NVIDIA and Lilly Announce Co-Innovation AI Lab to Reinvent Drug Discovery In the Age of AI | Eli Lilly and Company - Eli Lilly
NVIDIA and Lilly Announce Co-Innovation AI Lab to Reinvent Drug Discovery In the Age of AI | Eli Lilly and Company Eli Lilly
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Error While using langchain with huggingface models
from langchain_core.prompts import PromptTemplate from langchain_community.llms import HuggingFaceEndpoint import os os.environ[“HUGGINGFACEHUB_API_TOKEN”] = “hf_your_new_token_here” prompt = PromptTemplate( input_variables=[“product”], template=“What is a good name for a company that makes {product}?” ) llm = HuggingFaceEndpoint( repo_id=“mistralai/Mistral-7B-Instruct-v0.3”, temperature=0.7, timeout=300 ) chains = prompt | llm print(“LLM Initialized with Token!”) try: response = chains.invoke({“product”: “camera”}) print(“AI Suggestion:”, response) except Exception as e: print(f"Error details: {e}") when i run this i get Value error can anyone help me out? Its a basic prompt template and text gen code but still it doesnt work i used various models from Huggingface and its not working well
According to Microsoft Copilot Terms of Use, updated in Oct. 2025, "Copilot is for entertainment purposes only" and "Don t rely on Copilot for important advice" (Jowi Morales/Tom s Hardware)
Jowi Morales / Tom's Hardware : According to Microsoft Copilot Terms of Use, updated in Oct. 2025, Copilot is for entertainment purposes only and Don't rely on Copilot for important advice These might be boilerplate disclaimers, but they kind of contradict the company's ads and marketing.
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Docling Studio — open-source visual inspection tool for Docling pipelines
Hey everyone I built Docling Studio , an open-source visual inspection layer for Docling. The problem: if you’ve used Docling, you know the extraction engine is powerful — but validating outputs means digging through JSON and mentally mapping bounding box coordinates back to the original pages. No visual feedback loop. What Docling Studio does: Upload a PDF, configure your pipeline (OCR engine, table extraction, enrichment) Run the conversion Visually inspect every detected element — bounding boxes overlaid on original pages, element types, content preview on click Two modes: local (embedded Docling) or remote (Docling Serve) Stack: Vue 3 / TypeScript + FastAPI / Python, fully Dockerized (multi-arch), 180+ tests. Why it matters for RAG workflows: without seeing what Docling extracts, it’s
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