#jupyter_notebook#chatglm#chatglm3#gemma_2b_it#glm_4#internlm2#llama3#llm#lora#minicpm#q_wen#qwen#qwen1_5#qwen2
This guide helps beginners set up and use open-source large language models (LLMs) on Linux or cloud platforms like AutoDL, with step-by-step instructions for environment setup, model deployment, and fine-tuning for models such as LLaMA, ChatGLM, and InternLM[2][4][5]. It covers everything from basic installation to advanced techniques like LoRA and distributed fine-tuning, and supports integration with tools like LangChain and online demo deployment. The main benefit is making powerful AI models accessible and easy to use for students, researchers, and anyone interested in experimenting with or customizing LLMs for their own projects[2][4][5].
https://github.com/datawhalechina/self-llm
📊 ETFs | Weekly Flows Snapshot
SOL ETFs were the only ones with net inflows among the top 3 this week.
#ETF#Flows#Markets
#BTC#ETH#SOL#Crypto
———
結構解讀關鍵👇🥇資源搜索🖲️👆
📉 本週三大主流 ETF 資金流向出現明顯分化:
• BTC ETF:持續淨流出,賣壓集中於大型發行商
• ETH ETF:連續多日負流量,短線情緒偏保守
• SOL ETF:唯一錄得淨流入,資金逆勢布局
📈 結構解讀:
• 資金正在 避開高擁擠交易(BTC / ETH)
• SOL 成為短線輪動與主題交易承接標的
• ETF 流向顯示:市場並非全面風險退出,而是 選擇性進場
⚡️ 觀察重點:
若 SOL ETF 流入延續,可能代表
👉 資金正在測試「非 BTC / ETH 主流替代敘事」
👇⭐️👇
🤣留言分享觀點
🥲👇