Github LLMs

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GitHub - mendableai/firecrawl: 🔥 Turn entire websites into LLM-ready markdown or structured data. Scrape, crawl and extract with…

🔥 Turn entire websites into LLM-ready markdown or structured data. Scrape, crawl and extract with a single API. - mendableai/firecrawl

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565 views08:42

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Awesome LLM Strawberry (OpenAI o1)

▪ Github

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4.06K viewsedited 19:35

MiniCPM-V

MiniCPM-V 2.6: A GPT-4V Level MLLM for Single Image, Multi Image and Video on Your Phone

Creator: OpenBMB
Stars ⭐️: 11.4k
Forked By: 798
GitHub Repo:
https://github.com/OpenBMB/MiniCPM-V

➖➖➖➖➖➖➖➖➖➖➖➖➖➖
Join ✅https://t.iss.one/deep_learning_proj

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GitHub - OpenBMB/MiniCPM-o: MiniCPM-o 2.6: A GPT-4o Level MLLM for Vision, Speech and Multimodal Live Streaming on Your Phone

MiniCPM-o 2.6: A GPT-4o Level MLLM for Vision, Speech and Multimodal Live Streaming on Your Phone - OpenBMB/MiniCPM-o

576 views11:22

LLM based Multi-Agent methods

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Github: https://github.com/AgnostiqHQ/multi-agent-llm

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Paper: https://arxiv.org/abs/2409.12618v1

🤗 Dataset: https://paperswithcode.com/dataset/hotpotqa

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GitHub - AgnostiqHQ/multi-agent-llm: Lean implementation of various multi-agent LLM methods, including Iteration of Thought (IoT)

Lean implementation of various multi-agent LLM methods, including Iteration of Thought (IoT) - AgnostiqHQ/multi-agent-llm

4.56K views17:38

🌟 GRIN MoE: Mixture-of-Experts от Microsoft.

🟢total parameters: 16x3.8B;
🟢active parameters: 6.6B;
🟢context length: 4096;
🟢number of embeddings 4096;
🟢number of layers: 32;

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Arxiv

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Demo

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Github

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llama-stack

Model components of the Llama Stack APIs

Creator: Meta Llama
Stars ⭐️: 1.5k
Forked By: 137
https://github.com/meta-llama/llama-stack

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GitHub - meta-llama/llama-stack: Composable building blocks to build Llama Apps

Composable building blocks to build Llama Apps. Contribute to meta-llama/llama-stack development by creating an account on GitHub.

708 views12:42

Crawl 4 AI

Crawl4AI: Open-source LLM Friendly Web Crawler & Scrapper

Creator: UncleCode
Stars ⭐️: 8.6k
Forked By: 627
https://github.com/unclecode/crawl4ai

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GitHub - unclecode/crawl4ai: 🚀🤖 Crawl4AI: Open-source LLM Friendly Web Crawler & Scraper. Don't be shy, join here: https://dis…

🚀🤖 Crawl4AI: Open-source LLM Friendly Web Crawler & Scraper. Don't be shy, join here: https://discord.gg/jP8KfhDhyN - unclecode/crawl4ai

4.29K views09:19

🔥 NVIDIA silently release a Llama 3.1 70B fine-tune that outperforms
GPT-4o and Claude Sonnet 3.5

Llama 3.1 Nemotron 70B Instruct a further RLHFed model on
huggingface

https://huggingface.co/collections/nvidia/llama-31-nemotron-70b-670e93cd366feea16abc13d8

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4.15K viewsedited 20:09

GitHub

https://t.iss.one/deep_learning_proj

🌟 Zamba2-Instruct

В семействе 2 модели:

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Zamba2-1.2B-instruct;

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Zamba2-2.7B-instruct.

# Clone repo
git clone https://github.com/Zyphra/transformers_zamba2.git
cd transformers_zamba2

# Install the repository & accelerate:
pip install -e .
pip install accelerate

# Inference:
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch

tokenizer = AutoTokenizer.from_pretrained("Zyphra/Zamba2-2.7B-instruct")
model = AutoModelForCausalLM.from_pretrained("Zyphra/Zamba2-2.7B-instruct", device_map="cuda", torch_dtype=torch.bfloat16)

user_turn_1 = "user_prompt1."
assistant_turn_1 = "assistant_prompt."
user_turn_2 = "user_prompt2."
sample = [{'role': 'user', 'content': user_turn_1}, {'role': 'assistant', 'content': assistant_turn_1}, {'role': 'user', 'content': user_turn_2}]
chat_sample = tokenizer.apply_chat_template(sample, tokenize=False)

input_ids = tokenizer(chat_sample, return_tensors='pt', add_special_tokens=False).to("cuda")
outputs = model.generate(**input_ids, max_new_tokens=150, return_dict_in_generate=False, output_scores=False, use_cache=True, num_beams=1, do_sample=False)
print((tokenizer.decode(outputs[0])))

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3.51K viewsedited 19:16

Github

https://t.iss.one/deep_learning_proj

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LLM-Agent-Paper-List is a repository of papers on the topic of agents based on large language models (LLM)! The papers are divided into categories such as LLM agent architectures, autonomous LLM agents, reinforcement learning (RL), natural language processing methods, multimodal approaches and tools for developing LLM agents, and more.

