable for English text processing tasks.