diff --git a/configs/cot_eval_api.json b/configs/cot_eval_api.json
new file mode 100644
index 0000000..129d89e
--- /dev/null
+++ b/configs/cot_eval_api.json
@@ -0,0 +1,12 @@
+{
+    "job_type": "cot_eval_api",
+    "dataset": {
+      "input_path": "cot_input.json",
+      "output_path": "cot_output.json"
+    },
+    "inference":{
+      "base_url": "ENDPOINT",
+      "api_key": "TOKEN",
+      "max_new_tokens": 8196
+    }
+}
\ No newline at end of file
diff --git a/configs/instruct_eval_api.json b/configs/instruct_eval_api.json
new file mode 100644
index 0000000..44121d0
--- /dev/null
+++ b/configs/instruct_eval_api.json
@@ -0,0 +1,12 @@
+{
+    "job_type": "instruct_eval_api",
+    "dataset": {
+      "input_path": "instruct_input.json",
+      "output_path": "instruct_output.json"
+    },
+    "inference":{
+      "base_url": "ENDPOINT",
+      "api_key": "TOKEN",
+      "max_new_tokens": 8196
+    }
+}
\ No newline at end of file
diff --git a/configs/mmkd_black_box_api.json b/configs/mmkd_black_box_api.json
new file mode 100644
index 0000000..43bf9d9
--- /dev/null
+++ b/configs/mmkd_black_box_api.json
@@ -0,0 +1,30 @@
+{
+  "job_type": "mmkd_black_box_api",
+  "dataset": {
+    "instruction_path": "train.json",
+    "labeled_path": "train_labeled.json",
+    "seed": 42
+  },
+  "inference":{
+    "base_url": "ENDPOINT",
+    "api_key": "TOKEN",
+    "system_prompt" : "You are a helpful assistant.",
+    "max_new_tokens": 512
+  },
+  "models": {
+    "student": "student/Qwen/Qwen2.5-VL-3B-Instruct/"
+  },
+  "training": {
+    "output_dir": "./result/",
+    "num_train_epochs": 3,
+    "per_device_train_batch_size": 1,
+    "gradient_accumulation_steps": 8,
+    "max_length": 512,
+    "save_steps": 1000,
+    "logging_steps": 1,
+    "learning_rate": 2e-5,
+    "weight_decay": 0.05,
+    "warmup_ratio": 0.1,
+    "lr_scheduler_type": "cosine"
+  }
+}
\ No newline at end of file
diff --git a/easydistill/eval/data_eval.py b/easydistill/eval/data_eval.py
new file mode 100644
index 0000000..b16d4ff
--- /dev/null
+++ b/easydistill/eval/data_eval.py
@@ -0,0 +1,268 @@
+
+# Copyright 2024 Alibaba Group Holding Limited. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+
+import json
+import argparse
+import logging
+import os
+import re
+from tqdm import tqdm
+from openai import OpenAI
+
+
+logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')
+
+
+def build_cot_prompts(instruction, output):
+    rv_prompt_template = (
+        "You are an expert judge tasked with evaluating the Reasoning Verbosity of a Chain-of-Thought (CoT) "
+        "for a given problem and its answer. Reasoning Verbosity Evaluation Focus: Assess how well the CoT’s "
+        "length and step complexity match the problem’s inherent difficulty. An optimal chain is neither "
+        "missing essential steps nor padded with needless digressions. A simple question should be solved "
+        "with a brief, direct chain; a challenging one may justifiably require a longer path with reflection "
+        "and error-checking. Scoring Guidelines (0-9):\n"
+        "0-1 Minimal verbosity, straightforward expression with little to no elaboration.\n"
+        "2-3 Clear and concise reasoning with necessary explanations.\n"
+        "4-5 Moderate verbosity with detailed explanations and thorough reasoning.\n"
+        "6-7 Extensive verbosity with comprehensive justification and exploration of complex connections.\n"
+        "8-9 High verbosity with deep, exhaustive exploration of reasoning; involves extensive elaboration, nested justifications, "
+        "and consideration of counterarguments or alternative perspectives.\n"
+        "Given Problem, Answer with hain-of-Thought, you will:\n"
+        "1. Analyze the Reasoning Verbosity\n"
+        "2. Determine score using the above criteria\n"
+        "3. Output ONLY the integer score (0-9), place your score in <score></score>\n"
+        f"Problem: {instruction}\n"
+        f"Answer with Chain-of-Thought: {output}"
+    )
+    cd_prompt_template = (
+        "You are an expert judge assessing the Cognitive Difficulty of a Chain-of-Thought (CoT) "