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4.06K viewsedited 04:36

https://github.com/andrewyng/aisuite
#LLMs

https://t.iss.one/deep_learning_proj

2.99K viewsedited 18:51

LLM-based agents for Software Engineering
"Large Language Model-Based Agents for Software Engineering: A Survey".

https://github.com/FudanSELab/Agent4SE-Paper-List.

https://t.iss.one/deep_learning_proj

3.06K viewsedited 19:26

Welcome to Ollama's Prompt Engineering Interactive Tutorial

🔗 Github

https://t.iss.one/deep_learning_proj

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3.33K viewsedited 14:24

Forwarded from Machine learning books and papers

⚡️ MobileLLM

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MobileLLM-125M. 30 Layers, 9 Attention Heads, 3 KV Heads. 576 Token Dimension;

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MobileLLM-350M. 32 Layers, 15 Attention Heads, 5 KV Heads. 960 Token Dimension;

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MobileLLM-600M. 40 Layers, 18 Attention Heads, 6 KV Heads. 1152 Token Dimension;

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MobileLLM-1B. 54 Layers, 20 Attention Heads, 5 KV Heads. 1280 Token Dimension;

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Arxiv

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GitHub

@Machine_learn

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930 views03:00

Fine_Tuning_LLMs_with_Hugging_Face_Partial_Code.ipynb

214.1 KB

Fine Tuning LLMs with Hugging Face LLMs Code

https://t.iss.one/deep_learning_proj

3.46K viewsedited 03:04

Forwarded from Machine learning books and papers

🌟 BioNeMo: A Framework for Developing AI Models for Drug Design.

NVIDIA BioNeMo2 Framework is a set of tools, libraries, and models for computational drug discovery and design.

▶️ Pre-trained models:

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ESM-2 is a pre-trained bidirectional encoder (BERT-like) for amino acid sequences. BioNeMo2 includes checkpoints with parameters 650M and 3B;

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Geneformer is a tabular scoring model that generates a dense representation of a cell's scRNA by examining co-expression patterns in individual cells.

▶️ Datasets:

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CELLxGENE is a collection of publicly available single-cell datasets collected by the CZI (Chan Zuckerberg Initiative) with a total volume of 24 million cells;

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UniProt is a database of clustered sets of protein sequences from UniProtKB, created on the basis of translated genomic data.

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Project page

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Documentation

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GitHub

@Machine_learn

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913 views15:15

GitHub

https://t.iss.one/deep_learning_proj

🌟 LLaMA-Mesh:

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Arxiv

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4.14K viewsedited 20:40

Forwarded from Machine learning books and papers

Large Language Models Course: Learn by Doing LLM Projects

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Github: https://github.com/peremartra/Large-Language-Model-Notebooks-Course

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Paper: https://doi.org/10.31219/osf.io/qgxea

@Machine_learn

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825 views13:09

Foundations of Large Language Models (1).pdf

Forwarded from Machine learning books and papers

1.9 MB

Foundations of Large Language Models

📝 Table of Contents:
● Pre-training
● Generative Models
● Prompting
● Alignment

Tong Xiao and Jingbo Zhu
January 17, 2025

📃 Download from arXiv.

@Machine_learn

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704 views20:18

Evolutionary Computation in the Era of Large Language Model: Survey and Roadmap

Large language models (LLMs) have not only revolutionized natural language processing but also extended their prowess to various domains, marking a significant stride towards artificial general intelligence. The interplay between LLMs and evolutionary algorithms (EAs), despite differing in objectives and methodologies, share a common pursuit of applicability in complex problems. Meanwhile, EA can provide an optimization framework for LLM's further enhancement under black-box settings, empowering LLM with flexible global search capacities. On the other hand, the abundant domain knowledge inherent in LLMs could enable EA to conduct more intelligent searches. Furthermore, the text processing and generative capabilities of LLMs would aid in deploying EAs across a wide range of tasks. Based on these complementary advantages, this paper provides a thorough review and a forward-looking roadmap, categorizing the reciprocal inspiration into two main avenues: LLM-enhanced EA and EA-enhanced #LLM. Some integrated synergy methods are further introduced to exemplify the complementarity between LLMs and EAs in diverse scenarios, including code generation, software engineering, neural architecture search, and various generation tasks. As the first comprehensive review focused on the EA research in the era of #LLMs, this paper provides a foundational stepping stone for understanding the collaborative potential of LLMs and EAs. The identified challenges and future directions offer guidance for researchers and practitioners to unlock the full potential of this innovative collaboration in propelling advancements in optimization and artificial intelligence.

Paper: https://arxiv.org/pdf/2401.10034v3.pdf

Code: https://github.com/wuxingyu-ai/llm4ec

https://t.iss.one/deep_learning_proj

2.98K viewsedited 12:03