diff --git a/orby/scripts/eval_only_screenspot.sh b/orby/scripts/eval_only_screenspot.sh
new file mode 100644
index 00000000000..8dc94c116f2
--- /dev/null
+++ b/orby/scripts/eval_only_screenspot.sh
@@ -0,0 +1,21 @@
+set -e
+
+# Default values
+DATASET_VERSION="screenspot"
+MODEL_PATH=Qwen/Qwen2.5-VL-7B-Instruct
+REWARD_FILE=orby/reward/screenspot.py
+REWARD_FN=reward_func
+OUTPUT_FILE=responses.parquet
+PROMPT_FORMAT="qwen"
+DATA_PATH=~/data/$DATASET_VERSION/
+
+echo "Using dataset version: $DATASET_VERSION"
+echo "Data path: $DATA_PATH"
+
+# Evaluation
+python3 -m orby.trainer.main_eval \
+    data.path=$DATA_PATH/$OUTPUT_FILE \
+    data.prompt_key=prompt \
+    data.response_key=responses \
+    custom_reward_function.path=$REWARD_FILE \
+    custom_reward_function.name=$REWARD_FN
diff --git a/orby/scripts/init_interactive.sh b/orby/scripts/init_interactive.sh
index eb07705e120..184c58d191b 100644
--- a/orby/scripts/init_interactive.sh
+++ b/orby/scripts/init_interactive.sh
@@ -18,11 +18,41 @@ apt install -y awscli
 pip install 'urllib3<2'
 pip install parquet-tools
 
-# Download model.
+# Install Miniconda
+wget https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh
+bash Miniconda3-latest-Linux-x86_64.sh -b -p $HOME/miniconda
+source $HOME/miniconda/bin/activate
+
+# Create and setup conda environment
+conda create -n verl python=3.12 -y
+conda activate verl
+
+# Clone VERL repository
+git clone git@github.com:orby-ai-engineering/verl.git && cd verl
+# Initialize git submodules if needed
+git submodule update --init --recursive
+# Install dependencies
+pip install -e .[vllm]
+
+# Handle Flash Attention installation
+pip uninstall -y flash-attn
+FLASH_ATTENTION_FORCE_BUILD=TRUE pip install flash-attn --no-build-isolation
+
+# Install additional utilities
+pip install qwen_vl_utils
+pip install qwen_agent
+pip install hf_transfer
+
+# Download model, verify transformers installation
 python3 -c "import transformers; transformers.pipeline(model='Qwen/Qwen2.5-VL-7B-Instruct')"
 
-# Install verl lib: https://verl.readthedocs.io/en/latest/start/install.html
-pip3 install -e .[vllm]
+# Install internal packages
+conda deactivate
+conda create -n digital-agent python=3.12 -y
+git clone git@github.com:orby-ai-engineering/digital-agent.git & cd digital-agent
+git submodule update --init --recursive
+pip install -r requirements.txt
+
 
 # Download and convert action description dev set
 # mkdir -p ~/data/action_description/raw/
diff --git a/orby/trainer/vllm_generation.py b/orby/trainer/vllm_generation.py
new file mode 100644
index 00000000000..94b9eb0b9ab
--- /dev/null
+++ b/orby/trainer/vllm_generation.py
@@ -0,0 +1,143 @@
+import os
+import json
+import torch
+from typing import List, Dict, Any
+from openai import OpenAI
+from dataclasses import dataclass
+from pathlib import Path
+from tqdm import tqdm
+import pandas as pd
+import base64
+from io import BytesIO
+import ray
+from ray.util.multiprocessing import Pool
+
+@dataclass
+class Batch:
+    """Class to hold a batch of data"""
+    prompts: List[str]
+    ground_truth: List[Dict[str, Any]]
+
+class DataLoader:
+    """Data loader for loading and batching data"""
+    def __init__(self, data_path: str, batch_size: int = 128):
+        self.data_path = Path(data_path)
+        self.batch_size = batch_size
+        self.data = self._load_data()
+        
+    def _load_data(self) -> pd.DataFrame:
+        """Load data from the parquet file"""
+        return pd.read_parquet(self.data_path)
+    
+    def _create_chat_messages(self, row: pd.Series) -> str:
+        """Create chat template for the prompt"""
+        prompt = row['prompt']
+
+        # Convert image bytes to base64 encoding
+        if len(row['images']) != 1:
+            raise ValueError("More than 1 image")
+        image_bytes = BytesIO(row['images'][0]['bytes']).getvalue()
+        base64_image = base64.b64encode(image_bytes).decode('utf-8')
+        messages = [
+            {
+                "role": "system",
+                "content": [
+                    {
+                        "type": "text",