+        "for a given problem and its answer. Cognitive Difficulty Evaluation Focus: The level of "
+        "reasoning competence required for a model to follow and reproduce the chain faithfully. "
+        "Judge the reasoning approach, techniques, and overall difficulty. Higher scores correspond "
+        "to more advanced concepts, abstractions, or multi-layer reasoning patterns. "
+        "Scoring Guidelines (0-9):\n"
+        "0-1 Elementary facts or a single trivial operation.\n"
+        "2-3 Multi-step arithmetic, explicit enumeration, basic rule chaining.\n"
+        "4-5 Early-undergraduate logic/algebra; one non-obvious insight.\n"
+        "6-7 Advanced undergraduate techniques (determinants, dynamic programming, layered code reasoning, etc).\n"
+        "8-9 Graduate-level abstraction, nested proofs, intricate algorithmic analysis.\n"
+        "Given Problem, Answer with hain-of-Thought, you will:\n"
+        "1. Analyze the Cognitive Difficulty\n"
+        "2. Determine score using the above criteria\n"
+        "3. Output ONLY the integer score (0-9), place your score in <score></score>\n"
+        f"Problem: {instruction}\n"
+        f"Answer with Chain-of-Thought: {output}"
+    )
+    lc_prompt_template = (
+        "You are a rigorous logical validator analyzing problem-solving components. "
+        "Your task is to separately assess the validity of the reasoning process and final solution. "
+        "Given Problem, Answer with hain-of-Thought, you will:\n"
+        "1. Verify stepwise logical coherence and soundness\n"
+        "2. Confirm all critical problem constraints are properly addressed\n"
+        "3. Check for self-contradictions or unsupported leaps in logic\n"
+        "4. Verify the process can actually derive the proposed solution\n"
+        "5. Output ONLY the 1/0 answer (1 for true, 0 for false) for logical correctness, place your answer in <score></score>\n"
+        f"Problem: {instruction}\n"
+        f"Answer with Chain-of-Thought: {output}"    
+    )
+    return rv_prompt_template, cd_prompt_template, lc_prompt_template
+
+
+def build_instruct_prompts(instruction, output):
+    informativeness_template = (
+        "You are an expert judge tasked with evaluating the Informativeness of a response generated by an instruction-following model "
+        "for a given user instruction. Informativeness Evaluation Focus: Assess how thoroughly and accurately the response addresses "
+        "the user’s instruction, providing relevant details, facts, and explanations without omissions or irrelevant additions. "
+        "An informative response fully satisfies the query with meaningful content, whereas a less informative one may be vague, "
+        "incomplete, or superficial. Scoring Guidelines (0-9):\n"
+        "0-1 Very low informativeness; the response is irrelevant or nearly empty.\n"
+        "2-3 Low informativeness; addresses the instruction minimally with significant missing information.\n"
+        "4-5 Moderate informativeness; covers some key points but lacks depth or completeness.\n"
+        "6-7 High informativeness; provides detailed and mostly comprehensive information relevant to the instruction.\n"
+        "8-9 Exceptional informativeness; thoroughly and accurately covers all relevant aspects with rich and precise details.\n"
+        "Given Instruction and Model Response, you will:\n"
+        "1. Analyze the Informativeness of the response\n"
+        "2. Determine a score using the above criteria\n"
+        "3. Output ONLY the integer score (0-9), place your score in <score></score>\n"
+        f"Instruction: {instruction}\n"
+        f"Response: {output}"
+    )
+    helpfulness_template = (
+        "You are an expert judge tasked with evaluating the Helpfulness of a response generated by an instruction-following model "
+        "for a given user instruction. Helpfulness Evaluation Focus: Assess how well the response assists the user in accomplishing  "
+        "their goal, providing clear, actionable, and relevant information or guidance. A helpful response should be easy "
+        "to understand and effectively address the user’s needs without unnecessary confusion or missing key details.\n"
