firecrawl

Turn entire websites into LLM-ready markdown or structured data. Scrape, crawl and extract with a single API.
                                                                   
Creator: Mendable
Stars ⭐️: 12.3k
Forked By: 861
GitHub Repo:
https://github.com/mendableai/firecrawl

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

🖥 Awesome LLM Strawberry (OpenAI o1)



▪ Github

✅https://t.iss.one/deep_learning_proj

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

LLM based Multi-Agent methods

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

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

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

✅https://t.iss.one/deep_learning_proj

🌟 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;
✅https://t.iss.one/deep_learning_proj


🟡Arxiv
🟡Demo
🖥Github

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


✅https://t.iss.one/deep_learning_proj

Crawl 4 AI

Crawl4AI: Open-source LLM Friendly Web Crawler & Scrapper

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

✅https://t.iss.one/deep_learning_proj

🔥 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
✅https://t.iss.one/deep_learning_proj

🌟 Zamba2-Instruct

В семействе 2 модели:

🟢Zamba2-1.2B-instruct;
🟠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])))




🖥GitHub

https://t.iss.one/deep_learning_proj