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Awesome-LLM-Resources-List
A Curated Collection of LLM resources (work in progress).
Stars: 95
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Awesome LLM Resources is a curated collection of resources for Large Language Models (LLMs) covering various aspects such as serverless hosting, accessing off-the-shelf models via API, local inference, LLM serving frameworks, open-source LLM web chat UIs, renting GPUs for fine-tuning, fine-tuning with no-code UI, fine-tuning frameworks, OS agentic/AI workflow, AI agents, co-pilots, voice API, open-source TTS models, OS RAG frameworks, research papers on chain-of-thought prompting, CoT implementations, CoT fine-tuned models & datasets, and more.
README:
A Curated Collection of LLM resources. ๐กโจ
๐ Updated: 8th November 2024
Platform/Tool | Rel. | Scale Down | OS ๐ | GH | Start | GPU Machine | One-Click | Dev Exp. | Free-Tier |
---|---|---|---|---|---|---|---|---|---|
Beam.Cloud | 2021 | > 1 min | Helpers | โ | โ | ๐ | ๐ 15h | ||
Baseten | 2019 | > 15 min | ๐ด | Guide | โ | ๐ก | ๐ | $30 | |
Modal | 2021 | < 1 min | ๐ด | Helpers | โ | โ | ๐ | $30/m | |
HF Endpoints | 2023 | > 15 min | ๐ด | None Needed | โ | โ | ๐ | โ | |
Replicate | 2019 | < 1 min | ๐ด | Guide | โ | ๐ก | ๐คท | โ | |
Sagemaker (Serverless) | 2017 | N/A | ๐ด | N/A | ๐ต | โ | โ | 300,000s | |
Lambda w/ EFS (AWS) | 2014 | < 1 min | ๐ด | Guide | ๐ก | โ | โ | โ | |
RunPod Serverless | 2022 | > 30s | ๐ด | N/A | ๐ก | โ | ๐คท | โ | |
BentoML | 2019 | > 5 min | Gallery | ๐ก | ๐ก | ๐ | ๐ $10 |
It goes without saying that these platforms can usually do more than LLM serving**
Platform/Tool | Released | GitHub |
---|---|---|
Together.ai | N/A | ๐ด |
Fireworks.ai | N/A | |
Replicate | 2019 | |
Groq | N/A | |
DeepInfra | N/A | |
Bedrock | N/A | |
Lepton | N/A | |
Fal.ai | N/A | |
VertexAI | N/A |
Framework | Browser Chat ๐ฅ๏ธ | Organization | Open Source | GitHub |
---|---|---|---|---|
Llama.cpp | โ | ggerganov | ||
Ollama | โ | Ollama | ||
gpt4all | โ | Nomic.ai | ||
LMStudio | โ | LMStudio AI | ๐ด | |
OpenLLM | โ | BentoML |
Framework | Open Source | GitHub |
---|---|---|
vLLM | ||
OpenLLM | ||
TGI (Text Generation Inference) | ||
TensorRT LLM | ||
Ray Serve | ||
LMDeploy | ||
Ollama | ||
MLC-LLM |
Tool | Organization | Description | Open Source | GitHub |
---|---|---|---|---|
Text Generation WebUI | oobabooga | A Gradio web UI for Large Language Models. | ||
Jan AI | Jan HQ | Jan is an open source alternative to ChatGPT that runs 100% offline on your computer. Multiple engine support (llama.cpp, TensorRT-LLM) | ||
