Change default model and quantization value for LLM example (#3100)

examples/llm-flowertune/README.md

@@ -50,11 +50,11 @@ With an activated Python environment, run the example with default config values
 python main.py
 ```
 
-This command will run FL simulations with an 8-bit [OpenLLaMA 3Bv2](https://huggingface.co/openlm-research/open_llama_3b_v2) model involving 2 clients per rounds for 100 FL rounds. You can override configuration parameters directly from the command line. Below are a few settings you might want to test:
+This command will run FL simulations with a 4-bit [OpenLLaMA 7Bv2](https://huggingface.co/openlm-research/open_llama_7b_v2) model involving 2 clients per rounds for 100 FL rounds. You can override configuration parameters directly from the command line. Below are a few settings you might want to test:
 
 ```bash
-# Use OpenLLaMA-7B instead of 3B and 4-bits quantization
-python main.py model.name="openlm-research/open_llama_7b_v2" model.quantization=4
+# Use OpenLLaMA-3B instead of 7B and 8-bits quantization
+python main.py model.name="openlm-research/open_llama_3b_v2" model.quantization=8
 
 # Run for 50 rounds but increasing the fraction of clients that participate per round to 25%
 python main.py num_rounds=50 fraction_fit.fraction_fit=0.25

examples/llm-flowertune/conf/config.yaml

@@ -8,8 +8,8 @@ dataset:
   name: "vicgalle/alpaca-gpt4"
 
 model:
-  name: "openlm-research/open_llama_3b_v2"
-  quantization: 8 # 8 or 4 if you want to do quantization with BitsAndBytes
+  name: "openlm-research/open_llama_7b_v2"
+  quantization: 4 # 8 or 4 if you want to do quantization with BitsAndBytes
   gradient_checkpointing: True
   lora:
     peft_lora_r: 32