+        "Scoring Guidelines (0-9):\n"
+        "0-1 Not helpful; response is irrelevant, confusing, or fails to address the instruction.\n"
+        "2-3 Slightly helpful; responds partially but lacks clarity or important elements.\n"
+        "4-5 Moderately helpful; response addresses the instruction but may be incomplete or somewhat unclear.\n"
+        "6-7 Mostly helpful; provides clear and relevant information that adequately assists the user.\n"
+        "8-9 Extremely helpful; offers comprehensive, clear, and precise guidance or information that fully satisfies the user’s instruction.\n"
+        "Given Instruction and Model Response, you will:\n"
+        "1. Analyze the Helpfulness of the response\n"
+        "2. Determine a score using the above criteria\n"
+        "3. Output ONLY the integer score (0-9), place your score in <score></score>\n"
+        f"Instruction: {instruction}\n"
+        f"Response: {output}"
+    )
+    generalization_template = (
+        "You are an expert judge tasked with evaluating the Potential for Generalization of a response generated by an "
+        "instruction-following model to similar but unseen tasks. Generalization Evaluation Focus: Assess how well the response "
+        "demonstrates understanding and reasoning that can be effectively adapted or transferred to other related instructions or "
+        "problems beyond the specific input. A response with high generalization ability "
+        "captures underlying principles or strategies rather than relying on shallow, task-specific heuristics.\n"
+        "Scoring Guidelines (0-9):\n"
+        "0-1 Very poor generalization; response is overly specific, rigid, or fails to show adaptable reasoning.\n"
+        "2-3 Limited generalization; response applies partly to related tasks but is mostly narrow or shallow.\n"
+        "4-5 Moderate generalization; response reflects some transferable understanding but may lack depth or clarity.\n"
+        "6-7 Strong generalization; response shows clear reasoning patterns or concepts that can extend to similar tasks.\n"
+        "8-9 Exceptional generalization; response exhibits deep, abstract, and flexible comprehension applicable across a broad range of related instructions.\n"
+        "Given Instruction and Model Response, you will:\n"
+        "1. Analyze the Potential for Generalization to Similar Tasks\n"
+        "2. Determine a score using the above criteria\n"
+        "3. Output ONLY the integer score (0-9), place your score in <score></score>\n"
+        f"Instruction: {instruction}\n"
+        f"Response: {output}"
+    )
+    correctness_template = (
+        "You are a meticulous correctness evaluator tasked with assessing whether the response to a user instruction "
+        "is factually accurate and logically sound.\n"
+        "Your evaluation should determine:\n"
+        "1. Whether the response correctly addresses the instruction\n"
+        "2. Whether any factual claims or data are accurate\n"
+        "3. Whether the reasoning, if present, is logically valid and free of errors\n"
+        "4. Whether the final answer is consistent with the evidence or instructions provided\n"
+        "You will:\n"
+        "Output ONLY '1' if the response is correct and accurate, or '0' if it contains factual errors, logical flaws, "
+        "or fails to correctly address the instruction.\n"
+        "Place your answer in <score></score> tags.\n"
+        f"Instruction: {instruction}\n"
+        f"Response: {output}"
+    )
+    return informativeness_template, helpfulness_template, generalization_template, correctness_template
+
+
+
+def extract_score(text):
+    match = re.search(r"<score>(\d+)</score>", text)
+    if match:
+        return int(match.group(1))
+    else:
+        return -1
+
+
+def read_json_fields(filename):
+    try:
+        with open(filename, 'r') as file:
+            data = json.load(file)
+        return data
+    except FileNotFoundError:
+        logging.error("The file was not found.")
+    except json.JSONDecodeError:
+        logging.error("There was an error decoding the JSON file.")