+                        "text": prompt[0]["content"]
+                    }
+                ]    
+            },
+            {
+                "role": "user",
+                "content": [
+                    {
+                        "type": "text",
+                        "text": prompt[1]["content"]
+                    },
+                    {
+                        "type": "image_url",
+                        "image_url": {
+                            "url": f"data:image/png;base64,{base64_image}"
+                        }
+                    }
+                ]
+            }
+        ]
+        
+        return messages
+    
+    def get_batches(self):
+        """Generate batches of data"""
+        for i in range(0, len(self.data), self.batch_size):
+            batch_data = self.data.iloc[i:i + self.batch_size]
+            prompts = [self._create_chat_messages(row) for _, row in batch_data.iterrows()]
+            ground_truth = batch_data['reward_model'].tolist()
+            yield Batch(prompts=prompts, ground_truth=ground_truth)
+
+@ray.remote
+class VLLMClient:
+    """Client for interacting with VLLM server"""
+    def __init__(self, server_url: str):
+        self.client = OpenAI(
+            base_url=server_url,
+            api_key="not-needed"  # VLLM server doesn't require API key
+        )
+        
+    def generate(self, prompt: Any) -> str:
+        """Generate response for a single prompt"""
+        completion = self.client.chat.completions.create(
+            model="qwen25vl7b-9",  # Model name not needed as it's configured on server
+            messages=prompt,
+            temperature=0,
+            # max_tokens=2048,
+            top_p=1.0,
+            # frequency_penalty=0.0,
+            # presence_penalty=0.0
+        )
+        return [completion.choices[0].message.content]
+
+def process_batch(batch: Batch, client_pool: List[Any]) -> List[str]:
+    """Process a batch of prompts using the existing client pool"""
+    # Generate responses in parallel using the client pool
+    futures = [client.generate.remote(prompt) for client, prompt in zip(client_pool, batch.prompts)]
+    responses = ray.get(futures)
+    
+    return responses
+
+def main():
+    # Initialize Ray
+    ray.init()
+    
+    # Configuration
+    data_path = "~/data/screenspot/test.parquet"  # Replace with your data path
+    server_url = "http://model.orbyapi.com/v1"
+    batch_size = 16
+    output_file = os.path.join(os.path.dirname(data_path), "responses.parquet")
+    
+    # Create a pool of VLLM clients
+    client_pool = [VLLMClient.remote(server_url) for _ in range(batch_size)]
+
+    # Initialize components
+    data_loader = DataLoader(data_path, batch_size)
+    
+    # Process batches and collect responses
+    all_responses = []
+    
+    for batch in tqdm(data_loader.get_batches(), desc="Processing batches"):
+        # Generate responses from VLLM server in parallel using the client pool
+        responses = process_batch(batch, client_pool)
+        all_responses.extend(responses)
+   
+    # Add responses to the original dataset
+    data_loader.data['responses'] = all_responses
+    
+    # Save the updated dataset
+    data_loader.data.to_parquet(output_file, index=False)
+    print(f"Saved dataset with {len(all_responses)} responses to {output_file}")
+    
+    # Shutdown Ray
+    ray.shutdown()
+
+if __name__ == "__main__":
+    main()
diff --git a/orby/trainer/vllm_generation_without_ray.py b/orby/trainer/vllm_generation_without_ray.py
new file mode 100644
index 00000000000..02d4635e6f5
--- /dev/null
+++ b/orby/trainer/vllm_generation_without_ray.py
@@ -0,0 +1,128 @@
+import os
+import json
+import torch
+from typing import List, Dict, Any
+from openai import OpenAI
+from dataclasses import dataclass
+from pathlib import Path
+from tqdm import tqdm
+import pandas as pd
+import base64
+from io import BytesIO
+
+@dataclass
+class Batch:
+    """Class to hold a batch of data"""
+    prompts: List[str]
+    ground_truth: List[Dict[str, Any]]
+
