update: charts for 1.5B model, CPU dockerfile

Dockerfile

@@ -4,24 +4,12 @@ ENV PYTHONUNBUFFERED=1
 ENV HF_HOME=/tmp/huggingface
 ENV HOME=/tmp
 
-RUN apt-get update && apt-get install -y git build-essential && rm -rf /var/lib/apt/lists/*
-
-RUN useradd -m -u 1000 trainer && \
-    mkdir -p /home/trainer/.local/lib/python3.10/site-packages/deceit_env/data/ && \
-    chown -R trainer:trainer /home/trainer
-
-USER trainer
-ENV HOME=/home/trainer
-ENV HF_HOME=/home/trainer/.cache/huggingface
-ENV TORCHINDUCTOR_CACHE_DIR=/home/trainer/.cache/torch
+RUN apt-get update && apt-get install -y git && rm -rf /var/lib/apt/lists/*
 
 WORKDIR /app
 
-RUN pip install --no-cache-dir torch transformers peft trl bitsandbytes accelerate wandb datasets huggingface_hub
-
-RUN pip install --no-cache-dir git+https://github.com/Jayant-kernel/DECEIT-the-ai-truth-environment-.git
+RUN pip install --no-cache-dir matplotlib Pillow huggingface_hub
 
-COPY data/ /home/trainer/.local/lib/python3.10/site-packages/deceit_env/data/
-COPY train.py .
+COPY generate_charts.py .
 
-CMD ["python", "train.py"]
+CMD ["python", "generate_charts.py"]

generate_charts.py

@@ -26,7 +26,7 @@ print("Health server started")
 login(token=os.environ["HF_TOKEN"])
 
 # Data
-models = ['Base Model\n(untrained)', 'DECEIT Trained']
+models = ['Base Model\n(untrained)', 'DECEIT 1.5B Trained']
 colors = ['#e74c3c', '#2ecc71']
 mean_rewards = [0.137, 0.130]
 accuracy = [50.0, 36.7]
@@ -56,7 +56,7 @@ axes[3].set_title('Abstain Rate %\n(Honest Uncertainty - higher is better)')
 axes[3].set_ylabel('%')
 axes[3].set_ylim(0, 100)
 
-plt.suptitle('DECEIT: Base Model vs Trained Model\n(Qwen 2.5 0.5B, 30 episodes each)', fontsize=13)
+plt.suptitle('DECEIT: Base Model vs Trained Model\n(Qwen 2.5 1.5B, 30 episodes each)', fontsize=13)
 plt.tight_layout()
 plt.savefig('comparison_chart.png', dpi=150, bbox_inches='tight')
 plt.close()