+    except Exception as e:
+        logging.error(f"An error occurred: {e}")
+
+
+def write_data_to_json_file(data, file_path):
+    try:
+        with open(file_path, 'w') as file:
+            json.dump(data, file, ensure_ascii=False, indent=4)
+        logging.info(f"Data successfully written to {file_path}")
+    except Exception as e:
+        logging.error(f"An error occurred: {e}")
+
+        
+def generate_teacher_response_api(data_list, config, is_cot_model):
+    client = OpenAI(
+        api_key = config["inference"]["api_key"],
+        base_url = config["inference"]["base_url"]
+    )
+    models = client.models.list()
+    model = models.data[0].id
+    logging.info(model)
+    outcomes = []
+    for sample in tqdm(data_list, desc="Call remote model and generating responses"):
+        instruction = sample["instruction"]
+        output = sample["output"]
+        
+        def generate_score(sample, model, config):
+            message = [
+                {'role': 'user', 'content': sample}
+            ]
+            completion = client.chat.completions.create(
+                messages = message,
+                model = model,
+                max_completion_tokens = config["inference"]["max_new_tokens"]
+            )
+            result = completion.choices[0].message.content
+            score = extract_score(result)
+            return score
+            
+        if is_cot_model:
+            rv_prompt_template, cd_prompt_template, lc_prompt_template = build_cot_prompts(instruction, output)
+            rv_score = generate_score(rv_prompt_template, model, config)
+            cd_score = generate_score(cd_prompt_template, model, config)
+            lc_score = generate_score(lc_prompt_template, model, config)
+            lc_score = (lc_score == 1)
+            outcomes.append(
+                {
+                    'instruction': instruction,
+                     'output': output,
+                     "reasoning_verbosity": rv_score,
+                     "cognitive_difficulty": cd_score,
+                     "logical_correctness": lc_score
+                }
+            )
+        else:
+            informativeness_temp, helpfulness_temp, generalization_temp, correctness_temp = build_instruct_prompts(instruction, output)
+            informativeness = generate_score(informativeness_temp, model, config)
+            helpfulness = generate_score(helpfulness_temp, model, config)
+            generalization = generate_score(generalization_temp, model, config)
+            correctness = generate_score(correctness_temp, model, config)
+            correctness = (correctness == 1)
+            outcomes.append(
+                {
+                    'instruction': instruction,
+                     'output': output,
+                     "informativeness": informativeness,
+                     "helpfulness": helpfulness,
+                     "generalization": generalization,
+                    "correctness": correctness
+                }
+            )
+            
+    write_data_to_json_file(outcomes, config["dataset"]["output_path"])
+
+
+def infer_with_teacher_model(config):
+    logging.info('Generating distillation data from the teacher model!')
+    data_list = read_json_fields(config["dataset"]["input_path"])
+    job_type =  config["job_type"]
+    is_cot_model = "cot" in job_type
+    generate_teacher_response_api(data_list, config, is_cot_model)
+
+        
+def main():
+    parser = argparse.ArgumentParser()
+    parser.add_argument('--config', type=str, required=True, help='path to the json config file')
+    args = parser.parse_args()
+    config = json.load(open(args.config))
+    infer_with_teacher_model(config)
+
+
+if __name__ == "__main__":
+    main()
\ No newline at end of file
diff --git a/easydistill/mmkd/infer.py b/easydistill/mmkd/infer.py
new file mode 100644
index 0000000..65317bf
--- /dev/null
+++ b/easydistill/mmkd/infer.py
@@ -0,0 +1,122 @@
+
+# Copyright 2024 Alibaba Group Holding Limited. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+
+import json
+import argparse
+import logging
+from tqdm import tqdm
+from openai import OpenAI
+
+
+logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')
+
+
+def read_json_field(filename):
+    try:
+        with open(filename, 'r') as file:
+            data = json.load(file)
+        outputs = []
+        for item in data:
+            text = item["instruction"]
+            image = item["image"]
+            outputs.append((text, image))
+        return outputs
+    except FileNotFoundError:
+        logging.error("The file was not found.")
+    except json.JSONDecodeError:
+        logging.error("There was an error decoding the JSON file.")
+    except Exception as e:
+        logging.error(f"An error occurred: {e}")
+
+
+def write_data_to_json_file(data, file_path):
+    try:
+        with open(file_path, 'w') as file:
+            json.dump(data, file, ensure_ascii=False, indent=4)
+        logging.info(f"Data successfully written to {file_path}")
+    except Exception as e:
+        logging.error(f"An error occurred: {e}")
+
+
+def generate_teacher_response_api(data_list, config):
+    client = OpenAI(
+        api_key = config["inference"]["api_key"],
+        base_url = config["inference"]["base_url"]
+    )
+    models = client.models.list()
+    model = models.data[0].id
+    logging.info(model)
+    system_prompt = config["inference"]["system_prompt"]
+    if system_prompt == "":
+        system_prompt = "You are a helpful assistant."
+    outcomes = []
+    for text, image in tqdm(data_list, desc="Call remote model and generating responses"):
+        messages = [
+            {
+                "role": "system",
+                "content": system_prompt
+            },
+            {
+                "role": "user",
+                "content": [
+                    {
+                        "type": "image_url",
+                        "image_url": {
+                            "url": image
+                        },
+                    },
+                    {
+                        "type": "text",
+                        "text": text
+                    }
+                ]
+            }
+        ]
+        completion = client.chat.completions.create(
+            messages = messages,
+            model = model,
+            max_completion_tokens = config["inference"]["max_new_tokens"]
+        )
+        result = completion.choices[0].message.content
+        outcomes.append({'instruction': text, 'image': image, 'output': result})
+    write_data_to_json_file(outcomes, config["dataset"]["labeled_path"])
+
+
+def infer_with_teacher_model(config):
+    logging.info('Generating distillation data from the teacher model!')
