10_open_source_LLMs

1: Overview of Open Source LLMs

Model	Creator	Sizes	Language Support	Token Limit
LLaMA 2	Meta	7B - 70B parameters	Mainly English	~2000 tokens
Mistral	Mistral AI	7B, 8x7B (MoE)	English, French, Italian, German, Spanish	32,000 tokens

2: Advantages and Disadvantages of Open Source vs Closed Source LLMs

Aspect	Open Source	Closed Source
Confidentiality	+ (can run locally)	- (data sent externally)
Transparency	+ (more open about training/methods)	- (less transparent)
Control	+ (full control)	- (limited control)
Cost	- (high fixed costs)	+ (pay-per-use model)
Performance	- (generally lower than top closed models)	+ (higher performance)
Maintenance	- (self-maintained)	+ (no maintenance required)
Operation	- (self-operated)	+ (managed service)

3: Using Open Source LLMs - Key Points

Aspect	Details
Prompt Formatting	Specific formatting required (e.g., XML tags for LLaMA 2)
Local Deployment	Can be run on CPU, even with limited resources
Model Versions	Different sizes available (e.g., 7B, 13B for LLaMA 2)
Implementation	Various libraries available (e.g., llama.cpp)
Performance Considerations	Larger models generally perform better but require more resources

4: Code Implementation (LLaMA 2 Example)

Step	Description
Import Libraries	Import necessary packages (e.g., llama.cpp)
Load Model	Specify model path and parameters
Format Prompt	Use correct prompt template (with XML tags)
Run Inference	Pass formatted prompt to the model
Process Output	Receive and process model's response