Search Results for "chat"
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Showing 36 open source projects for "chat"
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DeepSeek LLM
DeepSeek LLM: Let there be answers
The DeepSeek-LLM repository hosts the code, model files, evaluations, and documentation for DeepSeek’s LLM series (notably the 67B Chat variant). Its tagline is “Let there be answers.” The repo includes an “evaluation” folder (with results like math benchmark scores) and code artifacts (e.g. pre-commit config) that support model development and deployment. According to the evaluation files, DeepSeek LLM 67B Chat achieves strong performance on math benchmarks under both chain-of-thought (CoT) and tool-assisted reasoning modes. ... -
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OpenAI Quickstart Python
Python example app from the OpenAI API quickstart tutorial
openai-quickstart-python is an official OpenAI repository containing multiple Python quickstart applications that demonstrate how to use different OpenAI API endpoints, including Chat and Assistants. It provides practical, beginner-friendly examples to help developers quickly learn how to send requests, handle responses, and build basic applications using the OpenAI Python SDK. The examples folder includes small, self-contained projects showcasing common use cases like chat completions, tool usage, and interactive interfaces. ... -
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GLM-4.7
Advanced language and coding AI model
...Its tool-use capabilities are substantially enhanced, with notable improvements in browsing, search, and tool-integrated reasoning tasks. Overall, GLM-4.7 shows broad performance upgrades across coding, reasoning, chat, creative writing, and role-play scenarios. -
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Qwen-Audio
Chat & pretrained large audio language model proposed by Alibaba Cloud
Qwen-Audio is a large audio-language model developed by Alibaba Cloud, built to accept various types of audio input (speech, natural sounds, music, singing) along with text input, and output text. There is also an instruction-tuned version called Qwen-Audio-Chat which supports conversational interaction (multi-round), audio + text input, creative tasks and reasoning over audio. It uses multi-task training over many different audio tasks (30+), and achieves strong multi-benchmarks performance without task-specific fine‐tuning. It includes features such as flexible multi-run chat, audio understanding/reasoning, music appreciation, and also tool usage (e.g. voice editing). -
Grafana: The open and composable observability platformGrafana is the open source analytics & monitoring solution for every database.
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Qwen
The official repo of Qwen chat & pretrained large language model
Qwen is a series of large language models developed by Alibaba Cloud, consisting of various pretrained versions like Qwen-1.8B, Qwen-7B, Qwen-14B, and Qwen-72B. These models, which range from smaller to larger configurations, are designed for a wide range of natural language processing tasks. They are openly available for research and commercial use, with Qwen's code and model weights shared on GitHub. Qwen's capabilities include text generation, comprehension, and conversation, making it a... -
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Qwen2-Audio
Repo of Qwen2-Audio chat & pretrained large audio language model
Qwen2-Audio is a large audio-language model by Alibaba Cloud, part of the Qwen series. It is trained to accept various audio signal inputs (including speech, sounds, etc.) and perform both voice chat and audio analysis, producing textual responses. It supports two major modes: Voice Chat (interactive voice only input) and Audio Analysis (audio + text instructions), with both base and instruction-tuned models. It is evaluated on many benchmarks (speech recognition, translation, sound classification, emotion, etc.), and offers pretrained models (e.g. 7B) released via ModelScope and Hugging Face. ... -
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GPT Discord Bot
Example Discord bot written in Python that uses the completions API
GPT Discord Bot is an example project from OpenAI that shows how to integrate the OpenAI API with Discord using Python. The bot uses the Chat Completions API (defaulting to gpt-3.5-turbo) to carry out conversational interactions and the Moderations API to filter user messages. It is built on top of the discord.py framework and the OpenAI Python library, providing a simple, extensible template for building AI-powered Discord applications. The bot supports a /chat command that spawns a public thread, carries full conversation context across messages, and gracefully closes the thread when context or message limits are reached. ... -
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gpt-oss
gpt-oss-120b and gpt-oss-20b are two open-weight language models
...The repository provides multiple reference implementations—including PyTorch, Triton, and Metal—for educational and experimental use, as well as example clients and tools like a terminal chat app and a Responses API server. -
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CogVLM
A state-of-the-art open visual language model
