Update README.md

README.md

@@ -34,36 +34,24 @@ This guide shows a new user how to:
 Install the basics:
 
 ```bash
-pip install -U torch huggingface_hub
-# optional speed up for large files
-pip install -U hf_transfer
-```
-
-If you prefer conda:
-
-```bash
-# example only - adjust paths to your cluster
+# Install core PyTorch and Transformers ecosystem
+pip install torch
+pip install transformers
+pip install esm
 
-conda create -y -n ESMCBA python=3.10
-conda activate ESMCBA
+# Install Hugging Face Hub utilities
+pip install "huggingface-hub<1.0"
 
-# Core dependencies
-pip install torch==2.6.0 transformers==4.46.3 esm==3.1.3 \
-            biopython==1.85 umap-learn==0.5.7 scikit-learn==1.6.1 \
-            seaborn==0.13.2 pandas==2.2.3 matplotlib==3.10.1
+# Optional: Install hf_transfer for faster large file downloads
+pip install hf_transfer
 
-# For downloading model checkpoints from Hugging Face
-pip install -U huggingface_hub
-
-# Optional: speed up large file downloads
-pip install -U hf_transfer
+pip install biopython umap-learn scikit-learn seaborn pandas matplotlib
 ```
 
 ## 2. Get the code
 
 ```bash
 git clone https://github.com/sermare/ESMCBA
-cd ESMCBA
 ```
 
 Inside the repo you should have `embeddings.py` available. If your file is named `embeddings_generation.py`, use that name instead in the commands below.
@@ -88,8 +76,12 @@ You can browse all files here: https://huggingface.co/smares/ESMCBA
 # download everything to ./models
 hf download smares/ESMCBA --repo-type model --local-dir ./models
 
-# or download a single file to ./models
-hf download smares/ESMCBA   "ESMCBA_epitope_0.5_20_ESMMASK_epitope_FT_15_0.0001_1e-05_AUG_6_HLAB5101_5_0.001_1e-06__3_B5101_Hubber_B5101_final.pth"   --repo-type model --local-dir ./models
+#or just get one model
+huggingface-cli download smares/ESMCBA \
+  "ESMCBA_epitope_0.8_30_ESMMASK_epitope_FT_5_0.001_1e-06_AUG_6_HLAA0201_2_0.001_1e-06__2_A0201_Hubber_A0201_final.pth" \
+  --repo-type model \
+  --local-dir ./models
+
 ```
 
 ### Option B — rely on the Hugging Face cache
@@ -121,6 +113,8 @@ python3 embeddings_generation.py   --model_path ./models/ESMCBA_epitope_0.5_20_E
 If `embeddings_generation.py` supports resolving from the Hub, you can pass either a file name or an `hf://` path and let the script download to cache.
 
 ```bash
+cd ESMCBA/ESMCBA
+
 python3 embeddings_generation.py   --model_path "ESMCBA_epitope_0.95_30_ESMMASK_epitope_FT_25_0.001_5e-05_AUG_3_HLAB1402_2_1e-05_1e-06__1_B1402_0404_Hubber_B1402_final.pth"   --name B1402-ESMCBA   --hla B1402   --encoding epitope   --output_dir ./outputs --peptides ASCQQQRAGHS ASCQQQRAGH ASCQQQRAG DVRLSAHHHR DVRLSAHHHRM GHSDVRLSAHH
 ```