AnythingLLM | Mintplex Labs | The all-in-one Desktop & Docker AI application with built-in RAG, AI agents, and more. | ||
Superagent | Superagent AI | Superagent allows any developer to add powerful AI assistants to their applications. These assistants use large language models (LLM), retrieval augmented generation (RAG), and generative AI to help users. | ||
Bionic-GPT | Bionic GPT | Replacement for ChatGPT, offering the advantages of Generative AI while maintaining strict data confidentiality. | ||
Open WebUI | Open WebUI | A user-friendly web interface for interacting with Large Language Models (LLMs). |
Platform | Templates | Beginner Friendly | GitHub |
---|---|---|---|
Brev.dev | Fine-tuning | โ | |
Modal | Fine-tuning | โ | |
Hyperbolic AI | None | โ | |
RunPod | None | โ | |
Paperspace | Fine-tuning | โ | |
Colab | Small models only | โ |
Tool | Beginner Friendly | Open Source | GitHub |
---|---|---|---|
Together.ai | โ | โ | N/A |
Hugging Face AutoTrain | โ | โ | |
AutoML | โ | โ | |
LLaMA-Factory | โ | โ | |
H2O LLM Studio | โ | โ |
Framework | Open Source | GitHub |
---|---|---|
Axolotl | ||
Unsloth |
Framework | Open Source | Beginner Friendly | Released | GitHub |
---|---|---|---|---|
LangChain | โ | 2022 | ||
LangGraph | โ | 2023 | ||
LlamaIndex | โ | 2023 | ||
Langroid | โ | 2023 | ||
Flowise | โ | 2023 | ||
Swarms | โ | 2023 | ||
CrewAI | โ | 2023 | ||
Autogen | โ | 2023 | ||
AutoChain | โ | 2023 | ||
SuperAGI | โ | 2023 | ||
AILegion | โ | 2023 | ||
MemGPT (Letta) | โ | 2023 | ||
uAgents | โ | 2023 | ||
Phidata | โ | 2023 | ||
Dify | โ | 2024 | ||
TaskingAI | โ | 2024 | ||
Bee Agent Framework | โ | 2024 | ||
Swarms | โ | 2024 | ||
IoA | โ | 2024 | ||
Atomic Agents | โ | 2024 |
Framework | Organization | Open Source | Released | GitHub |
---|---|---|---|---|
GPT Engineer | GPT Engineer Org | 2023 | ||
XAgent | OpenBMB | 2023 | ||
FinRobot | AI4Finance Foundation | 2024 | ||
STORM | Stanford OVAL | 2024 | ||
Multion | MULTI-ON | ๐ด | N/A | |
Minion | Minion AI | ๐ด | N/A |
Framework | Open Source | GitHub |
---|---|---|
Aider | ||
Cursor | ||
Continue |
Framework | Open Source | GitHub |
---|---|---|
VAPI.ai | ๐ด | |
Bland.ai | ๐ด | N/A |
CallAnnie | ๐ด | N/A |
RealtimeTTS | ||
RealtimeSTT | ||
Coqui TTS |
Model | License | Stars/Likes | Downloads (Last Month) | Repository |
---|---|---|---|---|
Kokoro-82M | Apache 2.0 | โญ 3.16k (HF) | ๐ฅ 557,392 | Hugging Face |
Zonos-v0.1-transformer | Apache 2.0 | โญ 249 (HF) | ๐ฅ 24,240 | Hugging Face |
XTTS-v2 | Non-Commercial | โค๏ธ 368 (HF) | ๐ฅ 2,545,850 | Hugging Face |
ChatTTS | AGPL-3.0 | N/A | N/A | GitHub |
MeloTTS | MIT | N/A | N/A | GitHub |
For more TTS models and rankings, check out the TTS Leaderboard.
