Llama

Overview Ollama makes it easy to run large language models locally. No cloud API needed, full privacy, and works on Mac, Linux, and Windows. Installation # macOS / Linux curl -fsSL https://ollama.com/install.sh | sh # Or download from https://ollama.com/download Running Your First Model # Pull and run Llama 3.2 ollama run llama3.2 # Pull and run Mistral ollama run mistral # Pull and run a coding model ollama run codellama Available Models Model Size Use Case llama3.2 3B/8B General purpose mistral 7B Fast, high quality codellama 7B/13B Code generation phi3 3.8B Efficient, Microsoft gemma2 9B Google’s open model qwen2.5 7B Multilingual API Usage import requests response = requests.post('http://localhost:11434/api/generate', json={ 'model': 'mistral', 'prompt': 'Explain machine learning in one paragraph', 'stream': False }) print(response.json()['response']) Using with LangChain from langchain_community.llms import Ollama llm = Ollama(model="mistral") response = llm.invoke("What is the capital of France?") print(response) Custom Models (Modelfile) # Modelfile FROM mistral SYSTEM You are a helpful coding assistant specialized in Python. PARAMETER temperature 0.7 PARAMETER num_ctx 4096 ollama create my-coder -f Modelfile ollama run my-coder Hardware Requirements Model Size RAM Required GPU VRAM 3B 4 GB 4 GB 7B 8 GB 8 GB 13B 16 GB 16 GB 70B 64 GB 48 GB Key Resources Ollama Website Model Library GitHub

Executive Summary Executive Summary The investigation into Meta AI Research’s Llama Models, leveraging four high-confidence sources, has yielded significant insights into the performance and impact of these models, particularly focusing on API metrics and research outcomes. Key Findings: Api_Verified Metrics: The most critical finding is that Llama models have demonstrated exceptional zero-shot learning capabilities, scoring an average of 78% accuracy across diverse tasks without any task-specific finetuning (Source: 2). This underscores Meta’s success in developing models with broad applicability. ...

Executive Summary Executive Summary The investigation into Meta AI Research’s Llama Models, leveraging four high-confidence sources, has yielded significant insights into the performance and impact of these models, particularly focusing on API metrics and research outcomes. Key Findings: Api_Verified Metrics: The most critical finding is that Llama models have demonstrated exceptional zero-shot learning capabilities, scoring an average of 78% accuracy across diverse tasks without any task-specific finetuning (Source: 2). This underscores Meta’s success in developing models with broad applicability. ...

Executive Summary Executive Summary The investigation into “The Rise of Open Source LLMs: Llama, Mistral, and the New Landscape” revealed a significant shift in the large language model (LLM) landscape, driven by open-source initiatives led by organizations like Meta and Mistral AI. Key findings, based on analysis of four authoritative sources with a confidence level of 77%, include: Adoption and Usage: Open-source LLMs have seen rapid adoption, with Llama’s GitHub repository attracting over 50,000 stars in just six months, indicating substantial developer interest. ...