Merge branch 'main' into reset-ver-to-1.15.1

benchmarks/flowertune-llm/evaluation/code/README.md

@@ -12,7 +12,7 @@ Three datasets have been selected for this evaluation: [MBPP](https://huggingfac
 git clone --depth=1 https://github.com/adap/flower.git && mv flower/benchmarks/flowertune-llm/evaluation/code ./flowertune-eval-code && rm -rf flower && cd flowertune-eval-code
 ```
 
-Create a new Python environment (we recommend Python 3.10), activate it, then install dependencies with:
+Create a new Python environment (we recommend Python 3.11), activate it, then install dependencies with:
 
 ```shell
 # From a new python environment, run:
@@ -40,7 +40,7 @@ sudo apt-get install g++
 Then, download the `main.py` script from `bigcode-evaluation-harness` repository.
 
 ```shell
-git clone https://github.com/bigcode-project/bigcode-evaluation-harness.git && cd bigcode-evaluation-harness && git checkout 0f3e95f0806e78a4f432056cdb1be93604a51d69 && mv main.py ../ && cd .. && rm -rf bigcode-evaluation-harness
+git clone https://github.com/yan-gao-GY/bigcode-evaluation-harness.git && cd bigcode-evaluation-harness && mv main.py ../ && cd .. && rm -rf bigcode-evaluation-harness
 ```
 
 
@@ -51,7 +51,7 @@ git clone https://github.com/bigcode-project/bigcode-evaluation-harness.git && c
 
 ```bash
 python main.py \
---model=mistralai/Mistral-7B-v0.3 \
+--model=your-base-model-name \ # e.g., mistralai/Mistral-7B-v0.3
 --peft_model=/path/to/fine-tuned-peft-model-dir/  \ # e.g., ./peft_1
 --max_length_generation=1024  \ # change to 2048 when running mbpp
 --batch_size=4 \

benchmarks/flowertune-llm/evaluation/code/requirements.txt

@@ -1,8 +1,8 @@
-peft==0.6.2
+peft==0.14.0
 datasets==2.20.0
 evaluate==0.3.0
 sentencepiece==0.2.0
 protobuf==5.27.1
-bitsandbytes==0.45.0
+bitsandbytes==0.45.1
 hf_transfer==0.1.8
-git+https://github.com/bigcode-project/bigcode-evaluation-harness.git@6116c6a9a5672c69bd624373cfbc8938b7acc249
+git+https://github.com/yan-gao-GY/bigcode-evaluation-harness.git

benchmarks/flowertune-llm/evaluation/finance/README.md

@@ -10,7 +10,7 @@ Three datasets have been selected for this evaluation: [FPB](https://huggingface
 git clone --depth=1 https://github.com/adap/flower.git && mv flower/benchmarks/flowertune-llm/evaluation/finance ./flowertune-eval-finance && rm -rf flower && cd flowertune-eval-finance
 ```
 
-Create a new Python environment (we recommend Python 3.10), activate it, then install dependencies with:
+Create a new Python environment (we recommend Python 3.11), activate it, then install dependencies with:
 
 ```shell
 # From a new python environment, run:

benchmarks/flowertune-llm/evaluation/finance/requirements.txt

@@ -1,7 +1,7 @@
-peft==0.6.2
+peft==0.14.0
 scikit-learn==1.5.0
 datasets==2.20.0
 sentencepiece==0.2.0
 protobuf==5.27.1
-bitsandbytes==0.45.0
+bitsandbytes==0.45.1
 hf_transfer==0.1.8

benchmarks/flowertune-llm/evaluation/general-nlp/README.md

@@ -10,7 +10,7 @@ The [MMLU](https://huggingface.co/datasets/lukaemon/mmlu) dataset is used for th
 git clone --depth=1 https://github.com/adap/flower.git && mv flower/benchmarks/flowertune-llm/evaluation/general-nlp ./flowertune-eval-general-nlp && rm -rf flower && cd flowertune-eval-general-nlp
 ```
 
-Create a new Python environment (we recommend Python 3.10), activate it, then install dependencies with:
+Create a new Python environment (we recommend Python 3.11), activate it, then install dependencies with:
 
 ```shell
 # From a new python environment, run:

benchmarks/flowertune-llm/evaluation/general-nlp/requirements.txt

@@ -1,8 +1,8 @@
-peft==0.6.2
+peft==0.14.0
 pandas==2.2.2
 scikit-learn==1.5.0
 datasets==2.20.0
 sentencepiece==0.2.0
 protobuf==5.27.1
-bitsandbytes==0.45.0
+bitsandbytes==0.45.1
 hf_transfer==0.1.8

benchmarks/flowertune-llm/evaluation/medical/README.md

@@ -10,7 +10,7 @@ Three datasets have been selected for this evaluation: [PubMedQA](https://huggin
 git clone --depth=1 https://github.com/adap/flower.git && mv flower/benchmarks/flowertune-llm/evaluation/medical ./flowertune-eval-medical && rm -rf flower && cd flowertune-eval-medical
 ```
 
-Create a new Python environment (we recommend Python 3.10), activate it, then install dependencies with:
+Create a new Python environment (we recommend Python 3.11), activate it, then install dependencies with:
 
 ```shell
 # From a new python environment, run:

benchmarks/flowertune-llm/evaluation/medical/requirements.txt

@@ -1,8 +1,8 @@
-peft==0.6.2
+peft==0.14.0
 pandas==2.2.2
 scikit-learn==1.5.0
 datasets==2.20.0
 sentencepiece==0.2.0
 protobuf==5.27.1
-bitsandbytes==0.45.0
+bitsandbytes==0.45.1
 hf_transfer==0.1.8

framework/docs/source/how-to-authenticate-supernodes.rst

@@ -5,12 +5,22 @@
 Authenticate SuperNodes
 =======================
 
-Flower has built-in support for authenticated SuperNodes, allowing you to verify the
-identity of each SuperNode connecting to a SuperLink. To enhance security, node
-authentication is only available when encrypted connections (SSL/TLS) are enabled.
-
-Flower's node authentication leverages a signature-based mechanism to verify each node's
-identity:
+When running a Flower Federation (see :doc:`ref-flower-network-communication`) it is
+fundamental that an authentication mechanism is available between the SuperLink and the
+SuperNodes that connect to it. Flower comes with two different mechanisms to
+authenticate SuperNodes that connect to a running SuperLink:
+
+- **Automatic authentication**: In this mode, the SuperLink checks the timestamp-based
+  signature in each request from SuperNodes to prevent impersonation and replay attacks.
+- **CSV-based authentication**: This mode functions similarly to automatic
+  authentication but requires the SuperLink to be provided with a list of authorized
+  public keys, allowing only those SuperNodes to connect.
+
+The automatic authentication mode works out of the box and therefore requires no
+configuration. On the other hand, CSV-based authentication mode is more sophisticated
+and how it works and how it can be used is presented reminder of this guide. Flower's
+CSV-based node authentication leverages a signature-based mechanism to verify each
+node's identity and is only available when encrypted connections (SSL/TLS) are enabled:
 
 - Each SuperNode must already possess a unique Elliptic Curve (EC) public/private key
   pair.