Framework | Organization | Open Source | Released | GitHub |
---|---|---|---|---|
Haystack | deepset.ai | 2023 | ||
RAGflow | Infiniflow | 2024 | ||
txtai | Neuml | 2022 | ||
LLM App | Pathway | 2023 | ||
Cognita | Truefoundry | 2024 | ||
R2R | SciPhi AI | 2024 | ||
Raptor | Parth Sarthi | 2024 |
See RAG_Techniques if you get stuck (not always needed)
Publication Date | Title | ๐ | Authors | Organization | Technique |
---|---|---|---|---|---|
January 28, 2022 | Chain-of-Thought Prompting Elicits Reasoning in Large Language Models | ๐ | Jason Wei, et al. | DeepMind | CoT Prompting |
March 21, 2022 | Self-Consistency Improves Chain of Thought Reasoning in Language Models | ๐ | Xuezhi Wang et al. | DeepMind | CoT with Self-Consistency |
May 21, 2022 | Least-to-Most Prompting Enables Complex Reasoning in Large Language Models | ๐ | Denny Zhou et al. | DeepMind | Least-to-Most Prompting |
May 21, 2022 | Large Language Models are Zero-Shot Reasoners | ๐ | Takeshi Kojima, et al. | DeepMind | Zero-shot-CoT |
October 6, 2022 | ReAct: Synergizing Reasoning and Acting in Language Models | ๐ | Shunyu Yao et al. | Princeton University | ReAct |
April 1, 2023 | Teaching Large Language Models to Self-Debug | ๐ | Xiang Lisa Li, et al. | DeepMind, Stanford University | Self-Debugging |
May 6, 2023 | Plan-and-Solve Prompting: Improving Zero-Shot Chain-of-Thought Reasoning by Large Language Models | ๐ | Lei Wang et al. | The Chinese University of Hong Kong, SenseTime Research | Plan-and-Solve Prompting |
May 23, 2023 | Letโs Verify Step by Step | ๐ | Anya Goyal, et al. | DeepMind | Verification for CoT |
October 3, 2023 | Large Language Models Cannot Self-Correct Reasoning Yet | ๐ | Qingxiu Dong, et al. | The Chinese University of Hong Kong, Huawei Noah's Ark Lab | Self-Correction in LLMs |
November 2023 | Universal Self-Consistency for Large Language Model Generation | ๐ | Xinyun Chen, Renat Aksitov, Uri Alon, Jie Ren, Kefan Xiao, Pengcheng Yin, Sushant Prakash, Charles Sutton, Xuezhi Wang, Denny Zhou | DeepMind | Universal Self-Consistency |
May 17, 2023 | Tree of Thoughts: Deliberate Problem Solving with Large Language Models | ๐ | Shunyu Yao, et al. | Princeton University, DeepMind | Tree-of-Thought |
February 15, 2024 | Chain-of-Thought Reasoning Without Prompting | ๐ | Xuezhi Wang, Denny Zhou | DeepMind | Chain-of-Thought Decoding |
March 21, 2024 | ChainLM: Empowering Large Language Models with Improved Chain-of-Thought Prompting | ๐ | Xiaoxue Cheng et al. | Renmin University of China | CoTGenius |
June 2024 | Language Agent Tree Search Unifies Reasoning, Acting, and Planning in Language Models | ๐ | Andy Zhou, Kai Yan, Michal Shlapentokh-Rothman, Haohan Wang, Yu-Xiong Wang | Language Agent Tree Search (LATS) | |
May 2024 | Monte Carlo Tree Search Boosts Reasoning via Iterative Preference Learning | ๐ | Yuxi Xie, et al. | National University of Singapore, DeepMind | MCTS |
September 18, 2024 | To CoT or not to CoT? Chain-of-thought helps mainly on math and symbolic reasoning | ๐ | Zayne Sprague, et al. | The University of Texas at Austin, Johns Hopkins University, Princeton University | Meta-analysis of CoT |
September 25, 2024 | Chain-of-Thoughtlessness? An Analysis of CoT in Planning | ๐ | Kaya Stechly, et al. | Arizona State University | Analysis of CoT in Planning |
October 18, 2024 | Supervised Chain of Thought | ๐ | Xiang Zhang, Dujian Ding | University of British Columbia | Supervised Chain of Thought |
October 24, 2024 | On examples: A Theoretical Understanding of Chain-of-Thought: Coherent Reasoning and Error-Aware Demonstration | ๐ | Zhiqiang Hu, et al. | Amazon, Michigan State University | Theoretical Analysis of CoT |
Implementation | Link | Author | GitHub Stars | GitHub Followers |
---|---|---|---|---|
CoT | chain-of-thought-hub | Franx Yao | ||
CoT | optillm | Codelion | ||
CoT | auto-cot | Amazon Science | ||
CoT | g1 | BKlieger Groq | ||
Decoding CoT | optillm/cot_decoding.py | Codelion | ||
Tree of Thoughts | tree-of-thought-llm | Princeton NLP | ||
Tree of Thoughts | tree-of-thoughts | Kye Gomez | ||
Tree of Thoughts | saplings | Shobrook | ||
MCTS | optillm/mcts.py | Codelion | ||
Graph of Thoughts | graph-of-thoughts | SPCL | ||
Other | CPO | SAIL SG | ||
Other | Everything-of-Thoughts-XoT | Microsoft |
Model Name | Author | Size | Link |
---|---|---|---|
