#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
#ATA/USDT analysis :
#ATA is currently finding support above the 200-period exponential moving average (200 EMA) within the support zone. The price is anticipated to test this zone and maintain its bullish momentum to reach the previous swing high.
TF : 4h
Entry : $0.0900
Target : $0.0983
SL : $0.0850
Currently, #ATAUSDT is compressed within a falling wedge pattern, a classic bullish reversal signal.
Should #ATA fail to bounce back from the $0.0820-$0.0700 support, our eyes will be on the next critical level at $0.0580. Historically, this level has been a stronghold, and the probability of a rebound here is notably higher.
But if $ATA breaks below these key support levels, the bears might take control, potentially leading to a bearish continuation.