...The repo provides multiple ways to run models (CLI, web demo, and OpenAI-Vision–style APIs), along with quantization options that reduce VRAM needs (e.g., 4-bit). It includes checkpoints for chat, base, and grounding variants, plus recipes for model-parallel inference and LoRA fine-tuning. The documentation covers task prompts for general dialogue, visual grounding (box→caption, caption→box, caption+boxes), and GUI agent workflows that produce structured actions with bounding boxes. -
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ChatGPT Retrieval Plugin
The ChatGPT Retrieval Plugin lets you easily find personal documents
The chatgpt-retrieval-plugin repository implements a semantic retrieval backend that lets ChatGPT (or GPT-powered tools) access private or organizational documents in natural language by combining vector search, embedding models, and plugin infrastructure. It can serve as a custom GPT plugin or function-calling backend so that a chat session can “look up” relevant documents based on user queries, inject those results into context, and respond more knowledgeably about a private knowledge base. The repo provides code for ingestion pipelines (embedding documents), APIs for querying, local server components, and privacy / PII detection modules. It also contains plugin manifest files (OpenAPI spec, plugin JSON) so that the retrieval backend can be registered in a plugin ecosystem. ... -
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Anthropic SDK Python
Provides convenient access to the Anthropic REST API from any Python 3
The anthropic-sdk-python repository is the official Python client library for interacting with the Anthropic (Claude) REST API. It is designed to provide a user-friendly, type-safe, and asynchronous/synchronous capable interface for making chat/completion requests to models like Claude. The library includes definitions for all request and response parameters using Python typed objects, automatically handles serialization and deserialization, and wraps HTTP logic (timeouts, retries, error mapping) so that developers can call the API in a clean, high-level way. The SDK supports both synchronous and asynchronous usage (via async/await) depending on context. ... -
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DeepSeek MoE
Towards Ultimate Expert Specialization in Mixture-of-Experts Language
...For example, their MoE variant with 16.4B parameters claims comparable or better performance to standard dense models like DeepSeek 7B or LLaMA2 7B using about 40% of the total compute. The repo publishes both Base and Chat variants of the 16B MoE model (deepseek-moe-16b) and provides evaluation results across benchmarks. It also includes a quick start with inference instructions (using Hugging Face Transformers) and guidance on fine-tuning (DeepSpeed, hyperparameters, quantization). The licensing is MIT for code, with a “Model License” applied to the models. -
13
MiMo-V2-Flash
MiMo-V2-Flash: Efficient Reasoning, Coding, and Agentic Foundation
...It uses an MoE setup where a very large total parameter count is available, but only a smaller subset is activated per token, which helps balance capability with runtime efficiency. The project positions the model for workflows that require tool use, multi-step planning, and higher throughput, rather than only single-turn chat. Architecturally, it highlights attention and prediction choices aimed at accelerating generation while preserving instruction-following quality in complex prompts. The repository typically serves as a launch point for running the model, understanding its intended use cases, and reproducing or extending its evaluation on reasoning and agent-style tasks. ... -
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LLamaSharp
C#/.NET binding of llama.cpp, including LLaMa/GPT model inference
The C#/.NET binding of llama.cpp. It provides APIs to infer the LLaMa Models and deploy it on the local environment. It works on both Windows, Linux and MAC without the requirement for compiling llama.cpp yourself. Its performance is close to llama.cpp. Furthermore, it provides integrations with other projects such as BotSharp to provide higher-level applications and UI. -
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VibeThinker
Diversity-driven optimization and large-model reasoning ability
...It contains about 1.5 billion parameters, far smaller than many “frontier” models, yet it is explicitly optimized for reasoning, mathematics, and code generation tasks rather than general open-domain chat. The innovation lies in its training methodology: the team uses what they call the Spectrum-to-Signal Principle (SSP), where a first stage emphasizes diversity of reasoning paths (the “spectrum” phase) and a second stage uses reinforcement techniques (the “signal” phase) to refine toward correctness and strong reasoning. The result is a model that outpaces many much larger models on domain-specific benchmarks, demonstrating that smaller models, if trained carefully and with the right objectives, can achieve high performance in reasoning-centric tasks. -
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Anthropic SDK TypeScript
Access to Anthropic's safety-first language model APIs
anthropic-sdk-typescript is the TypeScript / JavaScript client library for the Anthropic REST API, enabling backend or Node.js usage of models like Claude. It wraps API endpoints for creating messages, streaming responses, and managing parameters in a type-safe TS environment. The library is designed for server-side use, interfacing with REST, and is stable for integration in web services or backend agents. Example usage shows how to instantiate the Anthropic client, call... -
17
Qwen-VL
Chat & pretrained large vision language model