CoT-T5-3B | KAIST AI | 3B | ๐ |
CoT-T5-11B | KAIST AI | 11B | ๐ |
Llama-3.2V-11B-cot | Xkev | 11B | ๐ |
Llama-3.1-8B-Instruct-Reasoner-1o1_v0.3 | Lyte | 8B | ๐ |
Dataset Name | Author | Data Size | Likes | Link |
---|---|---|---|---|
chain-of-thought-sharegpt | Isaiah Bjork | 7.14k rows | ๐ 8 | ๐ |
CoT-Collection | KAIST AI | 1.84 million rows | ๐ 122 | ๐ |
Reasoner-1o1-v0.3-HQ | Lyte | 370 rows | ๐ 7 | ๐ |
OpenLongCoT-Pretrain | qq8933 | 103k rows | ๐ 86 | ๐ |
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weave
Weave is a toolkit for developing Generative AI applications, built by Weights & Biases. With Weave, you can log and debug language model inputs, outputs, and traces; build rigorous, apples-to-apples evaluations for language model use cases; and organize all the information generated across the LLM workflow, from experimentation to evaluations to production. Weave aims to bring rigor, best-practices, and composability to the inherently experimental process of developing Generative AI software, without introducing cognitive overhead.
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LLMStack
LLMStack is a no-code platform for building generative AI agents, workflows, and chatbots. It allows users to connect their own data, internal tools, and GPT-powered models without any coding experience. LLMStack can be deployed to the cloud or on-premise and can be accessed via HTTP API or triggered from Slack or Discord.
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VisionCraft
The VisionCraft API is a free API for using over 100 different AI models. From images to sound.
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kaito
Kaito is an operator that automates the AI/ML inference model deployment in a Kubernetes cluster. It manages large model files using container images, avoids tuning deployment parameters to fit GPU hardware by providing preset configurations, auto-provisions GPU nodes based on model requirements, and hosts large model images in the public Microsoft Container Registry (MCR) if the license allows. Using Kaito, the workflow of onboarding large AI inference models in Kubernetes is largely simplified.
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PyRIT
PyRIT is an open access automation framework designed to empower security professionals and ML engineers to red team foundation models and their applications. It automates AI Red Teaming tasks to allow operators to focus on more complicated and time-consuming tasks and can also identify security harms such as misuse (e.g., malware generation, jailbreaking), and privacy harms (e.g., identity theft). The goal is to allow researchers to have a baseline of how well their model and entire inference pipeline is doing against different harm categories and to be able to compare that baseline to future iterations of their model. This allows them to have empirical data on how well their model is doing today, and detect any degradation of performance based on future improvements.
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tabby
Tabby is a self-hosted AI coding assistant, offering an open-source and on-premises alternative to GitHub Copilot. It boasts several key features: * Self-contained, with no need for a DBMS or cloud service. * OpenAPI interface, easy to integrate with existing infrastructure (e.g Cloud IDE). * Supports consumer-grade GPUs.
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spear
SPEAR (Simulator for Photorealistic Embodied AI Research) is a powerful tool for training embodied agents. It features 300 unique virtual indoor environments with 2,566 unique rooms and 17,234 unique objects that can be manipulated individually. Each environment is designed by a professional artist and features detailed geometry, photorealistic materials, and a unique floor plan and object layout. SPEAR is implemented as Unreal Engine assets and provides an OpenAI Gym interface for interacting with the environments via Python.
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Magick
Magick is a groundbreaking visual AIDE (Artificial Intelligence Development Environment) for no-code data pipelines and multimodal agents. Magick can connect to other services and comes with nodes and templates well-suited for intelligent agents, chatbots, complex reasoning systems and realistic characters.