+class DataLoader:
+    """Data loader for loading and batching data"""
+    def __init__(self, data_path: str, batch_size: int = 128):
+        self.data_path = Path(data_path)
+        self.batch_size = batch_size
+        self.data = self._load_data()
+        
+    def _load_data(self) -> pd.DataFrame:
+        """Load data from the parquet file"""
+        return pd.read_parquet(self.data_path)
+    
+    def _create_chat_messages(self, row: pd.Series) -> str:
+        """Create chat template for the prompt"""
+        prompt = row['prompt']
+
+        # Convert image bytes to base64 encoding
+        if len(row['images']) != 1:
+            raise ValueError("More than 1 image")
+        image_bytes = BytesIO(row['images'][0]['bytes']).getvalue()
+        base64_image = base64.b64encode(image_bytes).decode('utf-8')
+        messages = [
+            {
+                "role": "system",
+                "content": [
+                    {
+                        "type": "text",
+                        "text": prompt[0]["content"]
+                    }
+                ]    
+            },
+            {
+                "role": "user",
+                "content": [
+                    {
+                        "type": "text",
+                        "text": prompt[1]["content"].replace("<image>", "")
+                    },
+                    {
+                        "type": "image_url",
+                        "image_url": {
+                            "url": f"data:image/png;base64,{base64_image}"
+                        }
+                    }
+                ]
+            }
+        ]
+        
+        return messages
+    
+    def get_batches(self):
+        """Generate batches of data"""
+        for i in range(0, len(self.data), self.batch_size):
+            batch_data = self.data.iloc[i:i + self.batch_size]
+            prompts = [self._create_chat_messages(row) for _, row in batch_data.iterrows()]
+            ground_truth = batch_data['reward_model'].tolist()
+            yield Batch(prompts=prompts, ground_truth=ground_truth)
+
+class VLLMClient:
+    """Client for interacting with VLLM server"""
+    def __init__(self, server_url: str):
+        self.client = OpenAI(
+            base_url=server_url,
+            api_key="not-needed"  # VLLM server doesn't require API key
+        )
+        
+    def generate(self, prompts: List[Any]) -> List[str]:
+        """Generate responses for a batch of prompts"""
+        completion = self.client.chat.completions.create(
+            model="qwen25vl7b-9",  # Model name not needed as it's configured on server
+            messages=prompts,
+            temperature=0,
+            top_p=1.0,
+        )
+        return [choice.message.content for choice in completion.choices]
+
+def process_batch(batch: Batch, client: VLLMClient) -> List[str]:
+    """Process a batch of prompts using a single client"""
+    responses = client.generate(batch.prompts)
+    return responses
+
+def main():
+    # Configuration
+    data_path = "~/data/screenspot/test.parquet"  # Replace with your data path
+    server_url = "http://model.orbyapi.com/v1"
+    batch_size = 16
+    output_file = os.path.join(os.path.dirname(data_path), "responses.parquet")
+    
+    # Create a single VLLM client
+    client = VLLMClient(server_url)
+
+    # Initialize components
+    data_loader = DataLoader(data_path, batch_size)
+    
+    # Process batches and collect responses
+    all_responses = []
+    
+    for batch in tqdm(data_loader.get_batches(), desc="Processing batches"):
+        # Generate responses from VLLM server sequentially
+        responses = process_batch(batch, client)
+        all_responses.extend(responses)
+   
+    # Add responses to the original dataset
+    data_loader.data['responses'] = all_responses
+    
+    # Save the updated dataset
+    data_loader.data.to_parquet(output_file, index=False)
+    print(f"Saved dataset with {len(all_responses)} responses to {output_file}")
+
+if __name__ == "__main__":
+    main() 
\ No newline at end of file