Qwen-VL is Alibaba Cloud’s vision-language large model family, designed to integrate visual and linguistic modalities. It accepts image inputs (with optional bounding boxes) and text, and produces text (and sometimes bounding boxes) as output. The model variants (VL-Plus, VL-Max, etc.) have been upgraded for better visual reasoning, text recognition from images, fine-grained understanding, and support for high image resolutions / extreme aspect ratios. Qwen-VL supports multilingual inputs... -
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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 with reinforcement learning and human preference alignment for improved instruction-following and function calling. ... -
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Alpaca.cpp
Locally run an Instruction-Tuned Chat-Style LLM
...This combines the LLaMA foundation model with an open reproduction of Stanford Alpaca a fine-tuning of the base model to obey instructions (akin to the RLHF used to train ChatGPT) and a set of modifications to llama.cpp to add a chat interface. Download the zip file corresponding to your operating system from the latest release. The weights are based on the published fine-tunes from alpaca-lora, converted back into a PyTorch checkpoint with a modified script and then quantized with llama.cpp the regular way. -
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Nemotron 3
Large language model developed and released by NVIDIA
NVIDIA-Nemotron-3-Nano-30B-A3B-FP8 is a state-of-the-art large language model developed and released by NVIDIA as part of its Nemotron 3 family, optimized for high-efficiency inference and strong reasoning performance in open AI workloads. It is the post-trained and FP8-quantized variant of the Nemotron 3 Nano model, meaning its weights and activations are represented in 8-bit floating point (FP8) to dramatically reduce memory usage and computational cost while retaining high accuracy. The... -
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Qwen2-7B-Instruct
Instruction-tuned 7B language model for chat and complex tasks
Qwen2-7B-Instruct is a 7.62-billion-parameter instruction-tuned language model from the Qwen2 series developed by Alibaba's Qwen team. Built on a transformer architecture with SwiGLU activation and group query attention, it is optimized for chat, reasoning, coding, multilingual tasks, and extended context understanding up to 131,072 tokens. The model was pretrained on a large-scale dataset and aligned via supervised fine-tuning and direct preference optimization. It shows strong performance across benchmarks such as MMLU, MT-Bench, GSM8K, and Humaneval, often surpassing similarly sized open-source models. ... -
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Grok-2.5
Large-scale xAI model for local inference with SGLang, Grok-2.5
...Grok-2.5 supports advanced inference with multi-GPU configurations, requiring at least 8 GPUs with more than 40 GB of memory each for optimal performance. It integrates with the SGLang framework to enable serving, testing, and chat-style interactions. The model comes with a post-training architecture and requires the correct chat template to function properly. It is released under the Grok 2 Community License Agreement, encouraging community experimentation and responsible use. -
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translategemma-4b-it
Lightweight multimodal translation model for 55 languages
...With a compact ~5B parameter footprint and BF16 support, the model is designed to run efficiently on laptops, desktops, and private cloud infrastructure, making advanced translation accessible without heavy hardware requirements. TranslateGemma uses a structured chat template that enforces explicit source and target language codes, ensuring consistent, deterministic behavior and reducing ambiguity in multilingual pipelines. It integrates seamlessly with Hugging Face Transformers through pipelines or direct model initialization, supporting GPU acceleration and scalable deployment. -
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Ministral 3 8B Instruct 2512
Compact 8B multimodal instruct model optimized for edge deployment
...It combines an 8.4B-parameter language model with a 0.4B vision encoder, enabling both text reasoning and image understanding. This FP8 instruct-fine-tuned variant is optimized for chat, instruction following, and structured outputs, making it ideal for daily assistant tasks and lightweight agentic workflows. Designed for edge deployment, the model can run on a wide range of hardware and fits locally on a single 12GB GPU, with the option for even smaller quantized configurations. Its multilingual support covers dozens of major languages, allowing it to work across diverse global environments and applications. ... -
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Ministral 3 3B Instruct 2512
Ultra-efficient 3B multimodal instruct model built for edge deployment
...It includes a 3.4B-parameter language model paired with a 0.4B vision encoder, enabling it to understand both text and visual inputs. As an FP8 instruct-fine-tuned model, it is optimized for chat, instruction following, and compact agentic tasks while maintaining strong adherence to system prompts. Despite its small size, it delivers efficient real-time performance and can run locally on a single 8GB GPU, with further memory reductions through quantization. It supports dozens of languages across major global regions, making it well-suited for multilingual and embedded applications. ...
