#4-bit quantization
17 related articles
AI Large Language Model Learning Roadm…
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AI Large Language Model Learning Roadmap: A Complete Guide from Zero to Project Implementation
A systematic AI LLM learning roadmap covering prompt engineering, RAG, AI Agent development, and fine-tuning — with beginner-friendly paths and practical tips.
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·2 min
LlamaFactory: A Comprehensive Guide to the Open-Source Framework for Unified Fine-Tuning of 100+ LLMs
Deep dive into LlamaFactory, an open-source unified fine-tuning framework supporting 100+ LLMs and VLMs with LoRA, QLoRA, RLHF methods, Web UI, 71K+ GitHub Stars, accepted at ACL 2024.
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Tutorials·3 min
Fully Automated Invoice Reimbursement with Local AI Agent: From Process Breakdown to Deployment
How to build a fully automated invoice reimbursement system with local AI Agents, covering OCR, info extraction, and form generation with MinerU+Qwen3+Qianwen Po.
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Deep Dives·3 min
Complete Guide to LLM Training: Pre-training, SFT Fine-tuning, and Preference Alignment Explained
Complete guide to the three core LLM training stages: pre-training, supervised fine-tuning (SFT), and preference alignment (DPO/PPO), covering LoRA, distillation, quantization, and pruning.
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Tutorials·2 min
Ollama Getting Started Guide: The Best Tool for Locally Deploying Open-Source LLMs
A detailed guide to Ollama's core features: free open-source local LLM management with cross-platform support, intelligent GPU/CPU scheduling, and API integration for running DeepSeek and other open-source models locally at zero cost.
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Tutorials·2 min
Ollama Local LLM Deployment: From Installation to Conversation in Three Steps
Learn how to deploy LLMs locally with Ollama in three simple steps: install, choose a model, and run. No coding required, supports offline use, and completely free.
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Tutorials·3 min
One Person, Three Machines: Local Agent Deployment and Multi-Machine Collaborative Operations in Practice
Deploy Cloud Code and Hermes AI Agents to efficiently manage three physical hosts solo. Covers Ventoy single-file deployment, BTRFS+RAW Image setup, Agent task division, and risk control strategies.
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Tutorials·2 min
Tutorial: Building a Low-Cost AI Code Editor with DeepSeek-V3 + VSCode
Step-by-step tutorial: Build a low-cost AI programming assistant using DeepSeek-V3 API with VSCode's Continue plugin. Covers setup, API Key configuration, code completion demo, and Ollama local deployment.
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Tutorials·2 min
Complete Guide to Configuring Local DeepSeek Model in PyCharm for AI-Assisted Programming
Learn how to configure a local DeepSeek model in PyCharm via Ollama for free, privacy-safe AI-assisted programming. Includes installation steps, plugin setup, usage tips, and hardware recommendations.
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Product Reviews·4 min
OpenHuman Deep Dive: A Context-First Open-Source Personal AI Agent
Deep dive into OpenHuman open-source AI Agent: context-first architecture, Rust+React hybrid, Memory Tree system, Token Juice compression, and multi-model routing.
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Tutorials·3 min
Can You Really Transition to AI/LLM in Three Months? A Deep Dive into the Learning Roadmap
Deep dive into a popular 3-month AI/LLM transition roadmap: from Python basics and Prompt engineering to LangChain, RAG, Agents, and hands-on projects, with realistic time estimates and pitfall warnings.
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Product Reviews
Llama 3.3 70B In-Depth Review: Testing…
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Llama 3.3 70B In-Depth Review: Testing the Strongest Open-Source LLM with 13 Questions
Meta releases Llama 3.3 70B open-source model with just 70B parameters rivaling 405B performance. Tested on 13 logic, math, and coding questions, it passed 12 — reshaping the open-source model landscape.
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Industry Insights
Deep Dive into Three Major LLM Career …
·3 min
Deep Dive into Three Major LLM Career Paths: Requirements, Tech Stacks, and Career Prospects
Deep analysis of three core LLM roles—Application Engineer, Development Engineer, and Algorithm Engineer—covering technical requirements, salary thresholds, and career prospects including RAG, fine-tuning, and inference deployment.
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Product Reviews
Qwen 3.6 vs Gemma 4: In-Depth Comparis…
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Qwen 3.6 vs Gemma 4: In-Depth Comparison of Local AI Coding Models Through Real-World Development
Real-world comparison of Qwen 3.6 and Gemma 4 local AI models building a Markdown editor with Tauri, testing planning ability, code generation, and development efficiency.
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Tutorials
Decoding LLM Naming Conventions: Param…
·3 min
Decoding LLM Naming Conventions: Parameter Counts, Quantization Formats & VRAM Requirements Quick Reference
Decode LLM naming conventions, understand 32B parameters & AWQ/GGUF quantization formats, with 4-bit VRAM estimation formulas, MOE model pitfalls, and model selection by GPU tier.
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Tutorials
LLM Learning Roadmap: A Complete Guide…
·3 min
LLM Learning Roadmap: A Complete Guide from Beginner to Project Implementation Across Seven Core Modules
A systematic breakdown of seven core LLM learning modules covering environment setup, Prompt Engineering, RAG, Agents, dev frameworks, fine-tuning, and hands-on projects for developers.
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Product Reviews
Three AI Agents Tested Head-to-Head: W…
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Three AI Agents Tested Head-to-Head: Which One Handles E-Commerce Livestream Data Analysis Best?
Testing three AI Agents on e-commerce livestream data analysis: local deployment memory limits, costly overseas APIs, and how a cloud-based multi-model solution delivers a complete business workflow.
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