+    data_list = read_json_field(config["dataset"]["instruction_path"])
+    try:
+        job_type =  config["job_type"]
+        if job_type == "mmkd_black_box_api":
+            generate_teacher_response_api(data_list, config)
+        else:
+            logging.error(f"Invalid job type: {job_type}")
+            raise ValueError(f"Invalid job type: {job_type}")
+    except ValueError as e:
+        logging.error(f"Training job terminated: {e}")
+        return
+
+        
+def main():
+    parser = argparse.ArgumentParser()
+    parser.add_argument('--config', type=str, required=True, help='path to the json config file')
+    args = parser.parse_args()
+    config = json.load(open(args.config))
+    infer_with_teacher_model(config)
+
+
+if __name__ == "__main__":
+    main()
\ No newline at end of file
diff --git a/easydistill/mmkd/train.py b/easydistill/mmkd/train.py
new file mode 100644
index 0000000..3a1d604
--- /dev/null
+++ b/easydistill/mmkd/train.py
@@ -0,0 +1,105 @@
+
+# Copyright 2024 Alibaba Group Holding Limited. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+
+import json
+import argparse
+import logging
+from datasets import load_dataset, Dataset
+from transformers import Qwen2_5_VLForConditionalGeneration, Qwen2_5_VLProcessor
+from qwen_vl_utils import process_vision_info
+from trl import SFTTrainer, SFTConfig
+
+
+logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')
+
+
+def train(config):
+    dataset = load_dataset("json", data_files=config["dataset"]["labeled_path"])
+    dataset = dataset.shuffle(seed=config["dataset"]["seed"])["train"]
+    student_model = Qwen2_5_VLForConditionalGeneration.from_pretrained(
+        config["models"]["student"],
+        trust_remote_code=True
+    )
+    processor = Qwen2_5_VLProcessor.from_pretrained(config["models"]["student"])
+
+    def collate_fn(examples):
+        texts = []
+        images = []
+        for example in examples:
+            chat = [
+                {
+                    "role": "user", 
+                    "content": [
+                        {
+                            "type": "image","image": example["image"]
+                        }, 
+                        {
+                            "type": "text","text": example["instruction"]
+                        }
+                    ]
+                },
+                {
+                    "role": "assistant", 
+                    "content": example["output"]
+                }
+            ]
+            text = processor.apply_chat_template(chat, tokenize=False)
+            texts.append(text)
+            image, _ = process_vision_info(chat)
+            images.append(image)
+
+        batch = processor(text=texts, images=images, return_tensors="pt", padding=True)
+        labels = batch["input_ids"].clone()
+        labels[labels == processor.tokenizer.pad_token_id] = -100
+        
+        if isinstance(processor, Qwen2_5_VLProcessor):
+            image_tokens = [151652, 151653, 151655]
+        else:
+            image_tokens = [processor.tokenizer.convert_tokens_to_ids(processor.image_token)]
+            
+        for image_token_id in image_tokens:
+            labels[labels == image_token_id] = -100
+        batch["labels"] = labels
+        return batch
+    
+    training_arguments = SFTConfig(**config["training"])
+    training_arguments.gradient_checkpointing_kwargs = dict(use_reentrant=False)
+    training_arguments.remove_unused_columns = False
+    training_arguments.dataset_kwargs = {"skip_prepare_dataset": True}
+    
+    trainer = SFTTrainer(
+        model=student_model,
+        data_collator=collate_fn,
+        processing_class=processor.tokenizer,
+        args=training_arguments,
+        train_dataset=dataset
+    )
+
+    trainer.train()
+    trainer.save_model(config["training"]["output_dir"])
+    processor.tokenizer.save_pretrained(config["training"]["output_dir"])
+
+
+def main():
+    parser = argparse.ArgumentParser()
+    parser.add_argument('--config', type=str, required=True, help='path to the json config file')
+    args = parser.parse_args()
+    config = json.load(open(args.config))
+    train(config)
+
+
+if __name__ == "__main__":
+    main()
\ No newline at end of file